The Effects of Unregulated Insider Trading
Master Thesis Finance
Tilburg School of Economics and Management
Supervisor:
Second Reader:
Author:
ANR:
Dr. R.G.P. Frehen
Dr. J.C. Rodriguez
H.G.C. Loeffen
s509632
Date of Final Version:
Date of Defense:
30-09-2014
07-10-2014
1
Abstract
The discussion on insider trading regulation is one that is at the heart of the contemporary financial
debate. The Securities and Exchange Commission regulates insider trading based on two arguments.
First, it is proposed that unregulated insiders would consistently capture high returns. Second,
proponents argue that insiders reduce the liquidity of the market. However, the empirical work on the
effects of unregulated insider trading is limited. To strengthen this contemporary debate, this thesis
examined both claims of the SEC and proponents of insider trading regulation, by studying the trading
of Bank of England directors during the early 18th century, when insider trading was completely
unregulated. Evidence is found that insiders did not significantly reduce the liquidity of BoE stock.
Furthermore, it is concluded that in relative calm periods insiders did not significantly exploit outsiders.
However, when relative volatile period occurs (i.c. the South Sea Bubble) evidence is found that
insiders benefitted themselves at the expense of outsiders. As a result, the findings of this study
provide evidence that advocate the favorability of insider trading regulation.
2
Summary
Abstract ................................................................................................................................................... 2
Chapter 1 Introduction ........................................................................................................................... 4
Chapter 2 Historical Background ............................................................................................................ 8
The Bank of England and its stock...................................................................................................... 8
The 1720 South Sea Bubble ................................................................................................................ 9
The Functioning of the 18th Century London Exchange ................................................................... 10
Chapter 3 Literature Review ................................................................................................................ 12
Kyle’s (1985) Continuous Action Model of Insider Trading ............................................................. 12
Profits ................................................................................................................................................ 13
Liquidity............................................................................................................................................. 16
Theoretical Framework .................................................................................................................... 21
Chapter 4 Description of Data and Measurement .............................................................................. 24
Description of Data ........................................................................................................................... 24
Description of Measurement ........................................................................................................... 26
Chapter 5 Results .................................................................................................................................. 32
Profits ................................................................................................................................................ 32
Liquidity............................................................................................................................................. 40
Chapter 6 Discussion and Conclusion .................................................................................................. 42
Discussion ......................................................................................................................................... 42
Conclusion ......................................................................................................................................... 43
Limitations and Recommendations for Future Research ................................................................ 44
References ............................................................................................................................................ 45
Appendix A, Additional Summary Statistics ........................................................................................ 47
Appendix B, 5 and 20 Day Investment Horizons ................................................................................. 49
3
Chapter 1 Introduction
Whether insider trading is harmful or favorable for the financial markets is a discussion at the heart of
contemporary debate. Although insider trading has been regulated as early as 1934 by the U.S.
Securities and Exchange Commission (SEC), diverging views exist in both academia and practice on the
usefulness of this enactment. The SEC initiated the insider trading law with the goal to create an equal,
transparent and fair exchange for all market participants. The reasoning that provided the main
fundament for the regulation is twofold. First, the SEC and proponents of insider regulation, claim that
informed insiders have a continuous information advantage compared to other market participants.
In their view, insiders consistently exploit this beneficial position benefiting themselves with abnormal
returns at the expense of uninformed outsiders, destroying the “fairness and integrity” of markets.
Second, proponents of insider trading regulation, argue that insider trading puts uninformed outsiders
in a structurally disadvantaged position, which reduces their willingness to trade and subsequently
reduces market liquidity. As a result, according to the proponents of insider trading regulation, the
practice of insider trading is harmful to uninformed investors and the functioning of the market and
therefore they propose regulation.
In contrast, opponents of insider trading regulation, with among them Nobel laureate in economics
Milton Friedman, advocate the favorability of insider trading and argue that one wants more insider
trading not less1. According to them, insiders have via insider trading an incentive to make the public
aware of the company’s deficiencies and strengths. Consequently, they argue that insider trading is
favorable for the efficient allocation of capital, since it is proposed to increase the informativeness of
a securities price. On top of that, Manne (1966; 2005) and Carlton and Fischel (1986) argue that insiders
are via insider trading more inclined to produce additional valuable information for the firm. As a
result, opponents of insider regulation argue that insider trading functions as an effective
compensation tool to the benefit of the corporation and its shareholders. Both arguments provide the
main fundament for the opponent of insider trading regulations view that insider trading is favorable
to society and therefore they oppose regulation.
Besides the disagreement among academics on whether insider trading should be regulated, scholars
provide arguments that challenges the effectiveness of the current insider regulation. For instance,
Carlton & Fischel (1983) argue that since regulation prohibits insiders to trade on non-public
information, incentives are created for part of the outsiders to specialize on gathering better
1
Friedman argued this in 2003, in an interview called “Trust markets to weed out corporate wrongdoers”. The
transcript is unavailable, therefore the argument is taken out of the paper of Hotson et al., (2008)
4
information faster than the rest of the market. As a result, these what Carlton and Fischel name
“information specialist” (e.g. high frequency traders, hedge funds, stock analysis) will still be ahead of
the rest of the market once new information becomes public and reap the profits from their
information advantage. The initial and subsequent insider trading regulations will therefore, according
to the opponents of insider regulation, result in new groups of market participants profiting from
information advantages and keeps the initial informed-uninformed trader problem intact. As a result,
the discussion on whether insider trading should be regulated or not and how effective the current
regulation is, still has reached no consensus.
Among the reasons that the discussion is still ongoing, two stand out. First, is that both the opponents
and proponents of insider trading regulation draw hypothetical cases of the effects of unregulated
insider trading on other market participants and the functioning of the market. However, the empirical
work supporting the cases mainly uses data from the post-1934 period in which insider trading is
regulated. During this period insiders have to disclose their trades and trading on undisclosed
nonpublic information is illegal. As a result, evidence shows that insiders strategically optimize their
trading quantity in order to not get attention from the regulators (Kyle, 1985; Glosten & Milgrom,
1985). Consequently, the post-1934 insider trading laws create noise in the measurement of the direct
impact of insider trades. Second, proponents of insider trading regulation argue that insider trading is
unfair, since the profiting of insiders is at the expense of outsiders. However, the current empirical
work studying the investment proceeds of insider trading limits its analysis to the profits insiders make,
but so far has not provided an analysis of the losses outsiders incur as the result of unregulated insider
trading. Therefore, if one wants to provide empirical evidence on the effects of unregulated insider
trading, one should study insider trading directly in an unregulated environment including the losses
outsiders incur as the result of the trading of insiders.
This thesis will resolve these two issues by studying unique, hand collected, and up until today
unanalyzed data from the Bank of England (BoE) stock ledgers. First, the holdings and 787 transactions
of 50 unique BoE directors have been gathered to examine the trading of insiders in the BoE.
Advantage is that insider trading in the 18th century was completely unregulated and according to
historian’s common practice (Bell, 2012), which provides a futile ground to examine the effects of
unregulated insider trading on other market participants and the functioning of the market.
Afterwards, the BoE stock ledgers are used to collect all the holdings and 11,977 transactions of 487
unique outsiders that have traded with a BoE director during June 1715 to September 1720. This allows
to not only provide evidence on the profits BoE directors made, but also on the losses outsiders
incurred as the result of unregulated insider trading. Another advantage of this study is the unique
price data it uses provided by Neal (1990). It has fewer gaps, and more importantly, has a daily
5
frequency. Most studies examining stock that traded during the early 18th century London exchange
have to build on price data reported in The Course of Exchange from Castaing. Disadvantage of this
data, is that it reports prices once every three days. As a consequence, there is considerable volatility
between reported prices compared to prices that are reported every trading day. On top of that,
Interesting angle of the research period of this thesis is that it covers world first financial bubble,
namely the 1720 South Sea Bubble. This event inhabits significant information asymmetries and price
fluctuations that can be exploited by BoE directors. As a result, this research is able to examine the
trading behavior of identical insiders in an extremely volatile period relative to a less volatile period.
The academic contribution of this thesis is in providing empirical evidence on the effects of unregulated
insider trading on profit and liquidity, which both provide the fundament of the proponents of insider
regulations claim that insider trading should be regulated. To study the effect of unregulated BoE
director trading on liquidity, Amihud’s (2002) ILLIQ measure was adopted. In contrast with what the
SEC would expect, the analyses provide evidence that BoE director trading does not significantly impact
the illiquidity of BoE stock. Therefore, the trading of BoE insiders did not increase the price impact of
the order flow of the BoE. On top of that, to examine the impact of unregulated insider trading on
profits, predictive power regressions derived from the work of Temin and Voth (2004) and Frehen et
al., (2013) were adopted. The regressions examining the trades of insiders provide mixed findings in
line with both the proponents and opponents of regulation. In a less volatile period (i.c. June 1715 to
September 1725 excluding the 1720 South Sea Bubble), evidence is found that insiders did not exploit
outsiders. However, in an extreme volatile period (i.c. the 1720 South Sea Bubble), the evidence shows
that insiders excessively benefitted themselves at the cost of outsiders. The regressions examining the
trades of outsiders present by and large the same results. The findings show that outsiders incur no
extra cost when trading with insiders in the less volatile period. Nonetheless, the findings indicate that
during the 1720 South Sea Bubble outsiders incurred significant extra losses when trading with
insiders.
Based on this evidence, it is concluded that insider trading should not be regulated out of the reason
that it reduces liquidity. On top of that, in a relatively less volatile period, when the information
between insiders and outsiders is relatively symmetric, insiders refrain from behaving opportunistically
and damaging outsiders. In this respect, insiders should not have to be regulated in this period.
However, when the level of information between insiders and outsiders becomes more asymmetric,
the benefits from exploiting – behaving opportunistically – at the cost of outsiders increases. In this
period, outsiders require a protection from trading with better informed insiders, since then insiders
will reap the profits at the expense of the outsiders. To conclude, given that one cannot predict future
events, this thesis shows that insiders should be restrained from the potential to engage in
6
opportunistic behavior to the benefit of other market participants. For that reason, it is concluded that
the trading of informed insiders should be regulated.
This thesis is structured as follows. Chapter 2 provides a brief historical background and chapter 3
provides a theoretical background by discussing the relevant literature. Subsequently, chapter 4
describes the data and measurement used to examine the hypotheses. Furthermore, this chapter
provides summary statistics regarding the data that is researched. Chapter 5 will present the results of
the analysis. Finally, chapter 6 will discuss the result in relation to the existing body of research and
will draw a conclusion.
7
Chapter 2 Historical Background
The aim of this chapter is to provide a brief historical background on the Bank of England, the 18th
century London Exchange and the South Sea Bubble. Furthermore, the work of historians is reviewed
to discuss the similarities and dissimilarities between the market and companies today and the market
and companies during the 18th century.
The Bank of England and its stock
The English government was at the end of the 17th century in a large need for capital to finance its war
activities with France. To get access to new capital, the government started using innovative financial
constructions to be able to borrow from corporate entities. This resulted in the origination of The Bank
of England in 1694, by signing a charter between the English government and a group of private
entrepreneurs aiming to establish a profitable company. The initial charter entailed a loan of
₤1,200,000 to the government at 8% interest. In return, the government would give economic
privileges to the BoE and use its authority to restrict competition in the banking industry. The activities
of the BoE are by and large the same as the operations of a commercial bank today (Broz & Grossman,
2004). For instance, the bank was able to deal in bills, make loans on notes, lend mortgages, and could
take deposits on different terms. Carlos & Neal (2006) state that to become a director at the BoE one
had to own a minimum amount of stock. A director role would require a minimum of ₤2,000 in nominal
value, to become deputy-governor ₤3,000 in nominal value, and to be able to become elected to
governor one would require a minimal amount of ₤4,000 in BoE shares. Initially the BoE’s stock was
held closely by its founders, but over time a secondary market emerged for BoE shares (Carlos,
Maguire, & Neal, 2006). On top of that, Carlos & Neal (2006) show that BoE stockholders came from
all over the social spectrum, and from all over England and Europe. Furthermore, Carlos & Neal (2006)
provide evidence that the BoE stock was known as the least volatile stock available on the London
exchange. Figure 1 presents the innovations in BoE share price covering the period June 1725 to
September 1725, which is this thesis period of analysis. What can be inferred from this figure, is that
except for 1720 South Sea Bubble the share price is relatively flat.
8
BoE Share Price June 1715 - September 1725
300
250
200
150
100
50
1715
1715
1715
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1716
1716
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1717
1717
1717
1717
1718
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1725
0
Figure 1: Bank of England share price from June 1715 to September 1725
The 1720 South Sea Bubble
One interesting feature of this study, is that it allows to examine insider trading in world’s first financial
bubble, known as The South Sea Bubble. After the treaty of Utrecht in 1713, ending the war of the
Spanish succession, the English government became concerned about the growing portions of its
outstanding debt. As a result, both the Bank of England and the South Sea Company entered in the
beginning of 1720 into competition for a deal with the government, to exchange the company’s equity
in return for government debt. To be able to win this competition for the debt-for-equity swap, the
incentives for the directors were such to rapidly increase the market value of its outstanding shares,
since this would allow more debt to be exchanged for the same amount of equity (Carlos & Neal, 2006).
Figure 2 plots the price graph of the Bank of England during the South Sea Bubble. Interestingly, the
South Sea Bubble brings a volatile period in the BoE stock price, with prices rising from 150 in the
beginning of May 1720 up to 250 by mid-June in the same year. As a result, BoE directors had during
this bubble significant opportunities to exploit their privileged information position at the expense of
outsiders.
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BoE Share Price 1720 South Sea Bubble
300
250
200
150
100
50
3
2
1
12
11
10
9
8
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4
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Figure 2: Bank of England Share Price from April 1720 to March 1720.
The Functioning of the 18th Century London Exchange
Cope (1978) argues that the early 18th century stock market functioned with great similarities as the
stock market today. According to him, trading took place in coffee houses or in the BoE transfer offices
where a broker receives a sell or a buy order from the public. Subsequently, the broker ask a market
maker (or dealer) to quote a bid and ask price. Important is that the broker does not disclose who his
client is, nor whether the client’s order is a buy or a sell order. The broker repeats this process of
receiving several prices quoted by market makers, up until he receives the best price. He then executes
the transaction in the name of his client and charges a brokerage commission. As a result, BoE directors
could trade without exposing their identity and market makers could not distinguish their order flow
between orders from insiders or outsiders.
