The boats that did not sail: asset price volatility in a
natural experiment∗
Peter Koudijs†
August 16, 2014
Abstract
What explains short term fluctuations of stock prices? This paper exploits a natural
experiment from the 18th century in which information flows were regularly interrupted
for exogenous reasons. English shares were traded on the Amsterdam exchange and news
came in on sailing boats that were often delayed because of adverse weather conditions.
The paper documents that prices responded strongly to boat arrivals, but that there was
considerable volatility in the absence of news. Results suggest that this was largely the
result of the revelation of (long-lived) private information and the (transitory) impact of
uninformed liquidity trades on intermediaries’ risk premia.
JEL Codes: G14, N2
Keywords: market efficiency, asset price volatility, financial history
∗
This paper is based on the first chapter of my dissertation. I thank Hans-Joachim Voth (advisor) and Fernando Broner and Jaume Ventura (committee members) for their advice and support. I thank Karine van der
Beek, Harry Garretsen, Will Goetzmann, Benjamin Golez, Jakub Jurek, Floor Koudijs, Hanno Lustig, Larry
Neal, Andrej Ukhov, Nico Voigtländer, Jan de Vries, Jan Luiten van Zanden, two anonymous referees, and
Bruno Biais for their detailed comments and suggestions. Many thanks to seminar participants at Cambridge
(EurHiStock), Chicago-Booth, Columbia, Evanston (EHA meetings), Geneva (EHES meetings), Northwestern, Nottingham (EHS meetings), NYU-Stern, Princeton, Stanford, Tilburg, UC Berkeley, UC Davis, UCLAAnderson, UPenn-Wharton, UPF-CREI, Utrecht, and Warwick for their feedback. Many thanks to Daphne
Acoca, Sid Basu, Michelle Lee, Sophia Paliza, Hannah Rusk, Anne Tran, and Tony Wang for excellent research
assistance. Any errors are my own.
†
Stanford Graduate School of Business - [email protected]
What drives the short run volatility of stock returns? Prices move significantly on a day
to day basis and it is often unclear why. An extensive literature, going back to Schwert
(1981, 1989), Roll (1984a, 1988), French and Roll (1986) and Cutler et al. (1989), broadly
distinguishes between two possible explanations: (1) the arrival of public news and (2) the
impact of the trading process, which can capture privately informed trading, liquidity trades
or shifts in investor sentiment.
It is not straightforward to distinguish between these two factors. Trading and the arrival
of news usually go together. Furthermore, it is difficult to measure the flow and impact of
information: news arrives continuously to the market and (modern textual analysis notwithstanding) it is often unclear what news items actually convey. This makes it challenging to
identify what price movements are truly unrelated to the arrival of new information, let alone
to determine what such price changes reflect.
The purpose of this paper is to shed more light on the sources of short term asset price
movements. Specifically, it uses a natural experiment where information flows are easily observable and where, for exogenous reasons, the market is starved from news for days in a row.
The paper aims to (1) identify how much markets move in the presence and absence of news
and (2) to explain asset price movements that are unrelated to the arrival of new information.
The natural experiment is provided by the Amsterdam market for English securities in
the 18th century. Back then, a number of English securities were traded on the Amsterdam
exchange. While the main market for these stocks was in London, Dutch investors held between
20 and 30% of English securities (Bowen 1989; Wright 1999). This gave rise to active trade
in Amsterdam. Importantly, for the specific periods under study (1771-1777 and 1783-1787)
virtually all relevant information about these securities originated in the British capital. This
information was transmitted to Amsterdam through official mail packet boats that sailed twice
a week (Figure 1 provides a map of the region). These were sailing ships, and due to adverse
weather conditions (especially Eastern winds) they were often delayed, leading to exogenous
variation in the flow of information and frequent news blackouts in Amsterdam that could last
2
for multiple days. This makes it possible to study stock returns in the absence of news. At
the same time, financial markets had long been established and functioned in a way similar to
today.
The paper first documents that the Amsterdam market reacted to the arrival of boats from
London as one would expect. Amsterdam prices strongly responded to the news brought in
and the variance of stock returns was significantly higher when boats from England arrived.
At the same time, volatility in the absence of boats was significant: the ratio of the “no-boat”
vs “boat” return variance was between 0.40 and 0.58.
The paper then attempts to decompose these asset price movements into their constituent
parts. There are two factors that can explain almost all of the volatility that is unrelated to
the arrival of news. The first is private information. English packet boats did not only bring in
public information, they also brought in trading orders from England that could reflect private
signals. The evidence suggests that these trading orders were spread out over time and can
account for a large fraction of the return variance on both days with and without the arrival
of packet boats. Second are liquidity shocks. The evidence suggests that, just as today, the
liquidity in the market was finite and that uniformed trades had (transitory) price impact. For
most securities, this explains an important fraction of returns.
Figure 2 illustrates the approach of the paper with two concrete examples. Panel (A) illustrates how public information was transmitted. On November 18, 1783 English prime-minister
Fox declared that the British East India Company’s (EIC) finances were in a “deplorable state”
(London Chronicle, Nov. 18, 1783; Sutherland 1952, p. 375). The company’s stock price in
London fell dramatically from 136 to 120. The vertical/diagonal black lines indicate how and
when the packet boats transmitted this news to Amsterdam. In the days following the drop
on the English exchange, weather conditions were unfavorable and the packet boat could not
cross the North Sea. Meanwhile, the EIC’s stock price in Amsterdam did not yet reflect the
news from England. Apparently, no other boats managed to get this major news across. After
wind conditions changed, a packet boat reached the Dutch Republic on November 28, ten days
3
after Fox gave his speech. Prices in Amsterdam fell sharply right after the boat arrival.