On top of that, with the allowance of free printing, several printed sources emerged to keep investors
informed. Newspapers reported on a systematic bases share prices and developments in the London
capital market. Even a publication specialized on the London stock market emerged with John
Castaing’s Course of Exchange (Cope, 1978). This twice-weekly letter contained prices over the past
three days and dates and amounts of dividend payments of the major London stocks. Furthermore,
Carlos & Neal (2006) argue that an investor willing to trade, could quickly gather information from the
printed sources available and the well-known trading places. To sum up, the technological state by the
beginning of the 18th century did not allow information to travel faster than the fastest horse. However,
there is evidence that shows that the London stock market can be considered as quite efficient (Cope,
1978; Temin & Voth, 2004; Carlos & Neal, 2006)
To conclude, according to historians the BoE and the early 18th century stock market, was highly
sophisticated and functioned with great similarities as the stock market and companies today. The BoE
10
had operational activities comparable to activities carried out by commercial banks in the present.
Furthermore, the process of executing transactions in the 18th century market as explained by Cope,
(1978), was sophisticated and provided a comparable cover insiders have in the present market. On
top of that, the availability of printed sources ensured that investors were exposed to the information
needed, which in turn ensured a sufficient level of market efficiency.
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Chapter 3 Literature Review
The theoretical fundament of this study is provided with the continuous auction model of insider
trading developed by Kyle (1985). This literature review will therefore start with a brief discussion of
Kyle’s model (Kyle, 1985). It will explain the different traders that are active in the market and their
underlying assumptions with respect to the way they operate in the market and the profits they make.
Furthermore, the process by which trading takes place in the model is discussed. Once the model is
reviewed, the review moves towards the most important arguments made by both opponents and
proponents of insider trading regulation. At the end of the different paragraphs, hypotheses will be
drawn of what is expected given our data, based on the current body of research. Given the
disagreement of both the opponent and proponent streams, it is chosen to separate the arguments
and supporting literature of both streams in each individual discussion area. As a result, both a
proponent and opponent hypothesis is drawn in the profit and liquidity discussion.
Kyle’s (1985) Continuous Action Model of Insider Trading
Kyle (1985) models the trading strategy of an insider in a dynamic model of efficient price formation.
The model assumes that all investors participating in the stock market can be divided in three different
groups of traders (i) an informed insider who has unique private information about the liquidation
value of a particular asset; (ii) market makers who hold inventories and facilitate trading over time.
The market makers determine the price at which the asset trades dependent on the quantities traded
by both insider and outsiders; and (iii) uninformed outsiders (noise traders) whose trading quantity is
assumed to follow a random walk.
Trading takes place in two sequential steps. First, insiders and outsider simultaneously place market
orders entailing the quantities they want to trade. The insider bases the quantity of his market order
dependent the private information of the liquidation value of the asset in combination with past prices
and quantities traded by himself. Furthermore, the insider has no access to information about future
prices nor of quantities traded by outsiders. The quantity of the marker order placed by the outsiders
is determined by nature, and consequently distributed independently from present or past quantities
traded by insiders and outsiders. In the second and final step of trading, the market maker observes
the order flow of the insiders and outsiders combined, but importantly not the order flow of each
group separately. The market maker is thus unable to distinguish between the quantities traded by
insiders and outsiders individually. Based on the order flow, the market makers determines a price
which makes clears the market. As a result, price changes are solely dependent on order flow
innovations.
12
With respect to the traders, the informed insider is assumed to be risk neutral and maximizes returns.
He acts as a monopolist in exploiting his private information, earning him profits at the expense of the
outsiders. The prices determined by market makers equal the expectation of the liquidation value of
the asset. These traders are assumed to set the bid and ask prices such, that they make on average
zero profits with their trading. On top of that, since market makers are unable to observe the individual
quantities traded by insiders or outsiders, outsiders provide the camouflage to cover the trading of
insiders. As a result, an increase in quantity traded by outsiders, induces insiders to increase the
quantities they trade with the same amount. Increasing quantities traded by outsiders therefore have
no effect on the price but only increase the profits made by the insider.
Profits
According to proponents of regulation of insider trading, informed insiders reap abnormal profits at
the expense of uninformed outsiders. As assumed in Kyle’s model, insiders have ex-ante unique private
information about the liquidation value of an asset (Kyle, 1985). This creates a continuous information
advantage for insiders on the on hand, and a continuous information disadvantage for market makers
and outsiders on the other hand. Proponents of insider regulation argue that insiders systematically
exploit this beneficial position, profiting themselves while hurting the rest of the market. As a result,
proponents argue that the information asymmetry insiders have, creates a structural advantage for
informed insiders to exploit uninformed outsiders, which according to them is unfair and therefore
should be regulated.
On top of that, proponents of regulation claim that if insiders are unregulated in their trading and at
the same time in control of corporate actions of their firm, incentives are created for insiders to engage
in immoral trading strategies. For instance, Bhattacharya (2013) states that unregulated insider trading
increases incentives for insiders to short their own company stock and subsequently produce
destructive information. On top of that, he argues that destroying value is on average easier then
creating value. Consequently, the short and producing destructive information strategy would be a
more efficient and effective strategy for an insider as opposed to go long and produce valuable
information. As a result, proponents of regulation state that if the insider choses to follow the first
strategy, it does not only harm its company and fellow shareholders but also executes this in an
immoral way.
On top of that, proponents argue that unregulated insider trading gives insiders an incentive to
manipulate stock prices (Schotland, 1967). The reasoning of this argument is as follows. Insiders are at
the same time in control of corporate actions and in control of their own stock holdings. Unregulated
insider trading gives them an extra incentive to push (manipulate) the stock price via certain corporate
13
actions in a for them favorable direction. Note that the favorable direction for insiders not necessarily
is favorable for other shareholders. For instance, a corporate action as an increase or decrease in
dividend payments might be beneficial for the short run portfolio performance of the insider but could
be harmful for the long run company value. As a result, outsiders might pay the bill of insiders
benefiting themselves.
If the above claims hold, then it is expected to see that insiders would reap abnormal returns. Several
studies have intended to measure the (abnormal) profits insiders make. Tavakoli, McMillan, &
McKnight (2012) show that transactions of directors predict future stock returns and that buy
transactions predict higher returns than sell transactions. Furthermore, Cheng & Lo (2006) provide
evidence that the number of bad news forecasts is increased before insiders buy. Via this action,
insiders depress stock prices and in turn buy company shares, with having a high probability that the
news is not as bad as forecasted resulting in stock price increases, indicating that insiders might use
(abuse) corporate actions to benefit their trading performance.
In addition, several studies have attempted to measure insiders profits when insiders are unregulated
in their trading. For instance, Leland (1992) constructs an econometric model that allows to study the
consequences of unregulated insider trading. He demonstrates that when insider trading is
unregulated, insiders and owners of investment projects would capture the benefits of trading at the
expense of outsiders and liquidity traders. Furthermore, Koudijs (2013) who studies the Bank of
England stock that traded in the 18th century Amsterdam exchange, concludes that insiders capture
high information rents compared to other market participants2. This evidence shows that when
insiders have the ability to exploit their information asymmetry, they capitalize on this opportunity
and significantly harm outsiders.
To conclude, insiders have a structurally advantaged information position relative to other market
participants. Proponents of insider trading regulation argue that insiders exploit this continuous
information advantage, to benefit themselves at the expense of the rest of the market. They argue
that this information advantage is unfair and increases the incentives for insiders to engage in immoral
trading strategies. As a result, proponents argue that regulation reduces the private information that
can be legally traded on, which reduces the profitability of insider trading, and in turn decreases the
incentives for insiders to engage in immoral behavior. Based on the arguments of proponents of
2
Note that this study measures insider trading in BoE stock indirectly. BoE stock was primarily traded in
London, were this study measures the direct activity of its directors. However, Koudijs (2013) measures insider
trading using indirect private signals arriving via shipping boats on the Amsterdam stock exchange.
14
regulation, it is expected to see that when insider trading is unregulated, then insiders significantly
capture higher returns compared to other market participants. This allows to hypothesize that:
Hypothesis 1A: Insiders that are unregulated in their trading earn higher returns with respect to other
market participants
In contrast, opposing theoretical arguments in the profit discussion are presented out of Carlton &
Fischel (1983) work, who challenge the likelihood that when insider trading is unregulated, insiders
systematically exploit their information advantage and engage in the immoral behavior proponents of
regulation claim them to do. The first argument states that insiders often work in teams, and
consequently have to persuade other team members to engage in certain corporate actions. If these
corporate actions are detrimental to the value of the firm – following the short and producing
destructive information trading strategy – insiders thus first have to convince other team members of
this action in order to execute it. As a consequence, Carlton & Fischel (1983) argue that the possibilities
for insiders working in a team to engage in immoral behavior and destroy company value, will be
constrained by the power of other team members.
The second argument made by Carlton & Fischel (1983), relates to the insiders concern about the value
of the insider’s human capital and for the reputation of the insider. The logic fundamental to Carlton
& Fischel’s (1983) claim is as follows. In order to stay valuable in the labor market, the goal of an insider
is to maximize the value of his human capital in the long run. In order to capitalize on this goal the
insiders wants to maximize his performance, since the performance will by and large determine the
market value of the human capital. Acting in ways detrimental to the performance of the firm, will thus
interfere with the long run human capital concerns of the insider, limiting the chance to engage in
immoral behavior. Moreover, once the market realizes the insider does engage in behavior the market
perceives as immoral, then the reputation of this insider will be severely harmed. In turn, the market
value of the human capital of this insider will significantly decrease limiting the possibilities to deliver
valuable future commercial dealings. On top of that, the human capital and reputation concerns
amplify the role teams have in limiting perverse behavior. Other team members will also aim to
maximize their human capital and reputation value. This will further limit the possibilities that they will
agree with value destructing corporate actions. As a result, opponents of insider regulation agree that
unregulated insider trading gives insiders the ability to exploit the information asymmetries. However,
Carlton & Fischel (1983) argue that human capital and reputation concerns prevent them from
engaging in perverse behavior as proposed by proponents of insider trading regulation.
Several studies provide evidence that support the opponent of regulation’s claim that insiders do not
significantly exploit the benefits of their information advantage. For instance, Heinkel & Kraus (1987)
15
study the Vancouver Stock Exchange, which is characterized by a large number of natural resource
companies inhibiting significant information asymmetries that can be exploited by insiders at the
expense of outsiders. They provide evidence that insiders do not capture any abnormal profits at the
expense of outsiders. On top of that, Elliott, Morse, & Richardson (1984) study insider’s profits around
various company announcements including annual earnings, dividend changes, bond rating changes,
mergers and bankruptcies. They find that insiders do not make abnormal returns around these
announcements compared to periods in which no announcements happen. Interestingly, the insiders
in these studies have significant opportunities to exploit the information asymmetries. However, they
are refrained from realizing the potential. Furthermore, Lin & Howe (1990) study the OTC market which
is less regulated with respect to insider trading than regular stock exchanges. Important finding of this
study is that insiders participating on this exchange do not outperform outsiders in terms of profits. As
a result, the evidence contradicts the proponent’s expectation that with less regulation – more private
information that can be legally traded on – insiders significantly hurt outsiders in terms of profits.
The empirical evidence examining the direct relationship between insider trading and human capital
and reputation concerns of insiders is nil. As a consequence, the arguments made by Carlton & Fischel
(1983) remain largely hypothetical in the sense they only have a theoretical ground. Nevertheless,
based on the arguments made by opponents of regulation and the supporting empirical work, shows
that when insiders have the ability to profit from their information advantage they do not significantly
exploit this at the expense of outsiders as proposed by proponents of regulation. Therefore, based on
the arguments of opponents of regulation, it is hypothesized that:
Hypothesis 1B: Insiders that are unregulated in their trading do not earn higher returns with respect to
other market participants
Liquidity
Central in the insider trading liquidity discussion is the willingness of outsiders to trade with insiders.
According to proponents of regulation the practice of insider trading puts outsiders in a structurally
disadvantaged position. As a result, outsiders are unwilling to trade (with insiders), which decreases
market liquidity. According to opponents of regulation, the practice of insider trading creates no
structural disadvantage for outsiders and as a result outsiders are willing to trade with insiders.
Therefore, opponents of regulation argue that insider trading should have no effect on market
liquidity. To explain the reasoning behind both opposing views Kyle’s (1985) model will be taken as
starting point in the discussion.
16
Kyle (1985) assumes that insiders have ex-ante unique private information about the liquidation value
of a stock during all steps of trading. This provides them with a continuous information advantage,
with the informed insider possessing most information, compared to all other market participants (e.g.
market makers and outsiders). According to proponents of regulation, insiders exploit this information
differential to benefit themselves at the expense of the rest of the market. Consequently, it is proposed
that an outsider is unwilling to trade, since he knows the informed insider will systematically reap part
of his profits. Subsequently, if outsiders are reluctant to trade then market liquidity is reduced. As a
result, proponents of regulation argue that insider trading is harmful to the functioning of the market,
since it is proposed to reduce market liquidity.
On top of that, Glosten & Milgrom (1985) extend Kyle’s (1985) model and focus on the role market
makers have in the functioning of the market. Important is to recall that market makers only observe
the total order flow, so the order flow of insiders and outsiders combined, instead of each individual
order flow. As a consequence, Glosten & Milgrom (1985) argue that a market maker faces an adverse
selection problem, since the anonymous insider might trade based on unique private information that
the market maker does not possess. Assuming that insiders exploit this information advantage at the
expense of other market participants, than the market maker systematically loses money when trading
with insiders. In order to rebalance for these losses, the market maker expands the bid-ask spread to
make on average zero profits.3 However, the result of this increased spread is that outsiders, who trade
at the same spread as insiders since the market maker cannot distinguish between individual order
flows, pay a liquidity premium as the result of insiders. As a result, proponents of regulation argue that
informed insider trading forces market makers to expend the bid-ask spread, which subsequently
carries the costs that market markers incur of insider trading, to outsiders trading at the same bid-ask
spread.
Several studies provide evidence supporting the argument made by the proponents of insider trading
regulation. For instance, Leland (1992) econometrically demonstrates that when insider trading is
permitted, then the liquidity of the market is reduced. On top of that, Cheng et al. (2006) study the
relationship between director dealings and market liquidity. They provide evidence that on days
directors are active in the market, the bid-ask spread widens and market depth (the size of the order
flow innovation needed to change price for a given trading quantity) decreases. These liquidity costs
(the costs of an increased spread and reduced market depth) are borne by all market participants. As
a consequence, according to proponents of insider regulation the practice of informed insider trading
creates structural disadvantages for outsiders reducing their propensity to trade and subsequent
3
Kyle (1985) assumes that market makers make on average zero profits. However, in the real world market
makers do on average make profits, which results in a further widening of the bid-ask spread.
17
market liquidity. For that reason, they are of the view that informed insider trading should be
regulated. To conclude, based on the proponent of insider trading regulation arguments it is expected
to see, that if insider trading is unregulated then the market liquidity is reduced when insiders are
active in the market. This allows to hypothesize that:
Hypothesis 2A: Unregulated insider trading activity has a negative effect on market liquidity
The first argument opponents of regulation make in the liquidity discussion originates out of the profit
discussion. Opponents of regulation of insider trading acknowledge that insiders have a privileged
information position and that this advantage gives them the opportunity to exploit outsiders in a for
insiders profitable manner. However, opponents provide evidence that counters the proponent view
that insiders will significantly capitalize on this opportunity (Manne, 1966; Manne, 2005; Carlton &
Fischel, 1983; Bainbridge, 1986; Bainbridge, 2000). As a result, insiders do not put outsiders in a
disadvantaged position, and for that reason outsiders are willing to trade keeping the liquidity of the
market at equal levels.
Another argument is provided by Bainbridge (1986; 2000). He is of the view that selling to or buying
from an insider cannot be perceived as being in a disadvantaged position. He explains his statement in
the follow manner. Insider trading conveys information if the trading direction of the insider is known.
However, on an anonymous exchange it is impossible to observe the counterparty and consequently
no one is able to convey information out of the insider’s trades. In other words, in an anonymous
trading environment, outsiders determine the decision to buy, sell or hold irrespective of who the
counterparty is since they are unable to observe information about their counterparty. As a
consequence, Bainbridge (1986, 2000) states that the probability and the ability to convey information
out insider trading is equal for all respective outsiders and therefore not putting outsiders in a
structural disadvantaged position.
On top of that, opponents of insider trading regulation reason that insider trading improves the
informativeness of a stock price and as a result increases the liquidity of the market. Insiders are
assumed to have continuously access to ex-ante unique private information about the liquidation value
of a particular asset (Kyle, 1985). If an insider notices that as the result of new information this
liquidation value is not in line with the current stock price, than the insider will trade in a direction
pushing the stock price closer to its liquidation value. For instance, an insider becomes aware of private
information that makes the liquidation value of the asset higher (lower) than its current stock price,
then his trading action will be a buy (sell) bringing the price of the asset closer to the value if all
information was incorporated in the price. However, insider trading regulation prohibits insiders from
trading based on private information. The regulation forces that this private information first has to
18
become publicly available before any investor is allowed to trade on it. As a result, opponents of
regulation argue that insider regulation slows down the process by which new information is expressed
in the stock price. Consequently, they argue that the advantage of unregulated insider trading is an
accelerated process by which new private information affecting the liquidation value of an asset is
incorporated in the stock price. Furthermore, several econometricians have extended Kyle’s (1985)
model and demonstrate that insider trading has a positive effect on the informativeness of a securities
price and that in turn the increased informativeness increases the liquidity of the asset. The intuition
behind this evidence is that insiders that are competing (Holden & Subrahmanyam, 1994; Baruch,
2002) or are unregulated (Buffa , 2010) trade in a more aggressive way to protect themselves against
future price risk caused by outsiders. As a result, the private information of the insider is incorporated
in the price during early stages of trading and thereby strongly reducing the adverse selection problem
faced by the market maker during subsequent steps of trading. As a result, this evidence shows that
insider trading increases the informativeness of the stock price which reduces the adverse selection
problem faced by the market maker resulting in increased market liquidity.
Interesting empirical evidence in this respect is provided by Cornell & Sirri (1992). They study the court
records of the insider trading of Anheuser-Busch during the Campbell Tagart take over. Important
feature of their data is that it allows to precisely distinguish between insider trades and outsider trades.
These authors provide evidence that, in contrast of the adverse selection argument proposed by
Glosten & Milgrom (1985) increased insider trading as a percentage of the total volume traded, did
not result in a widening of the bid-ask spread set by the market makers. On top of that, the evidence
of this study shows that insider trading actually improved the liquidity in the market. Outsiders thus
increased their trading quantities at times more insider were active in the market, contradicting what
one would expect if outsiders would perceive the trading of insiders as being in a disadvantaged
position. As a result, even if insiders are making excessive profits at the expense of outsiders – what
happened in the Campbell Tagart case – then still evidence shows that market liquidity is not always
decreased.
To conclude, opponents of regulation argue that insider trading does not provides structural
disadvantages for outsiders. First, because insiders do not significantly exploit outsiders in terms of
profits. Second, because when engaging on an anonymous exchange the chance of drawing an insider
or an outsider is random and no outsider can convey information out of the trading of insiders. Third,
since insider trading positively affects the information efficiency and subsequently reduces the adverse
selection problem faced by market makers. As a result, opponents of regulation argue that outsiders
have no disadvantaged position and are therefore are willing to trade with insiders, which does not
19
affect the liquidity of the market. Therefore, based on the arguments of opponents of regulation it is
hypothesized that:
Hypothesis 2B: Unregulated insider trading activity has no effect on market liquidity
20
Theoretical Framework
The reasoning of both the opponents and proponents of regulation are presented in a theoretical framework.
Outsider’s Profits
+
Regulation
Insider Trading
Insider’s Profits
Liquidity
Figure 3 Theoretical Framework of Proponents of Insider Trading Regulation.
The signs -, + and 0 indicate a negative, positive or zero relationship between both constructs respectively
Proponents argue that insider trading creates a structural advantage for insiders. As a result, proponents argue that insiders exploit their information
advantage significantly increasing their profits, while significantly decreasing the profits of outsiders (hypothesis 1A). Furthermore, proponents of regulation
argue that insider trading puts outsiders in a structurally disadvantaged position, therefore proponents argue that outsiders (noise traders and market makers)
are unwilling to trade. As a result, proponents of insider trading regulation are of the view that insider trading has a negative effect on the liquidity in the
21
market (hypothesis 2A). To prevent this from happening, proponents of regulation argue that insiders should be regulated in their trading, and as a result they
propose regulation.
o
Outsider’s Profits
Regulation
o
Insider Trading
Insider’s Profits
o
+
Information
Efficiency
+
Liquidity
Figure 4 Theoretical Framework Opponents of Insider Trading Regulation.
The signs -, + and 0 indicate a negative, positive or zero relationship between both constructs respectively.
On the other hand, opponents of regulation bring in arguments that challenge the likelihood that insiders significantly exploit their information advantage. As
a result, they are of the view that insider trading has no significant influence on the profits of insiders and on the profits of outsiders (hypothesis 1B).
22
Furthermore, opponents of insider trading regulation argue that outsiders do not have a structural disadvantage and should be willing to trade with insiders
and therefore market liquidity is unaffected by insider trading (hypothesis 2B). On top of that, opponents argue that the trading of insiders improves the
information efficiency in the market, which in turn has a beneficial effect on market liquidity. As a result, opponents argue that regulation is unnecessary given
the proponent claims. Moreover, they state that regulation is harmful to the information efficiency of the market, and consequently they oppose regulation.
23
Chapter 4 Description of Data and Measurement
The goal of this chapter is to describe the data used in this thesis. It will explain why particularly this
data is chosen and why this data is a valuable contribution in comparison with the current literature.
Furthermore, the sources of the data will be described and the process of preparing the data to levels
that allow it to be analyzed is explained. Finally, summary statistics will be presented to provide a
broad picture of the data and to give a background for the subsequent analysis of the following
chapter.
Description of Data
In order to analyze insider trading in the BoE, it first should be known which traders were part of the
court of directors of the BoE in what year. To determine this, pictures the BoE director meeting minutes
are analyzed, which have been retrieved from the Bank of England archives. The court on average
consisted of 24 BoE directors, one deputy-governor and one governor between June 1715 and
September 1725. As a result, in total there were on average 26 insider’s part of the court of directors
of the BoE each year. Furthermore, the minutes are examined to determine the dividend payout.
Dividends were paid twice a year during the two weeks closing of the transfer books in March and
September and varied during our period of analysis between 3 to 4 pounds per share. As a result, with
the amount of the dividends and the moment of payout being known, the dividends can be carefully
incorporated in the calculation of investment returns to the benefit of the profit analysis in the
subsequent chapter.
On top of that, pictures of the BoE stock ledgers have been studied, which are provided by the Bank of
England archives. These allow to examine all the stock transfers of every individual participating in BoE
shares from June 1715 to September 1725. The pictures contain the names and beginning and ending
balances of the individual investors, transaction dates and the number of shares transferred with each
buy and sell transaction. First, the holdings and 787 transactions of 50 unique traders that held
positions as BoE directors in June 1715 until September 1725 have been gathered. This allows to
examine the profits unregulated insiders (i.c. BoE directors) made during this 10 year period. However,
to give a better analysis of the effects of unregulated insider trading, one does not only want to
examine the profits insiders make but also the losses outsiders incur as the result of unregulated
insider trading. Therefore, also all the holdings and 11,977 transactions of 487 unique counterparties
that have traded with a BoE director between June 1715 until the closing of the transfer books in
September 1720 have been recorded. Ideally, one would be able to also gather the trades of the
insider’s counterparties during September 1720 until September 1725. However, limitations in time
24
prevented it from doing so. As a result, this study will use the trading of BoE directors in June 1715 to
September 1725 and the trading of their counterparties in June 1715 to September 1720, to examine
how much returns unregulated insiders make and how damaging this practice is from the
counterparty’s point of view.
Furthermore, this study has access to two other valuable data sources. The first source, is provided by
the supervisor of this thesis Dr. Frehen and entails all left pages of the BoE stock ledgers from holdings
and transactions from September 1720 to September 1725. Unfortunately, only the left pages of the
stock ledgers have been gathered, which record all the sell transactions (right pages record the buys)
of the individual traders. However, since a sell is for the counterparty a buy, it is able to calculate the
total transaction volume for each individual trading day during this period, by taking the sum of the
total sell volume transacted on a given day. This data will be used in the liquidity test which will be
explained in the description of measurement area of this chapter.
The second source, entails a price series of daily price quotations derived from Neal (1990). The
advantage of using the data collected by Neal is that it has fewer gaps, and more importantly, has a
daily frequency. Most studies examining stock that traded during the early 18th century London
exchange have to build on price data reported in The Course of Exchange from Castaing. Disadvantage
of this data, is that it reports prices once every three days. As a consequence, there is considerable
volatility between reported prices compared to prices that are reported every trading day. The price
data used in this study therefore will be more precise in measuring what it intends to measure, based
on prices compared to studies using prices reported in The Course of Exchange.
In order to prepare the data, several corrections had to be carried out. First, the account of John
Hanger, John Rudge, Thomas Scawen and Gerard Conyers is removed from the dataset. This single
account of multiple BoE directors reflects stock mortgaged to the BoE by individual shareholders and
consequently does not reflect individual transactions. Furthermore, stock subscriptions do not reflect
a transaction between an insider and an outsider and therefore have been taken out of consideration.
In addition, the price series from Neal contains a few missing prices on trading days. These missing
prices have been interpolated between the previous and subsequent trading day. Finally, during March
and September the transfer books closed for two weeks which allowed to determine the dividend
payouts to the individual shareholders. It is assumed that no trading took place during these closing
periods.
25
Description of Measurement
The first goal of the subsequent results chapter will be to provide evidence for the hypothesis 1A and
1B. Consequently, it should be examined how much profits informed BoE directors made and how
much losses the uninformed outsiders incurred as the result of unregulated insider trading. To
capitalize on this goal, a predictive power regression, which is the most commonly accepted measure
to examine the profits of insiders used in insider studies, is adopted (Wu, 1963; Seyhun, 1988; Seyhun,
1992b; Lakonishok & Lee, 2001; Temin & Voth, 2004; Tavakoli, McMillan, & McKnight, 2012; Frehen,
Goetzmann, & Rouwenhorst, 2013). The logic behind this regression is that traders with ex-ante unique
private information about an asset, have the power to predict future price movements. A predictive
power regression exposes if traders exploit this information advantage by regressing the trading
directions (buys and sells) and trading volumes on future returns. As a result, this regression examines
if traders exploit their information position and subsequently gives insights into the profits they reap
with their trading.
The predictive power regressions adopted in this thesis is originally developed by Temin & Voth (2004).
These researchers aim to expose the trading performance of Hoare’s Bank on the London stock
exchange during the 1720 South Sea Bubble. As a result, the nature of their data and the aim of their
study is highly comparable to the data and aim of this thesis, which increases the applicability of their
regressions. However, Temin & Voth’s (2004) predictive power regressions are developed to examine
the trading decisions of a few individuals as is the case with Hoare’s Bank. The disadvantage is that
these regressions do not consider net share purchases and consequently do not properly incorporate
when Hoare’s Bank traded in opposing directions (conducting both buys and sells) on the same trading
day. The probability of encountering both buys and sell transactions of a group of insiders on a single
trading day is higher compared to when individuals are studied. As a result, these regressions limit the
potential to examine the trading behavior of clienteles as is the intention of this study. Therefore, the
extended version of Temin & Voth’s (2004) predictive power regression developed by Frehen et al.
(2013) is used in this thesis to examine the profitability of trading. These regressions consider net share
purchases which will be further explained in the following paragraph and as a result, more accurately
incorporate when BoE directors traded in opposing directions on the same day.
Out of the work of Frehen et al. (2013), two different kinds of regressions which allow to separately
analyze the buy and sell transactions of BoE directors are adopted. The first regression examines the
predictive power of the BoE director’s buy transactions and takes the following form:
ln⁡(
𝑃𝑡+⁡𝜏+⁡𝐷𝑡+𝜏⁡
𝑃𝑡
) = ⁡𝛼 + ⁡ 𝛽𝐵𝑢𝑦 𝐷𝐵𝑢𝑦,𝑡 + ln⁡(
𝑃𝑡−𝜏⁡+⁡𝐷𝑡−𝜏⁡
𝑃𝑡
) + ⁡ 𝜀𝑡 ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡
(1)
26
Where dependent variable ln⁡(
𝑃𝑡+𝜏⁡+⁡𝐷𝑡+𝜏⁡
𝑃𝑡
)⁡indicates the log return of 𝑡 + 𝜏 days and the independent
variable 𝐷𝐵𝑢𝑦,𝑡 is a dummy variable, which equals one on days where the net share purchase of BoE
directors was positive, and zero if otherwise. To tackle the possibility that the trading of BoE directors
was driven by a momentum strategy, variable ln⁡(
𝑃𝑡−𝜏⁡+⁡𝐷𝑡−𝜏⁡
𝑃𝑡
) is added which controls for the past 𝜏-
day return. Furthermore, 𝜏 which denotes the investment horizon has to be assumed. Temin & Voth
(2004) argue that determinant in this decision is the horizon on which the inside information is
considered to be useful. In other words, how efficiently the market incorporates new private
information. As discussed in the historical background section, the 18th century London stock exchange
is considered as quite efficient (Carlos & Neal, 2006). As a result, it is chosen to follow an investment
horizon in line with the studies of Temin & Voth (2004) and Frehen et al. (2013)4, which set 𝜏 equal to
10, indicating a 10-day horizon. Furthermore, it is chosen to run an OLS regression with Newey-West
consistent standard errors to tackle autocorrelation in the error terms. As a result, 𝛽𝐵𝑢𝑦 can be
interpreted as the mean return of 𝑡 + 10 of days on which BoE directors bought more shares then they
sold, and 𝛼 as the mean return of 𝑡 + 10 of days on which BoE directors have not bought more shares
then they sold. The second regression, examines the predictive power of the sell transactions of BoE
directors. This regression can be written as:
ln⁡(
𝑃𝑡+10⁡+⁡𝐷𝑡+10⁡
𝑃𝑡
) = ⁡𝛼 + ⁡ 𝛽𝑆𝑒𝑙𝑙 𝐷𝑆𝑒𝑙𝑙,𝑡 + ⁡ ln⁡(
Where dependent variable ln⁡(
𝑃𝑡−10⁡+⁡𝐷𝑡−10⁡
𝑃𝑡+10⁡+⁡𝐷𝑡+10⁡
𝑃𝑡
𝑃𝑡
) + ⁡ 𝜀𝑡 ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡
(2)
) indicates the log return of 𝑡 + 10 days and the
independent variable 𝐷𝑆𝑒𝑙𝑙,𝑡 is a dummy variable, which equals one on days where the net share
purchase of BoE directors was negative, and zero if otherwise. As a result, 𝛽𝑆𝑒𝑙𝑙 can be interpreted as
the mean return of 𝑡 + 10 of days on which BoE directors sold more shares then they bought, and 𝛼
as the mean return of 𝑡 + 10 of days on which BoE directors have not sold more shares then they
bought. Finally, the buy and sell transactions of BoE directors are considered in a multivariate
regression. This regression is defined as:
ln⁡(
𝑃𝑡+10⁡+⁡𝐷𝑡+10⁡
𝑃𝑡
) = ⁡𝛼 + ⁡ 𝛽𝐵𝑢𝑦 𝐷𝐵𝑢𝑦,𝑡 + 𝛽𝑆𝑒𝑙𝑙 𝐷𝑆𝑒𝑙𝑙,𝑡 + ln⁡(
𝑃𝑡−10⁡+⁡𝐷𝑡−10⁡
𝑃𝑡
) + ⁡ 𝜀𝑡 ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(3)
As a result, 𝛽𝐵𝑢𝑦 can be interpreted as the mean return of 𝑡 + 10 of days on which the net share
purchase of BoE directors is positive, 𝛽𝑆𝑒𝑙𝑙 can be interpreted as the mean return of 𝑡 + 10 of days on
which the net share purchase of BoE directors is negative, and 𝛼 as the mean return of 𝑡 + 10 of days
on which BoE directors did not trade.
4
Both studies examine shares that traded on the 18th century London stock market
27
Furthermore, it is chosen to separately examine the predictive power of the volume traded by BoE
directors. To analyze this, a regression of Temin & Voth (2006) is adopted which is written as:
ln⁡(
𝑃𝑡+10⁡+⁡𝐷𝑡+10⁡
𝑃𝑡
) = ⁡𝛼 + ⁡ 𝛽𝐵𝑉𝑜𝑙 𝐵𝑉𝑜𝑙,𝑡 + 𝛽𝑆𝑉𝑜𝑙 𝑆𝑉𝑜𝑙,𝑡 + ln⁡(
𝑃𝑡−10⁡+⁡𝐷𝑡−10⁡
𝑃𝑡
) + ⁡ 𝜀𝑡 ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(4)
Where explanatory variables 𝐵𝑉𝑜𝑙,𝑡 equals the buy volume of BoE directors in hundreds on day 𝑡 and
𝑆𝑉𝑜𝑙𝑙,𝑡 equals the sell volume of BoE directors in hundreds on day 𝑡. Both the dependent variable and
the past 10 day return momentum control have the same interpretation as in the previous predictive
power regressions. As a result, 𝛽𝐵𝑉𝑜𝑙 can be interpreted as the average effect in 10 day future returns
BoE directors experienced when changing their buy volume and 𝛽𝑆𝑉𝑜𝑙 can be interpreted as the
average effect in 10 day future returns BoE directors experienced when changing their sell volume.
Table 1 Summary Statistics BoE Director Trades June 1715 - September 1725
Sell
Buy
#Trades
399
388
Average Volume
1410
1241
Trading Days
Non Negative NSP
Non Negative NSP
Dbuy
2684
214
8,0%
Dsell
2684
194
7%
Trading Days
Mean
Standard Deviation
Minimum
Maximum
Future 10 day return
2684
0,1939%
3,216%
-42,381%
26,248%
Bvol
2684
0,1775
0,927
0
18
Svol
2684
0,1784
1,058
0
34
This table reports the summary statistics of the data used for the predictive power regressions examining the trades of
BoE directors from June 1715 to September 1725
Table 1 presents the summary statistics of the data used for the predictive power regressions of BoE
directors. Between June 1715 and September 1725, 2684 trading days have taken place. BoE directors
conducted 399 sell trades and 388 buy trades in this period. Furthermore, of the 2684 trading days the
net share purchase of BoE directors was positive on 214 (8%) of the trading days and negative on 194
(7%) of the trading days.
Once the predictive power analysis of BoE directors have been conducted, the analysis turns towards
the outsiders that participated in BoE stock. As explained in the data description, also all the trades of
outsiders that transacted with BoE directors during June 1715 and September 1720 have been
gathered. As a result, this thesis is not only able to examine the profits insiders make, but also the
losses outsiders incur as the result of unregulated insider trading. To analyze the outsider trades, two
predictive power regressions based on the regressions of Frehen et al. (2013) are created, which take
28
the standpoint of the outsiders trading. The transactions conducted by outsiders are detangled in
transactions that occurred with insiders and outsiders. The first regression covers the buy transactions
and can be written as:
ln⁡(
𝑃𝑡+10⁡+⁡𝐷𝑡+10⁡
𝑃𝑡
) = ⁡𝛼 + ⁡ 𝛽𝐵𝑢𝑦 𝐷𝐵𝑢𝑦,𝑡 + 𝛽𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟 𝐷𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟,𝑡 + ⁡ ln⁡(
𝑃𝑡−10⁡+⁡𝐷𝑡−10⁡
𝑃𝑡
)+
𝜀𝑡 ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(5)
Dependent variable ln⁡(
𝑃𝑡+10⁡+⁡𝐷𝑡+10⁡
𝑃𝑡
) indicates the log return of 𝑡 + 10 days. The first independent
variable 𝐷𝐵𝑢𝑦,𝑡 is a dummy variable, which equals one on days where the net share purchase of
outsiders was positive, and zero if otherwise. Furthermore, The second independent variable
𝐷𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟,𝑡 is a dummy variable, which equals one on days where the net share purchase of outsiders
transacting with insiders was positive, and zero if otherwise. Identical as for the predictive power
regressions of BoE directors, ln⁡(
𝑃𝑡−10⁡+⁡𝐷𝑡−10⁡
𝑃𝑡
) is added to control for momentum behavior, which can
be interpreted as the past 10 day return. As a result, 𝛽𝐵𝑢𝑦 can be interpreted as the average future 10
day return outsiders experience on days their net share purchase was positive. Interesting feature of
this regression is that 𝛽𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟 can be interpreted as the extra future 10 day costs/revenues
outsiders incur on days they were net buyers from BoE directors. A similar regression is written with
respect to the sell transactions of outsiders and has the following notation:
ln⁡(
𝑃𝑡+10⁡+⁡𝐷𝑡+10⁡
𝑃𝑡
) = ⁡𝛼 + ⁡ 𝛽𝑆𝑒𝑙𝑙 𝐷𝑆𝑒𝑙𝑙,𝑡 + 𝛽𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟 𝐷𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟,𝑡 + ⁡ ln⁡(
𝑃𝑡−10⁡+⁡𝐷𝑡−10⁡
𝑃𝑡
)+
⁡𝜀𝑡 ⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡⁡(6)
Dependent variable ln⁡(
𝑃𝑡+10⁡+⁡𝐷𝑡+10⁡
𝑃𝑡
) indicates the log return of 𝑡 + 10 days. The first independent
variable 𝐷𝑆𝑒𝑙𝑙,𝑡 is a dummy variable, which equals one on days where the net share purchase of
outsiders was negative, and zero if otherwise. Furthermore, The second independent variable
𝐷𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟,𝑡 is a dummy variable, which equals one on days where the net share purchase of outsiders
transacting with insiders was negative, and zero if otherwise. As a result, 𝛽𝑆𝑒𝑙𝑙 can be interpreted as
the average future 10 day return outsiders experience on days they sold and 𝛽𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟 can be
interpreted as the extra future 10 day costs/revenues outsiders incur on days when they were net
sellers to BoE directors.
Table 2 presents the summary statistics of outsiders that have transacted with a BoE director between
June 1715 and September 1720. This data will be used in the predictive power regressions of outsiders.
Between June 1715 and September 1720, 1406 trading days have taken place. Outsiders conducted
5874 sell trades with among them 396 sell trades to BoE directors. Furthermore, the outsider group
29
engaged in 6103 buy trades and among these buy trades, 372 were with BoE directors. Furthermore,
of the 1406 trading days the NSP of outsiders was positive on 478 (34%) of the trading days and the
NSP with BoE directors was positive on 179 (13%) of the trading days. In addition, the NSP was negative
on 410 (29%) of the trading days and the NSP with BoE directors was negative on 175 (12%) of the
trading days.
Table 2 Summary Statistics Outsider Trades June 1715 - September 1720
Sell - OutsiderInsider
Sell - Insider
Buy-OutsiderInsider
Buy - Insider
#Trades
5874
396
6103
372
Average Volume
1052
1155
1057
1214
Trading Days
Non Negative NSP
Non Negative NSP
DBuy-OutsiderInsider
1406
478
34%
DBuy-Insider
1406
179
13%
DSell - OutsiderInsider
1406
410
29%
DSell - Insider
1406
175
12%
Trading Days
Mean
Standard Deviation
Minimum
Maximum
Future 10 day return
1397
0,4990%
3,151%
-13,036%
26,248%
This table reports the summary statistics of the data used for the predictive power regressions examining the trades of
outsiders from June 1715 to September 1720
After the profits have been examined, the analysis focusses on the effect of unregulated insider trading
on market liquidity. The proponents of insider trading regulation argue that a market maker, the trader
that in Kyle’s (1985) model determines the price in the market, wants to protect himself against better
informed traders. To do so, he expands the bid-ask spread when insiders are active, thereby increasing
the price impact of the order flow. Ideally, one would have data of the bid-ask spread or the net order
flow to examine the proposed liquidity effect. However, data of the bid-ask spread of the BoE requires
a lot of microstructure data that is either unavailable or unreliable. Furthermore, to determine the net
order flow the marker maker observes, one would have to identify market makers during our period
of analysis. This identification is given the available information not reliable, which might severely harm
the implacability of the test results. As a result, to examine the effect of BoE director trading on market
liquidity, it is chosen to adopt a measure that follows the concept of Kyle’s Lambda, namely Amihud’s
(2002) Illiquidity measure ILLIQ.5
5
Note that illiquidity is the opposite of liquidity. Consequently, a stock that has a high illiquidity has a low
liquidity and vice versa.
30
Amihud (2002) defines ILLIQ as the daily ratio of the absolute price change to its pound trading volume,
which is empirically defined as:
⁡𝐼𝐿𝐿𝐼𝑄𝑡 = ⁡
|𝑃𝑡 − 𝑃𝑡−1 |⁡
𝑉𝑜𝑙𝑢𝑚𝑒𝑡⁡
According to Amihud (2002), this is interpreted as the daily price response associated with a one pound
change in trading volume, which serves as a measure to examine the price impact of the order flow.
When ILLIQ is high, then the price impact of the order flow is high and relatively less trading volume is
needed to move the price of the asset. When ILLIQ is low, then then price impact of the order flow is
low and relatively more trading volume is needed to move prices. Advantage of this measure is that it
allows to construct illiquidity as a times series over the period of September 1720 to September 1725,
which allows to study the effects of BoE director trading on illiquidity over a longer period of time. As
a result, to expose the relationship between illiquidity and BoE director trading the following
regression is written:
𝐼𝐿𝐿𝐼𝑄𝑡 = ⁡𝛼 + ⁡ 𝛽1 %𝐼𝑛𝑠𝑖𝑑𝑒𝑟𝑠𝑡 + ⁡ 𝜀𝑡
(7)
Where independent variable %𝐼𝑛𝑠𝑖𝑑𝑒𝑟𝑠𝑡 is the percentage trading volume of BoE directors contained
in the total trading volume on trading day t and 𝐼𝐿𝐿𝐼𝑄𝑡 is the illiquidity on trading day t. As a result,
𝛽1 ⁡can be interpreted as the average effect of trading volume of BoE directors relative to total volume
on the illiquidity of the BoE stock on trading day t.
Table 3 Summary Statistics Illiquidity September 1720 – September 1725
Illiquidity
Trading Days
Mean
Standard Deviation
Minimum
Maximum
896
0,0209
0,0893
0
1,1605
%Insiders
896
3,033%
11,572%
0
100%
This table presents summary statistics of the data used in the illiquidity regressions, to examine
the impact of BoE director trading on the illiquidity of BoE Stock
Table 3 reports the summary statistics of the data used to examine the impact of BoE director trading
on the illiquidity of BoE Stock. In total there were 896 trading days between September 1720 and
September 1725. On average the Illiquidity of BoE stock was 0,0209, indicating that on average an
increase of a hundred pound trading volume moved the price of the BoE stock with 0,0209.
Furthermore, on average 3,033% of the total trading was trading volume of BoE directors during
September 1720 to September 1725.
31
Chapter 5 Results
Profits
Monthly Net Share Purchases and Monthly BoE Return
40000
0,4
30000
0,3
20000
0,2
10000
0,1
0
0
Monthly Net Share Purchase BoE Directors
1725
1725
1724
1724
1724
1724
1723
1723
1723
1723
1722
1722
1722
1722
1721
1721
1721
1721
1720
1720
1720
1720
1719
1719
1719
1719
1718
1718
1718
1718
1717
1717
1717
-0,5
1717
-50000
1716
-0,4
1716
-40000
1716
-0,3
1716
-30000
1715
-0,2
1715
-20000
1715
-0,1
1715
-10000
Monthly Bank of England Return
Figure 5: shows a time series of monthly net share purchases of BoE directors and monthly holding period returns of the BoE for each individual month between June 1715- September 1725.
The monthly net share purchase is the sum of the total buy volume of BoE directors in month t, minus the total sell volume of BoE directors in month t. The Monthly return is calculated as the
natural logarithm of the average price in month t+1 divided by the average price in month t.
32
Figure 3 provides a time series of the monthly net share purchases of BoE directors and the monthly
holding period returns of the BoE, for the months between June 1715 and September 1725. The
monthly net share purchases of BoE directors in month t have been calculated by taking the sum of
the BoE directors buy volume in month t minus the sum of the BoE directors sell volume in month t.
The monthly return of the BoE has been calculated by taking the natural logarithm of the average BoE
share price in month t+1 divided by the average BoE share price in month t. The pattern of the monthly
net share purchases shows periods where it moves in the same direction as the pattern of the monthly
BoE return. Especially during the 1720 South Sea Bubble, BoE directors massively bought more then
they sold prior to increases in returns. Subsequently, this figure indicates that BoE directors sold more
than they bought during the month the return decreased to negative levels. As a result, figure 3
indicates that there are periods in which BoE directors engaged in more buy then sell transactions prior
to increases in returns and BoE directors executed more sell then buy transactions prior to decreases
in returns, which provides preliminary evidence of BoE directors benefiting themselves with high
investment proceeds.
To provide more robust evidence on the profits BoE directors made, the analysis turns towards the
predictive power regressions of BoE directors as explained in the measure description of Chapter 4. If
hypothesis 1A is true – insiders that are unregulated in their trading earn higher returns with respect
to other market participants – then it is expected to find that 𝛽𝐵𝑢𝑦 is positive and significant, indicating
that BoE directors have high future returns on days their net share purchase was positive.
Furthermore, it is expected that 𝛽𝐵𝑉𝑜𝑙 is positive and significant, indicating that increased BoE director
buy volume positively predicted future returns. On the sell side, if hypothesis 1A is to be true, then it
is expected to find that 𝛽𝑆𝑒𝑙𝑙 is negative and significant, indicating that BoE directors have higher future
returns on days their net share purchase was negative6. On top of that, it is expected that 𝛽𝑆𝑉𝑜𝑙 ⁡is
negative and significant, indicating that increased BoE director sell volume positively predicted future
returns. Automatically, if hypothesis 1A is true, then hypothesis 1B – insiders that are unregulated in
their trading do not earn higher returns with respect to other market participants – is false or vice
versa.
The results of the predictive power regressions of BoE director trades from the full June 1715 –
September 1725 sample period are presented in table 4. Regression 1 shows that 𝛽𝐵𝑢𝑦 is positive and
marginally significant at a 10% level. This coefficient takes the value of 0,00840, which allows to
interpret that BoE directors earned on average a future 10 day return of 0,840% on days they bought
more shares then they sold. Regression 2 shows that 𝛽𝑆𝑒𝑙𝑙 is negative, however this coefficient is non-
6
It is assumed that ex-post trading decreases in share price can be interpreted as a positive return for a seller.
33
significant indicating that BoE directors did not have statistically high returns on days their net share
purchase was negative. In regression 3, the buy and sell dummies are regressed in a multivariate
regression. The result is that 𝛽𝐵𝑢𝑦 becomes slightly lower and stays significant at the 10% level.
Furthermore, 𝛽𝑆𝑒𝑙𝑙 remains non-significant. As a result, it can be concluded that BoE directors only
earned significant higher future 10 day returns on days their net share purchase was positive during
the full sample period. Regression 4, exposes the relationship between the buy and sell volume of BoE
directors and the future 10 day return. However, this regression does not present any significant
findings, indicating that trading volumes of BoE directors inhibited no predictive power during June
1715 and September 1725.
Table 4 Predictive Power BoE Director Trades June 1715 – September 1725
Regressions
(1)
(2)
(3)
(4)
Variables
Future 10 Day Return
Future 10 Day Return
Future 10 Day Return
Future 10 Day Return
βBuy
0.00840*
0.00809*
(1.78293)
(1.72906)
βSell
-0.00462
-0.00392
(-0.99863)
(-0.85643)
βBVol
0.00175
(1.46162)
βSVol
0.00002
(0.03433)
α
Past 10 day return
0.00104
0.00205
0.00135
0.00139
(0.68364)
(1.51477)
(1.04416)
(0.94683)
0.12519
0.12176
0.12477
0.12447
(1.32082)
(1.29031)
(1.31857)
(1.31105)
Observations
2,674
2,674
2,674
2,674
This table represents regressions 1, 2, 3 and 4 of the measurement description section in Chapter 4 respectively. Data are all BoE
director transactions in the full June 1715 – September 1725 sample period. The regressions are Ordinary Least Squares (OLS)
regression’s with Newey-West consistent standard errors. Dependent variable in all four regressions of this table, is the future
10 day return. Past 10-day return is added in all four regressions to control for momentum behavior. The t-statistics are reported
in parentheses below the coefficients and ***, **, * indicates significance of the coefficient at a 1%, 5% or 10% level respectively.
In regression 1 the independent variable is a dummy variable that equals one on days where the net share purchase of BoE
directors was positive, and zero if otherwise. Coefficient 𝛽𝐵𝑢𝑦 is the mean future 10 day return of days on which BoE directors
bought more shares then they sold, and 𝛼 is the mean future 10 day return of days on which BoE directors have not bought more
shares then they sold. In regression 2 the independent variable is a dummy variable which equals one on days where the net
share purchase of BoE directors was negative, and zero if otherwise. Coefficient 𝛽𝑆𝑒𝑙𝑙 is the mean future 10 day return of days on
which BoE directors sold more shares then they bought, and 𝛼 is the mean future 10 day return of days on which BoE directors
have not sold more shares then they bought. In regression 3 both buy and sell dummies are regressed in a multivariate regression.
In regression 4 the buy and sell trading volumes of BoE directors are regressed on future 10 day returns. Coefficient 𝛽𝐵𝑉𝑜𝑙 is the
average effect in 10 day future returns BoE directors experienced when changing their buy volume with thousand and 𝛽𝑆𝑉𝑜𝑙 is
the average effect in 10 day future returns BoE directors experienced when changing their sell volume with thousand.
For the economic interpretation it is estimated how much excess returns BoE directors made with their
buy transactions on a yearly basis. For this purpose regression 1 is used, where the 𝛽𝐵𝑢𝑦 of 0,840% is
taken. This can be interpreted as a future 10 day return of days on which BoE directors were net buyers.
Assuming that a year on average has 250 trading days, then on a yearly basis this future 10 day return
34
would amount up to 21%7. To provide a benchmark, the mean in future 10 day return covering the
identical period is taken from the summary statistics in chapter 4 and subsequently is calculated on a
yearly basis. This amounts up to 4,83%8 and can be interpreted as the average yearly return of
following a “buy and hold” strategy with BoE stock. If one then compares the average yearly return
BoE directors experienced on days their net share purchase was positive to the buy and hold return of
BoE stock, it is concluded that BoE directors experienced economically significant returns with their
buy transactions during June 1715 and September 1725, providing evidence in line with hypothesis 1A.
As explained in the historical background chapter, the incentives for BoE directors during the South
Sea Bubble were to increase the stock price of the BoE in order to have the best chances of winning
the Debt-for-Equity deal with the English government. As a result, a second test is run to examine the
predictive power of BoE director trades during the 1720 South Sea Bubble. The summary statistics
regarding the data of this period are presented in table 1 of appendix A and the results of this test are
shown in table 5. Regression 2 and 3 still show that BoE directors did not experience positive future 10
day returns on days where their net share purchase was negative. However, regression 1 provides
interesting results with respect to 𝛽𝐵𝑢𝑦 , which becomes more significant and has multiplied more than
five times compared to the buy coefficient covering the full sample period in table 4. This coefficient
allows to interpret that BoE directors earned on average a future 10 day return of 4,521% on days they
bought more shares then they sold during 1720. On top of that, in regression 4 𝛽𝐵𝑉𝑜𝑙 is found to be
positive and marginally significant at a 10% level. The coefficient allows to interpret that a hundred
increase in nominal value of BoE director buy volume on average predicted an increase in future 10
day return of 1,297% in BoE stock. Based on the results of table 2, it is statistically concluded that BoE
director trades inhibited strong predictive power during the 1720 South Sea Bubble.
For the economic interpretation again 𝛽𝐵𝑢𝑦 of regression 1 in table 2 is taken. This coefficient equals
4,521% which is the average future 10 day return on days the net share purchase of BoE directors was
positive. This average amounts up to 113,01%9 on a yearly basis for the 1720 South Sea Bubble year.
If from the summary statistics provided in table 1 from appendix A, the average future 10 day return
covering 1720 is taken and subsequently is calculated on a yearly basis, it is concluded that the buy
and hold return of BoE stock in 1720 equals -3,55%10. Based on the findings, then it is concluded that
especially during the 1720 South Sea Bubble, BoE directors earned economically significant returns
providing strong support for hypothesis 1A.
7
0,840% * 25 = 21%
0,193% * 25 = 4,83%
9
4,521% * 25 = 113,01%
10
-0,142% * 25 = -3,55%
8
35
Table 5 Predictive Power BoE Director Trades 1720 South Sea Bubble
Regressions
(1)
(2)
(3)
(4)
Variables
Future 10 Day Return
Future 10 Day Return
Future 10 Day Return
Future 10 Day Return
βBuy
0.04521**
0.04261**
(2.29349)
βSell
(2.27655)
-0.02098
-0.01225
(-1.42027)
(-0.95234)
βBVol
0.01297*
(1.89752)
βSVol
-0.00097
(-0.28723)
α
Past 10 day return
-0.00874
0.00260
-0.00614
-0.00645
(-0.63522)
(0.20973)
(-0.48863)
(-0.47421)
0.16524
0.15866
0.16547
0.17045
(1.35926)
(1.27175)
(1.36106)
(1.36846)
Observations
266
266
266
266
This table represents regressions 1, 2, 3 and 4 of the measurement description section in Chapter 4 respectively. Data are all BoE
director transactions in 1720 including the South Sea Bubble. The regressions are Ordinary Least Squares (OLS) regression’s with
Newey-West consistent standard errors. Dependent variable in all four regressions of this table is the future 10 day return. Past
10-day return is added in all four regressions to control for momentum behavior. The t-statistics are reported in parentheses
below the coefficients and ***, **, * indicates significance of the coefficient at a 1%, 5% or 10% level respectively. In regression
1 the independent variable is a dummy variable that equals one on days where the net share purchase of BoE directors was
positive, and zero if otherwise. Coefficient 𝛽𝐵𝑢𝑦 is the mean future 10 day return of days on which BoE directors bought more
shares then they sold, and 𝛼 is the mean future 10 day return of days on which BoE directors have not bought more shares then
they sold. In regression 2 the independent variable is a dummy variable which equals one on days where the net share purchase
of BoE directors was negative, and zero if otherwise. Coefficient 𝛽𝑆𝑒𝑙𝑙 is the mean future 10 day return of days on which BoE
directors sold more shares then they bought, and 𝛼 is the mean future 10 day return of days on which BoE directors have not
sold more shares then they bought. In regression 3 both buy and sell dummies are regressed in a multivariate regression. In
regression 4 the buy and sell trading volumes of BoE directors are regressed on future 10 day returns. Coefficient 𝛽𝐵𝑉𝑜𝑙 is the
average effect in 10 day future returns BoE directors experienced when changing their buy volume with thousand and 𝛽𝑆𝑉𝑜𝑙 is
the average effect in 10 day future returns BoE directors experienced when changing their sell volume with thousand.
Since evidence is found of BoE director trades inhibiting predictive power during the full sample period,
and especially during the 1720 South Sea Bubble, also the predictive power of BoE directors in the full
period excluding the 1720 is examined. The summary statistics regarding the data of this period are
presented in table 2 of appendix A and the results of the analysis are presented in table 6. With respect
to the coefficients in regressions 1, 2, 3 and 4 it can be concluded that all have become small compared
to the coefficients of the regressions of tables 4 and 5, and more importantly all have become
insignificant. As a result, it can be concluded that BoE director trades inhibited no predictive power on
days they were net buyers nor on day they were net sellers, during the full sample period excluding
1720. Therefore, the evidence provided by this analysis does not provide support that BoE directors
earned higher returns, which allows to reject hypothesis 1A, and accept hypothesis 1B in the full period
excluding 1720 and the South Sea Bubble.
36
Table 6 Predictive Power BoE Director Trades June 1715 – September 1725 excluding 1720 South Sea Bubble
Regressions
(1)
(2)
(3)
(4)
Variables
Future 10 Day Return
Future 10 Day Return
Future 10 Day Return
Future 10 Day Return
βBuy
-0.00012
-0.00010
(-0.07151)
βSell
(-0.06070)
0.00027
0.00026
(0.21109)
(0.20478)
βBVol
-0.00040
(-0.82942)
βSVol
0.00012
(0.58151)
α
Past 10 day return
0.00232***
0.00230**
0.00230**
0.00236***
(2.58932)
(2.50147)
(2.52515)
(2.63355)
0.00449
0.00468
0.00459
0.00399
(0.08274)
(0.08612)
(0.08438)
(0.07357)
Observations
2,408
2,408
2,408
2,408
This table represents regressions 1, 2, 3 and 4 of the measurement description section in Chapter 4 respectively. Data are all BoE
director transactions in 1720 including the South Sea Bubble. The regressions are Ordinary Least Squares (OLS) regression’s with
Newey-West consistent standard errors. Dependent variable in all four regressions of this table is the future 10 day return. Past
10-day return is added in all four regressions to control for momentum behavior. The t-statistics are reported in parentheses
below the coefficients and ***, **, * indicates significance of the coefficient at a 1%, 5% or 10% level respectively. In regression
1 the independent variable is a dummy variable that equals one on days where the net share purchase of BoE directors was
positive, and zero if otherwise. Coefficient 𝛽𝐵𝑢𝑦 is the mean future 10 day return of days on which BoE directors bought more
shares then they sold, and 𝛼 is the mean future 10 day return of days on which BoE directors have not bought more shares then
they sold. In regression 2 the independent variable is a dummy variable which equals one on days where the net share purchase
of BoE directors was negative, and zero if otherwise. Coefficient 𝛽𝑆𝑒𝑙𝑙 is the mean future 10 day return of days on which BoE
directors sold more shares then they bought, and 𝛼 is the mean future 10 day return of days on which BoE directors have not
sold more shares then they bought. In regression 3 both buy and sell dummies are regressed in a multivariate regression. In
regression 4 the buy and sell trading volumes of BoE directors are regressed on future 10 day returns. Coefficient 𝛽𝐵𝑉𝑜𝑙 is the
average effect in 10 day future returns BoE directors experienced when changing their buy volume with thousand and 𝛽𝑆𝑉𝑜𝑙 is
the average effect in 10 day future returns BoE directors experienced when changing their sell volume with thousand.
In the above regressions the returns of BoE directors have been examined relative to the returns of
the BoE. This provided mixed evidence, on the one hand it is found that BoE directors were able to
predictive future 10 day returns during the full sample period and especially strong evidence is found
during the 1720 South Sea Bubble. On the other hand, BoE director trades did not inhibit any significant
predictive power during the full sample period excluding the 1720 South Sea Bubble. However, to
judge on the effects of unregulated insider trading, one wants not an analysis limited to the profits of
insiders but also wants to examine the losses outsiders incur when trading with unregulated insiders.
As a result, the analysis turns towards predictive power regressions 5 and 6, which examine the trading
of outsiders as explained in the measurement description of Chapter 4. The focus point in the analysis
will be on 𝛽𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟 and 𝛽𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟 , which can be interpreted as the average 10 day extra
costs/revenues outsiders incur on days they were net buyers or net sellers to BoE directors. If
hypothesis 1A holds, then it is expected that BoE directors earn on average higher returns with respect
to other market participants. As a result, in regression 5 it is expected that 𝛽𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is significant
37
and is lower than 𝛽𝐵𝑢𝑦 , indicating that outsiders incurred extra future 10 day costs on days they are
net buyers from BoE directors relative to days they bought from both directors and outsiders.
Following the same logic, then in regression 6 it is expected that 𝛽𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is significant and higher
then 𝛽𝑆𝑒𝑙𝑙 , indicating that outsiders incurred extra future 10 day losses on days they were net sellers
to BoE directors relative to days they sold to both directors and outsiders.
The results of the predictive power regressions of outsiders are presented in table 7. First, panel A is
analyzed which covers the trades of the period June 1715 to September 172011. The regression
estimates shows that 𝛽𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is lower than 𝛽𝐵𝑢𝑦 and that 𝛽𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is higher than 𝛽𝑆𝑒𝑙𝑙 , indicating
that outsiders incurred extra cost on days they were net buyers and net sellers to BoE director versus
days they bought and sold to BoE directors and outsiders combined. However, all coefficients are nonsignificant and therefore miss the power to draw a statistically meaningful conclusion. As a result, it is
concluded that outsiders did not incur significant extra losses on days they traded with BoE directors
during the period of June 1715 to September 1720, providing evidence in line with hypothesis 1B.
Second, the trading of outsiders during the South Sea Bubble is analyzed. The summary statistics
regarding the data of this period are presented in table 3 of appendix A and the subsequent results are
presented in Panel B of table 7. As for the buy transactions examined with regression 5, no statistically
significant regression estimates are found. However, regression 6 of Panel B provides interesting
results with a 𝛽𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟 equaling 0,03837, which is significant at a 5% level. This allows to conclude
that the average 10 day loss outsiders extra incurred on days they were net sellers to BoE directors
equals 3,837% during the 1720 South Sea Bubble.
To determine the economic magnitude, it is estimated what this 10 day loss on a yearly basis amounts
up to. If a trading year consists of 250 trading days then on an average yearly basis outsiders incur
95,925%12 extra costs on days they were net sellers to BoE directors during the year 1720. Given that
the average future 10 day return covering the identical period equals 2,667%13, it is calculated that on
a yearly basis this buy and hold return amounts up to 66,69%14. If one compares the extra cost an
outsider incurs on days they were net sellers to BoE directors with the average buy and hold return for
the identical period, one can conclude that outsiders incur on average economically significant extra
11
Due to time constraints only the trades of outsider that transacted with BoE directors during June 1715 –
September 1720 have been gathered.
12
3,837% * 25 = 95,925%
13
See summary statistics on future 10 day return in table 3 of appendix A.
14
2,667% * 25 = 66,69%. Note, that this return is that high since the period leading up to the closing of the
transfer books in September 1720, is the run up period in the South Sea Bubble. Once the transfer books
opened again at the end of September 1720 the bubble collapsed. However, this data is not available and
therefore not included in the analysis.
38
losses on days they were net sellers to BoE directors during the 1720 South Sea Bubble, providing
evidence in line with hypothesis 1A.
Table 7 Predictive Power Outsider Trades
Panel A 1715-1720
Regressions
(5)
Future 10 Day
Return
βBuy
0.00302
0.02226
0.00055
(1.47436)
(1.49610)
(0.55937)
-0.00112
-0.00692
-0.00171
(-0.39488)
βSellInsider
Past 10 Day
Return
(6)
Future 10 Day
Return
(-0.41703)
-0.00178
βSell
Constant
(5)
Future 10 Day
Return
Panel A 1715-1719
VARIABLES
βBuyInsider
(6)
Future 10 Day
Return
Panel B 1720
(5)
Future 10 Day
Return
(6)
Future 10 Day
Return
(-1.27151)
-0.01498
0.00041
(-0.93574)
(-1.05487)
(0.38127)
0.00623
0.03837**
-0.00144
(1.48989)
(2.42550)
(-0.74515)
0.00367**
0.00429**
0.01744
0.02112
0.00274**
0.00272*
(2.24116)
(2.45600)
(1.16593)
(1.32825)
(2.04588)
(1.86836)
0.08870
0.09084
0.05541
0.06479
0.02732
0.02721
(0.57251)
(0.58144)
(0.29323)
(0.32585)
(0.44921)
(0.44825)
1,387
1,387
131
131
1,256
1,256
Observations
This table represents regressions 5 and 6 of the measurement description section in Chapter 4. Data in panel A are all trades
of outsiders that have transacted with BoE directors during the full June 1715 – September 1720 sample period. The data
in panel B are all the outsider trades in 1720 and the data in panel C is the full period excluding 1720. The regressions are
Ordinary Least Squares (OLS) regression’s with Newey-West consistent standard errors. Dependent variable in regression
5 and 6 is the future 10 day return. Past 10-day return is added in both regressions to control for momentum behavior. The
t-statistics are reported in parentheses below the coefficients and ***, **, * indicates significance of the coefficient at a
1%, 5% or 10% level respectively. In regression 5, the first independent variable 𝐷𝐵𝑢𝑦,𝑡 is a dummy variable that equals one
on days where the net share purchase of outsiders was positive, and zero if otherwise. The second independent variable
𝐷𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟,𝑡 is a dummy variable, which equals one on days where the net share purchase of outsiders transacting with
insiders was positive, and zero if otherwise. 𝛽𝐵𝑢𝑦 is the average future 10 day return outsiders experience on days their net
share purchase was positive and 𝛽𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is the average extra future 10 day costs/revenues outsiders incur when their
net share purchase from BoE directors was positive. In regression 6, the first independent variable 𝐷𝑆𝑒𝑙𝑙,𝑡 is a dummy
variable, which equals one on days where the net share purchase of outsiders was negative, and zero if otherwise. The
second independent variable 𝐷𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟,𝑡 is a dummy variable, which equals one on days where the net share purchase of
outsiders transacting with insiders was negative, and zero if otherwise. 𝛽𝑆𝑒𝑙𝑙 is the average future 10 day return outsiders
experience on days their net share purchase was negative and 𝛽𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is the average extra future 10 day costs/revenues
outsiders incur when their net share purchase from BoE directors was negative.
In Panel C of table 7, the regression results covering full sample period of the outsider trades excluding
1720 and its South Sea Bubble are presented15. Both regressions 5 and 6 have lower coefficients
compared to the periods of analysis of panel A and B and both have become insignificant. As a result,
outsiders did not incur significant extra losses on days they were net sellers or net buyers from BoE
15
The summary statistics regarding the data of this period are presented in table 4 of appendix A
39
directors during this period. This allows to conclude that outsiders during June 1715 and March 1719
had little to fear from trading with BoE directors, which provides evidence that support hypothesis 1B.
As a robustness check, regressions 1 until 5 have been regressed with investment horizons of 5 and 20
days for the full sample period, the 1720 South Sea Bubble and the full sample period excluding the
1720 South Sea Bubble. Given that the majority of the analysis findings is unsurprising, it is chosen to
present the results in appendix B. However, two findings require additional attention. First, the
estimate of 𝛽𝑆𝑒𝑙𝑙 of regression 2 from panel B in table 2 provides interesting results. This coefficient
allows to infer that on average BoE directors made a future 20 day return on days their net share
purchase was negative of 5,196% during the 1720 South Sea Bubble. Second, interesting results are
presented in panel B of table 5. The regressions examine the outsider trades during 1720 on a 20 days
horizon. Interestingly the regression estimates show that 𝛽𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is significant and lower than 𝛽𝐵𝑢𝑦
and that 𝛽𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is significant higher than 𝛽𝑆𝑒𝑙𝑙 , indicating that outsiders incurred significant extra
losses on days they were net buyers and net sellers to BoE director on a 20 day horizon. As a result,
these findings provide further evidence of BoE directors making high profits, not only when their net
share purchase was positive but also when negative, on a 20 day window during the 1720 South Sea
Bubble, which strengthens the support for hypothesis 1A.
To conclude, the predictive power regressions provide mixed result with respect to hypothesis 1A and
1B. The predictive power analysis of BoE director trades finds hypothesis 1A supported during the full
sample period and especially strong support in line with this hypothesis is found during the 1720 South
Sea Bubble. However, if the bubble year is excluded from the full sample period the results support
hypothesis 1B. On top of that, the predictive power regressions of outsiders present by and large the
same results. Outsiders pay both statistically and economically significant extra costs on days they
traded with BoE directors during the 1720 South Sea Bubble, consequently hypothesis 1A. However,
when again the South Sea Bubble is excluded from the analysis then no support for hypothesis 1A is
found and hypothesis 1B is accepted.
Liquidity
As explained in chapter 4, Amihud’s ILLIQ measure is adopted to examine the effect of BoE director
trading on the liquidity of BoE stock with the goal to provide evidence for the proponent and opponent
of regulation hypotheses. If proponents hypothesis 2A – Insider trading activity has a negative effect
on market liquidity – is true, then it is expected to find that when more BoE directors trade than the
market becomes more illiquid. In regression 7, it is then expected to find that the 𝛽1 of %𝐼𝑛𝑠𝑖𝑑𝑒𝑟𝑠𝑡 is
positive and significant. On the other hand, if hypothesis 2A is false than opponent hypothesis 2B –
40
Insider trading activity has no effect on market liquidity – is true. This would be the case if 𝛽1 would
be non-significant.
Table 8 reports the results of the ILLIQ regressions covering the period of September 1720 to
September 1725. Regression 7 shows that 𝛽1 is negative and non-significant. This allows to infer that,
a standard deviation increase in the trading volume of BoE directors relative to total trading volume
on day t has on average no significant effect on the illiquidity of BoE stock on day t during September
1720 to September 1725. Since the effect of increased trading volume of BoE directors on BoE
illiquidity might have a delayed effect, also regression 7 is run with 𝐼𝐿𝐿𝐼𝑄𝑡+1 , 𝐼𝐿𝐿𝐼𝑄𝑡+2 , as dependent
variables. The estimates are exposed in regressions 8 and 9 respectively. If 𝛽1 of regression 7 is
compared to 𝛽1 of regression 8 and 9, then the estimates show that the coefficient becomes
increasingly positive over time indicating that BoE director volume has an increased impact on the
Illiquidity of BoE stock. However, the coefficient refrains from reaching a significance level that would
allow to draw a statistically powerful conclusion from the regression estimates. As a result, the
regression estimates show no significant findings and therefore it can be concluded that also over time
increased trading volume of BoE directors relative to total trading volume, has no significant effect on
the illiquidity of BoE stock. Therefore, the evidence of the ILLIQ regressions reject hypothesis 2A and
accept hypothesis 2B, that insider trading activity has no effect on market liquidity.
Table 8 The Effect of BoE Director Trading on Illiquidity of BoE Stock September 1720 – September 1725
Panel A
Panel B
Panel C
Regression
(7)
(8)
(9)
VARIABLES
ILLIQ t
ILLIQ t+1
ILLIQ t+2
β1 %Insiders t
Constant
Observations
-0.01325
0.00273
0.00580
(-0.51341)
(0.08981)
(0.18402)
0.02130***
0.02459***
0.02307***
(6.90803)
(6.00757)
(5.87931)
896
658
594
0.00029
0.00001
0.00006
R-squared
This table reports the OLS regressions estimates of regression 7, 8 and 9.Regression 7 is explained in the measurement
description section in chapter 4. Independent variable in this regression is %𝐼𝑛𝑠𝑖𝑑𝑒𝑟𝑠𝑡 is the daily percentage trading
volume of BoE directors contained in the total trading volume on trading day t and 𝐼𝐿𝐿𝐼𝑄𝑡 is the illiquidity of trading day
t. 𝛽1 ⁡is the average effect of changes in the percentage trading volume of BoE directors on the illiquidity of the BoE stock
in period t. In regression 8 and 9, all stays equal to regression 7 only the dependent variable changes and to 𝐼𝐿𝐿𝐼𝑄𝑡+1 and
𝐼𝐿𝐿𝐼𝑄𝑡+2 in regression 8 and 9 respectively. The t-statistics are reported in parentheses below the coefficients and ***,
**, * indicates significance of the coefficient at a 1%, 5% or 10% level respectively.
41
Chapter 6 Discussion and Conclusion
In this chapter the implications of the results are discussed in the light of what is known given the
current body of research. First, the discussion focusses on the profit area, afterwards discussion turns
towards the liquidity area and finally both areas will be combined to draw a conclusion.
Discussion
Earlier it was argued by proponents of insider trading regulation that insiders significantly exploit their
continuous information advantage to benefit themselves at the expense of the rest of the market. This
provided the fundament for proponent hypothesis 1A: “Insiders that are unregulated in their trading
earn higher returns with respect to other market participants”. In contrast, opponents of regulation
argue that insiders do not significantly exploit their information advantage at the expense of outsiders.
This provided the opponent hypothesis 1B: “Insiders that are unregulated in their trading do not earn
higher returns with respect to other market participants”.
The findings of the predictive power regressions in the profit area, provide evidence that supports both
the proponent and opponent hypotheses. The evidence of the predictive power regressions excluding
the 1720 South Sea Bubble showed, in line with the opponent hypothesis 1B, that BoE directors did
not experience significantly higher returns compared to other market participants during this period.
Remarkable for this period is the low price volatility16, which decreases the likelihood that insiders
possessed valuable ex-ante private information that enabled them to profit on. Therefore, it is likely
that the benefits for insiders to engage in opportunistic behavior and exploit outsiders are considered
as low, and do not outweigh the potential costs when cheating. Based on this evidence it can be
concluded that in a relatively less volatile period, outsiders do not need to be protected for insiders.
Moreover, under the assumption that insider trading improves market efficiency, then insider trading
regulation in a relative calm period could be harmful to the market to the extend it reduces the
informativeness of the securities price.
However, considering the findings of the predictive power regressions examining the trades of insiders
from 1720 and it’s South Sea Bubble, it can be concluded that during this period insiders excessively
benefitted themselves at the expense of uninformed outsiders. Remarkable in this period is the high
price volatility of the BoE when one considers the period of the 1720 South Sea Bubble relative to the
period of analysis excluding the bubble period. This volatility is likely to be explained as the result of
16
For an empirical comparison in volatility between 1720 and its South Sea Bubble and the full sample period
excluding the bubble, compare the standard deviation in future 10 day return presented in appendix A from
table 1 with table 2 and table 3 with table 4.
42
new information on the developments of the Debt-for-Equity deal17. Given the role BoE directors had
in the BoE, it is likely that they had first access to new information on these developments, which
provided them with ex-ante private information about future price movements in BoE stock. Seen in
this light, the high ex-ante private information of BoE directors increased the potential to engage in
opportunistic behavior. In turn, the evidence provided by the predictive power regressions shows that
BoE directors exploited this information asymmetry significantly hurting outsiders. Therefore, it can
be concluded that at these time (i.c. the 1720 South Sea Bubble) outsiders probably lose more than
they would if BoE directors were regulated and prohibited to trade on their ex-ante private
information.
In the liquidity discussion, proponents of insider trading argue that the practice of insider trading
creates structural disadvantages for outsiders reducing their willingness to trade and in turn reducing
the liquidity of the market. This originated in proponent hypothesis 2A: “Unregulated insider trading
activity has a negative effect on market liquidity”. On the other hand, opponents of regulation argue
that outsiders have no structural disadvantages and are therefore are willing to trade with insiders,
which in turn does not affect the liquidity of the market. As a result, opponent hypothesis 2B:
“Unregulated insider trading activity has no effect on market liquidity”, was drawn.
The findings of this thesis provide evidence that BoE director trading activity has no significant effect
on the illiquidity of BoE stock. Therefore, in contrast with what the proponents of insider trading
regulation would expect, unregulated BoE director trading did not increase the price impact of the
order flow of BoE stock. Based on the findings of this study, it is therefore concluded that the reasoning
of the proponents of regulation that unregulated insider trading activity creates structural
disadvantages and reduces the willingness to trade of outsiders does not hold. This allows to draw the
conclusion based on the encountered evidence that insider trading should not be regulated out of the
reason that insiders reduce the liquidity of their companies stock.
Conclusion
This thesis examined the impact of unregulated insider trading on the distribution of investment
proceeds and the market liquidity. In contrast with the argument of the SEC, it is concluded that insider
trading should not be regulated out of the reason that it reduces the liquidity of their own company’s
shares. Furthermore, it is concluded that insiders possess ex-ante private information and acting
opportunistically on that information comes at a cost when insiders cheat with that information at the
17
As explained in the historical background section, the South Sea Bubble finds its roots in the competition for
the Debt-for-Equity deal with the English government
43
expense of outsiders. In this respect insiders refrain, in in a relatively less volatile period, from
exploiting their information advantage and incurring the cost of cheating. However, when the amount
of ex-ante private information increases (i.c. the 1720 South Sea Bubble), then the benefits of behaving
opportunistically on the private information outweigh the cost of cheating for the insider. In this
respect, outsiders have to be protected for insiders at times insiders possess high amounts of ex-ante
private information. Given the fact that one cannot predict future events that provide insiders with
this information advantages, it is concluded that to restrain the potential for insiders to engage in
opportunistic behavior, insider trading should be regulated.
Limitations and Recommendations for Future Research
This thesis has several limitations. First, due to time constraints it was unable to gather the trades of
outsiders that transacted with BoE directors during the period September 1720 to September 1725.
Specifically, the first days after the opening of the transfer books in September 1720 proved to be days
that the South Sea Bubble collapsed. Therefore this thesis is unable to examine how outsiders traded
when the South Sea Bubble in BoE stock experienced its largest decreases in price. Second, only the
trades of outsiders that have transacted with BoE directors have been gathered. This means that a
specific group of outsiders that have not transacted with BoE directors is not included in the analysis.
Third, the price series used in the analysis reports a single price for each trading day. However, there
could be considerable intra-day volatility especially during the South Sea Bubble. Finally, for the
liquidity analysis one would ideally possess reliable information on the bid-ask spread, unfortunately
this data is unavailable for the BoE in the early 18th century.
Interesting directions for future research would be to examine the proposed advantage of insider
trading, namely how unregulated insiders (BoE directors) affect the informativeness of the security’s
(BoE’s) price. This study provides empirical evidence on the profits and liquidity discussion, which are
the proposed disadvantages of insider trading. However, to be able to give a complete picture of the
effects of unregulated insider trading, one should additionally study the proposed advantages. On top
of that, the effectiveness of the insider trading regulation is an interesting point of departure for future
research. In the ideal situation one should be able to provide empirical evidence on how much profits
insiders make if unregulated compared to the profits identical insiders would make when regulated.
44
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46
Appendix A, Additional Summary Statistics
This appendix provides tables with additional summary statistics.
Table 1 Summary Statistics BoE Director Trades 1720 South Sea Bubble
Sell
Buy
#Trades
99
117
Average Volume
1288
1022
Trading Days
Non Negative NSP
Non Negative NSP
Dbuy
266
45
17%
Dsell
266
47
18%
Trading Days
Mean
Standard Deviation
Minimum
Maximum
Future 10 day return
266
-0,1420%
8,911%
-42,381%
26,248%
Bvol
266
0,4495677
1,21181
0
9,5
Svol
266
0,4793233
1,40720
0
16
This table reports the summary statistics of the data used for the predictive power regressions examining the trades of
BoE directors in 1720 and its South Sea Bubble
Table 2 Summary Statistics BoE Director Trades June 1715 - September 1725 excluding 1720 South Sea Bubble
Sell
Buy
#Trades
240
271
Average Volume
1460
1336
Trading Days
Non Negative NSP
Non Negative NSP
Dbuy
2418
169
7%
Dsell
2418
147
6%
Trading Days
Mean
Standard Deviation
Minimum
Maximum
Future 10 day return
2418
0,2310%
1,662%
-8,164%
7,869%
Bvol
2418
0,148698
0,88500
0
18
Svol
2418
0,14449
1,00810
0
34
This table reports the summary statistics of the data used for the predictive power regressions examining the trades of
BoE directors in from June 1715 to September 1725 excluding 1720 and its South Sea Bubble
47
Table 3 Summary Statistics Outsider Trades 1720 South Sea Bubble
Sell - OutsiderInsider
Sell - Insider
Buy-OutsiderInsider Buy - Insider
#Trades
1305
115
1562
94
Average Volume
1014
1113
964
1084
Trading Days
Non Negative NSP
Non Negative NSP
DBuy-OutsiderInsider
140
69
50%
DBuy-Insider
140
58
42%
DSell - OutsiderInsider
140
62
45%
DSell - Insider
140
56
40%
Trading Days
Mean
Standard Deviation
Minimum
Maximum
Future 10 day return
131
2,6676%
8,290%
-13,036%
26,248%
This table reports the summary statistics of the data used for the predictive power regressions examining the trades of outsiders in 1720
and its South Sea Bubble
Table 4 Summary Statistics Outsider Trades June 1715 - March 1719
Sell - OutsiderInsider
Sell - Insider
Buy-OutsiderInsider
Buy - Insider
#Trades
4569
281
4541
278
Average Volume
1055
1172
1089
1258
Trading Days
Non Negative NSP
Non Negative NSP
DBuy-OutsiderInsider
1266
419
33%
DBuy-Insider
1266
148
12%
DSell - OutsiderInsider
1266
372
29%
DSell - Insider
1266
148
12%
Trading Days
Mean
Standard Deviation
Minimum
Maximum
Future 10 day return
1266
0,2746%
1,832%
-8,164%
7,736%
This table reports the summary statistics of the data used for the predictive power regressions examining the trades of outsiders from
June 1715 to March 1719 (excluding the 1720 South Sea Bubble)
48
Appendix B, 5 and 20 Day Investment Horizons
As a robustness check, regressions 1 until 5 have been regressed with investment horizons of 5 and 10 days for the full sample period, the 1720 South Sea
Bubble and the full sample period excluding the 1720 South Sea Bubble. The estimates of these regressions are presented in the following tables.
Table 1 Predictive Power BoE Director Trades June 1715 – September 1725, Tau = 5 and Tau = 20
Panel A τ=5
Panel B τ=20
Regressions
(1)
(2)
(3)
(4)
(1)
(2)
(3)
(4)
Future 5 Day
Future 5 Day
Future 5 Day
Future 5 Day
Future 20 Day
Future 20 Day
Future 20 Day
Future 20 Day
Variables
Return
Return
Return
Return
Return
Return
Return
Return
βBuy
0.00533*
0.00497*
0.01641
0.01567
(1.81229)
(1.74291)
(1.36879)
(1.29848)
βSell
-0.00496
-0.00454
-0.01072
-0.00937
(-1.07963)
(-0.99847)
(-1.36068)
(-1.17903)
βBVol
0.00074
0.00402
(0.94770)
(1.42411)
βSVol
-0.00020
-0.00072
(-0.31750)
(-0.62373)
α
0.00077
0.00156**
0.00113
0.00110
0.00243
0.00454
0.00317
0.00316
(0.81444)
(2.06242)
(1.57536)
(1.26342)
(0.78336)
(1.38954)
(1.17249)
(0.98060)
LN (Pt-τ/Pt)
-0.09780
-0.10019
-0.09819
-0.09915
0.16459
0.16222
0.16469
0.16306
(-1.50368)
(-1.52144)
(-1.51034)
(-1.50832)
(1.42044)
(1.37977)
(1.40804)
(1.40060)
Observations
2,683
2,683
2,683
2,683
2,653
2,653
2,653
2,653
This table represents regressions 1, 2, 3 and 4 of the measurement description section in Chapter 4 respectively. Data are all BoE director transactions from June 1715 to September 1725. The
regressions are Ordinary Least Squares (OLS) regression’s with Newey-West consistent standard errors. In Panel A τ=5 and dependent variable is the future 5 day return and in Panel B τ=20
and the dependent variable is the future 20 day return. Past τ day return is added in all regressions to control for momentum behavior. The t-statistics are reported in parentheses below the
coefficients and ***, **, * indicates significance of the coefficient at a 1%, 5% or 10% level respectively. In regression 1, the independent variable is a dummy variable that equals one on days
where the net share purchase of BoE directors was positive, and zero if otherwise. Coefficient 𝛽𝐵𝑢𝑦 is the mean future τ day return of days on which BoE directors bought more shares then
they sold, and 𝛼 is the mean future τ day return of days on which BoE directors have not bought more shares then they sold. In regression 2 the independent variable is a dummy variable
which equals one on days where the net share purchase of BoE directors was negative, and zero if otherwise. Coefficient 𝛽𝑆𝑒𝑙𝑙 is the mean future τ day return of days on which BoE directors
sold more shares then they bought, and 𝛼 is the mean future τ day return of days on which BoE directors have not sold more shares then they bought. In regression 3 both buy and sell
dummies are regressed in a multivariate regression. In regression 4 the buy and sell trading volumes of BoE directors are regressed on future τ day returns. Coefficient 𝛽𝐵𝑉𝑜𝑙 is the average
effect in τ day future returns BoE directors experienced when changing their buy volume with thousand and 𝛽𝑆𝑉𝑜𝑙 is the average effect in τ day future returns BoE directors experienced when
changing their sell volume with thousand.
49
Table 2 Predictive Power BoE Director Trades 1720 South Sea Bubble, Tau = 5 and Tau = 20
Panel A τ=5
Panel B τ=20
Regressions
(1)
(2)
(3)
(4)
(1)
(2)
(3)
(4)
Future 5 Day
Future 5 Day
Future 5 Day
Future 5 Day
Future 20 Day
Future 20 Day
Future 20 Day
Future 20 Day
Variables
Return
Return
Return
Return
Return
Return
Return
Return
βBuy
0.02781**
0.02448**
0.08936**
0.08189**
(1.97494)
(1.99533)
(2.48134)
(2.41522)
βSell
-0.02076
-0.01577
-0.05196**
-0.03513*
(-1.22710)
(-1.01188)
(-1.98293)
(-1.67260)
βBVol
0.00715
0.02617**
(1.45513)
(2.28352)
βSVol
-0.00184
-0.00623
(-0.51240)
(-0.87435)
α
-0.00553
0.00284
-0.00217
-0.00316
-0.01759
0.00671
-0.01011
-0.01125
(-0.59773)
(0.40970)
(-0.30594)
(-0.37918)
(-0.60638)
(0.19994)
(-0.35320)
(-0.36330)
LN (Pt-τ/Pt)
-0.10967
-0.11576
-0.10823
-0.11129
0.22523
0.22733
0.22736
0.22692
(-1.40342)
(-1.39880)
(-1.37823)
(-1.37626)
(1.43803)
(1.37895)
(1.42249)
(1.42791)
Observation
266
266
266
266
266
266
266
266
s represents regressions 1, 2, 3 and 4 of the measurement description section in Chapter 4 respectively. Data are all BoE Director transactions from the 1720 South Sea Bubble. The
This table
regressions are Ordinary Least Squares (OLS) regression’s with Newey-West consistent standard errors. In Panel A τ=5 and dependent variable is the future 5 day return and in Panel B τ=20
and the dependent variable is the future 20 day return. Past τ day return is added in all regressions to control for momentum behavior. The t-statistics are reported in parentheses below the
coefficients and ***, **, * indicates significance of the coefficient at a 1%, 5% or 10% level respectively. In regression 1, the independent variable is a dummy variable that equals one on days
where the net share purchase of BoE directors was positive, and zero if otherwise. Coefficient 𝛽𝐵𝑢𝑦 is the mean future τ day return of days on which BoE directors bought more shares then
they sold, and 𝛼 is the mean future τ day return of days on which BoE directors have not bought more shares then they sold. In regression 2 the independent variable is a dummy variable
which equals one on days where the net share purchase of BoE directors was negative, and zero if otherwise. Coefficient 𝛽𝑆𝑒𝑙𝑙 is the mean future τ day return of days on which BoE directors
sold more shares then they bought, and 𝛼 is the mean future τ day return of days on which BoE directors have not sold more shares then they bought. In regression 3 both buy and sell
dummies are regressed in a multivariate regression. In regression 4 the buy and sell trading volumes of BoE directors are regressed on future τ day returns. Coefficient 𝛽𝐵𝑉𝑜𝑙 is the average
effect in τ day future returns BoE directors experienced when changing their buy volume with thousand and 𝛽𝑆𝑉𝑜𝑙 is the average effect in τ day future returns BoE directors experienced when
changing their sell volume with thousand.
50
Table 3 Predictive Power BoE Director Trades June 1715 - September 1725 Excluding 1720, , Tau = 5 and Tau = 20
Panel A τ=5
Panel B τ=20
Regressions
(1)
(2)
(3)
(4)
(1)
(2)
(3)
(4)
Future 5 Day
Future 5 Day
Future 5 Day
Future 5 Day
Future 20 Day
Future 20 Day
Future 20 Day
Future 20 Day
Variables
Return
Return
Return
Return
Return
Return
Return
Return
βBuy
0.00041
0.00040
-0.00114
-0.00102
(0.33368)
(0.32873)
(-0.34854)
(-0.31095)
βSell
-0.00010
-0.00007
0.00196
0.00188
(-0.10677)
(-0.07472)
(0.87620)
(0.84582)
βBVol
-0.00044
-0.00008
(-1.06397)
(-0.08102)
βSVol
0.00007
0.00034
(0.41387)
(1.14471)
α
0.00131***
0.00134***
0.00131**
0.00139***
0.00586***
0.00565***
0.00573***
0.00573***
(2.63642)
(2.63915)
(2.57784)
(2.82805)
(3.06320)
(2.87778)
(2.96230)
(2.95218)
LN (Pt-τ/Pt)
-0.03697
-0.03721
-0.03701
-0.03738
-0.08754
-0.08688
-0.08749
-0.08689
(-0.91060)
(-0.91298)
(-0.90789)
(-0.92316)
(-1.28693)
(-1.28402)
(-1.28584)
(-1.28398)
Observations
2,417
2,417
2,417
2,417
2,387
2,387
2,387
2,387
This table represents regressions 1, 2, 3 and 4 of the measurement description section in Chapter 4 respectively. Data are all BoE Director transactions from June 1715 - September 1725
Excluding 1720. The regressions are Ordinary Least Squares (OLS) regression’s with Newey-West consistent standard errors. In Panel A τ=5 and dependent variable is the future 5 day return
and in Panel B τ=20 and the dependent variable is the future 20 day return. Past τ day return is added in all regressions to control for momentum behavior. The t-statistics are reported in
parentheses below the coefficients and ***, **, * indicates significance of the coefficient at a 1%, 5% or 10% level respectively. In regression 1, the independent variable is a dummy variable
that equals one on days where the net share purchase of BoE directors was positive, and zero if otherwise. Coefficient 𝛽𝐵𝑢𝑦 is the mean future τ day return of days on which BoE directors
bought more shares then they sold, and 𝛼 is the mean future τ day return of days on which BoE directors have not bought more shares then they sold. In regression 2 the independent
variable is a dummy variable which equals one on days where the net share purchase of BoE directors was negative, and zero if otherwise. Coefficient 𝛽𝑆𝑒𝑙𝑙 is the mean future τ day return of
days on which BoE directors sold more shares then they bought, and 𝛼 is the mean future τ day return of days on which BoE directors have not sold more shares then they bought. In
regression 3 both buy and sell dummies are regressed in a multivariate regression. In regression 4 the buy and sell trading volumes of BoE directors are regressed on future τ day returns.
Coefficient 𝛽𝐵𝑉𝑜𝑙 is the average effect in τ day future returns BoE directors experienced when changing their buy volume with thousand and 𝛽𝑆𝑉𝑜𝑙 is the average effect in τ day future returns
BoE directors experienced when changing their sell volume with thousand.
51
Table 4 Predictive Power Outsider Trades June 1715 – September 1720, Tau = 5 and Tau = 20
Panel A τ=5
Panel A τ=20
Regressions
(5)
(6)
(5)
(6)
Variables
Future 5 Day Return
Future 5 Day Return
Future 20 Day Return
Future 20 Day Return
βBuy
0.00177
0.00541
(1.35934)
(1.34551)
-0.00164
-0.00571
βBuyInsider
(-0.97147)
βSell
βSellInsider
LN (Pt-τ/Pt)
Constant
(-1.18168)
-0.00191
-0.00569
(-1.51231)
(-1.38770)
0.00417
0.01419
(1.48783)
(1.29560)
-0.03472
-0.03407
0.26773
0.26759
(-0.35596)
(-0.34959)
(1.39748)
(1.40226)
0.00248**
0.00291**
0.00742**
0.00844**
(2.08044)
(2.48940)
(1.99807)
(2.20439)
1,396
1,396
1,366
1,366
Observations
This table represents regressions 5 and 6 of the measurement description section in Chapter 4. Data are the trades of outsiders
that have traded with BoE directors in the period of June 1715 – September 1725. The regressions are Ordinary Least Squares
(OLS) regression’s with Newey-West consistent standard errors. In Panel A, dependent variable in regression 5 and 6 is the
future 5 day return and in Panel B dependent variable in regression 5 and 6 is the future 20 day return. Past τ-day return is
added in both regressions to control for momentum behavior. The t-statistics are reported in parentheses below the coefficients
and ***, **, * indicates significance of the coefficient at a 1%, 5% or 10% level respectively. . In regression 5, the first
independent variable 𝐷𝐵𝑢𝑦,𝑡 is a dummy variable that equals one on days where the net share purchase of outsiders was
positive, and zero if otherwise. The second independent variable 𝐷𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟,𝑡 is a dummy variable, which equals one on days
where the net share purchase of outsiders transacting with insiders was positive, and zero if otherwise. 𝛽𝐵𝑢𝑦 is the average
future 10 day return outsiders experience on days their net share purchase was positive and 𝛽𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is the average extra
future 10 day costs/revenues outsiders incur when their net share purchase from BoE directors was positive. In regression 6,
the first independent variable 𝐷𝑆𝑒𝑙𝑙,𝑡 is a dummy variable, which equals one on days where the net share purchase of outsiders
was negative, and zero if otherwise. The second independent variable 𝐷𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟,𝑡 is a dummy variable, which equals one on
days where the net share purchase of outsiders transacting with insiders was negative, and zero if otherwise. 𝛽𝑆𝑒𝑙𝑙 is the average
future 10 day return outsiders experience on days their net share purchase was negative and 𝛽𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is the average extra
future 10 day costs/revenues outsiders incur when their net share purchase from BoE directors was negative.
52
Table 5 Predictive Power Outsider Trades 1720 South Sea Bubble, Tau = 5 and Tau = 20
Panel A τ=5
Panel B τ=20
Regressions
(4)
(5)
(4)
(5)
Variables
Future 5 Day Return
Future 5 Day Return
Future 20 Day Return
Future 20 Day Return
βBuy
0.00551
βBuyInsider
0.04911**
(0.53746)
(2.27848)
-0.00681
-0.07280**
(-0.76068)
βSell
βSellInsider
LN (Pt-τ/Pt)
Constant
(-2.47580)
-0.00815
-0.04206**
(-0.93203)
(-2.13051)
0.02332*
0.09964***
(1.89140)
(4.13519)
-0.08074
-0.07047
0.32147
0.30597
(-0.60554)
(-0.53066)
(1.35052)
(1.34849)
0.01431
0.01243
0.04258
0.04044
(1.20650)
(1.17113)
(1.08527)
(1.05723)
135
135
120
120
Observations
This table represents regressions 5 and 6 of the measurement description section in Chapter 4. Data are the trades of outsiders
in 1720 that have traded with BoE directors in the period of June 1715 – September 1725. The regressions are Ordinary Least
Squares (OLS) regression’s with Newey-West consistent standard errors. In Panel A, dependent variable in regression 5 and 6 is
the future 5 day return and in Panel B dependent variable in regression 5 and 6 is the future 20 day return. Past τ-day return is
added in both regressions to control for momentum behavior. The t-statistics are reported in parentheses below the coefficients
and ***, **, * indicates significance of the coefficient at a 1%, 5% or 10% level respectively. . In regression 5, the first
independent variable 𝐷𝐵𝑢𝑦,𝑡 is a dummy variable that equals one on days where the net share purchase of outsiders was
positive, and zero if otherwise. The second independent variable 𝐷𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟,𝑡 is a dummy variable, which equals one on days
where the net share purchase of outsiders transacting with insiders was positive, and zero if otherwise. 𝛽𝐵𝑢𝑦 is the average
future 10 day return outsiders experience on days their net share purchase was positive and 𝛽𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is the average extra
future 10 day costs/revenues outsiders incur when their net share purchase from BoE directors was positive. In regression 6,
the first independent variable 𝐷𝑆𝑒𝑙𝑙,𝑡 is a dummy variable, which equals one on days where the net share purchase of outsiders
was negative, and zero if otherwise. The second independent variable 𝐷𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟,𝑡 is a dummy variable, which equals one on
days where the net share purchase of outsiders transacting with insiders was negative, and zero if otherwise. 𝛽𝑆𝑒𝑙𝑙 is the average
future 10 day return outsiders experience on days their net share purchase was negative and 𝛽𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is the average extra
future 10 day costs/revenues outsiders incur when their net share purchase from BoE directors was negative.
53
Table 6 Predictive Power Outsider Trades June 1715 – March 1719, Tau = 5 and Tau = 20
Panel A τ=5
Panel B τ=20
Regressions
(4)
(5)
(4)
(5)
VARIABLES
Future 5 Day Return
Future 5 Day Return
Future 20 Day Return
Future 20 Day Return
βBuy
βBuyInsider
0.00033
0.00221
(0.43952)
(1.06607)
-0.00089
-0.00079
(-0.93515)
βSell
βSellInsider
(-0.27929)
-0.00045
-0.00289
(-0.53349)
(-1.56346)
-0.00038
-0.00086
(-0.29836)
0.00709
(-0.23788)
LN (Pt-τ/Pt)
0.00759
-0.09807
-0.09976
(0.20904)
(0.19537)
(-1.01494)
(-1.04274)
Constant
0.00144**
0.00162**
0.00768**
0.00927***
(2.02534)
(2.56932)
(2.11401)
(2.73939)
Observations
1,257
1,257
1,257
1,257
This table represents regressions 5 and 6 of the measurement description section in Chapter 4. Data are the trades of outsiders
between June 1715 and March 1719 that have traded with BoE directors in the period of June 1715 – September 1725. The
regressions are Ordinary Least Squares (OLS) regression’s with Newey-West consistent standard errors. In Panel A, dependent
variable in regression 5 and 6 is the future 5 day return and in Panel B dependent variable in regression 5 and 6 is the future 20
day return. Past τ-day return is added in both regressions to control for momentum behavior. The t-statistics are reported in
parentheses below the coefficients and ***, **, * indicates significance of the coefficient at a 1%, 5% or 10% level respectively.
. In regression 5, the first independent variable 𝐷𝐵𝑢𝑦,𝑡 is a dummy variable that equals one on days where the net share purchase
of outsiders was positive, and zero if otherwise. The second independent variable 𝐷𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟,𝑡 is a dummy variable, which
equals one on days where the net share purchase of outsiders transacting with insiders was positive, and zero if otherwise. 𝛽𝐵𝑢𝑦
is the average future 10 day return outsiders experience on days their net share purchase was positive and 𝛽𝐵𝑢𝑦𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is the
average extra future 10 day costs/revenues outsiders incur when their net share purchase from BoE directors was positive. In
regression 6, the first independent variable 𝐷𝑆𝑒𝑙𝑙,𝑡 is a dummy variable, which equals one on days where the net share purchase
of outsiders was negative, and zero if otherwise. The second independent variable 𝐷𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟,𝑡 is a dummy variable, which
equals one on days where the net share purchase of outsiders transacting with insiders was negative, and zero if otherwise.
𝛽𝑆𝑒𝑙𝑙 is the average future 10 day return outsiders experience on days their net share purchase was negative and 𝛽𝑆𝑒𝑙𝑙𝐼𝑛𝑠𝑖𝑑𝑒𝑟 is
the average extra future 10 day costs/revenues outsiders incur when their net share purchase from BoE directors was negative.
54