Panel (B) illustrates the importance of private signals and factors unrelated to new information. During the winter of 1775-1776 weather conditions were particularly unfavorable and
news from England reached Amsterdam with large intervals. This was in the middle of the
American Revolution and on December 27, an envoy arrived in London with official dispatches
from the Revolutionaries. On January 2, the Dutch newspapers reported that the content
of these messages were kept secret. In the subsequent two weeks there was a news blackout
in Amsterdam due to especially strong Eastern winds.1 When, on January 15, the English
news (dated January 4) finally arrived, there was not much to report except for rumors: the
contents of the American dispatches still had not been disclosed. At the same time, the EIC
stock price in London had increased from 165.75 to 167.75. Interestingly, the Amsterdam price
had gone up as well during the blackout. Later, it turned out that the American dispatches
contained a serious peace proposal - potentially good news for the EIC. It is possible that
privately informed agents traded on this information in both markets, causing prices to go
up. Importantly, the Figure suggests that this private information did not get incorporated in
prices right away.
After January 4, prices in London fell in response to the news that the English court was
not prepared to accept the American terms for peace. On January 11, for no apparent reason,
the London EIC price rose from 166.5 to 168.5. When Amsterdam learned of this on January
23, the market only partially responded. In the subsequent blackout period that lasted more
than two weeks, the Amsterdam EIC price fell from 167.25 to 166.75, only to recover to its
previous level after the arrival on English news on February 7. The causes for these price
movements were probably unrelated to information and may have reflected liquidity shocks or
changes in sentiment.
These examples illustrate that public news and private information played an important
1
To illustrate this: an English freighter ship that tried to reach the Dutch shore on January 6 was blown
back all the way to Eastbourne on the English Channel over a distance of 182 miles. (Rotterdamsche Courant,
Feb 4 1776)
4
role. To quantify the impact of information on overall volatility, I apply the following approach.
The contribution of public news is identified by the covariance between London returns before
the departure of a boat and price changes in Amsterdam after its arrival. Estimates indicate
that public news explains more than 50% of the return variance on days ships sailed in and
accounts for around 40% of the overall return variance. Empirical results confirm that boats
conveyed important news and that prices in Amsterdam did not respond to transitory (liquidity) shocks in Amsterdam. Results also suggest that the Amsterdam price responded to public
news without delay.
Private information is identified by the covariance of Amsterdam returns with contemporaneous (but as of yet unreported) price changes in London after the departure of a boat.
Similar to the example in Figure 2 , panel (B) the idea is that private information caused
Amsterdam and London prices to (slowly) move in the same direction, even when there was
no direct information sharing. Estimates indicate that private information explains about 25
and 35% of volatility on days with and without boats respectively. Additional tests suggest
that it is unlikely that this co-movement is driven by (a) news arriving through other channels,
(b) the arrival of information from a third source like Paris, the US or the East Indies, or (c)
correlated liquidity shocks.
Finally, I use the covariance of returns within Amsterdam to identify the (transitory)
impact of liquidity shocks. Results suggest that these can explain up to 30% of the overall
return variance for most securities. Together with the impact of public news and private
information, this almost fully explains the securities’ price movements.2 The exception is the
British East India Company for which liquidity seems to have been particularly good. A
large fraction of returns remains unexplained for this security, specifically for days without
the arrival of boats. I hypothesize that this is an indirect result of the presence of private
information. Due to the arrival of private signals from London, adverse selection costs may
have been particularly high after the arrival of boats, leading uninformed agents to concentrate
2
Empirical tests suggest that general economic developments in the Netherlands or fluctuations in the Dutch
short term interest rate did not have an important effect on English security returns.
5
their liquidity trades in periods without any boat arrivals (Admati and Pfleiderer 1988). It
is impossible to differentiate this from other explanations that emphasize the importance of
sentiment shocks or the divergence of beliefs during periods without recent information from
England.3 Table 1 provides a brief overview of the different factors affecting security returns.
[Table 1 about here]
This paper is related to an extensive body of literature, first of all to contributions quantifying the impact of news on stock price volatility (Berry and Howe 1994; Mitchell and Mulherin
1994; Andersen et al. 2000; Kalev et al. 2004).4 These studies generally find a weak relation
between the arrival of news and stock market volatility. Recent contributions use textual analysis to identify the impact of different types of news (Antweiler and Frank 2004; Demers and
Vega 2006; Tetlock 2007; Tetlock et al. 2008; Griffin et al 2010.; Davis et al. 2012; Boudoukh
et al. 2013). This improves the explanatory power of news. Related contributions analyze
weather-sensitive assets for which it is easier to measure information flows and to identify
causality (Roll 1984a; Fleming, Kirby and Ostdiek 2006; Boudoukh et al. 2007). These studies
find an important role for public information. Nevertheless, an important fraction of volatility
remains unexplained, especially “out of season”. Related to this literature is Yermack (2012)
who shows that stocks are less volatile when CEOs are on vacation and news announcements
are delayed.
A second strand in the literature looks at periods during which information arrives as usual
but markets are closed (French and Roll 1986; Barclay et al. 1990; Ito and Lin 1992; Ito et
al. 1998). These studies find that return variance is surprisingly low when trade is restricted.
This suggests that the arrival of news itself is relatively unimportant and that trading activity
(capturing either privately informed trading, liquidity trades or something else) explains the
largest fraction of the return variance.
Third, this paper is related to studies on cross-listings, specifically to contributions that
3
See Baker and Wurgler (2006) and Tetlock (2007) for evidence on sentiment.
Ederington and Lee 1993; Fleming and Remolona 1999; Bollerslev et al. 2000; Balduzzi et al. 2001; and
DeGennaro and Schrieves 1997; Andersen and Bollerslev 1998; Melvin and Yin 2000; Evans and Lyons 2008;
Hashimoto and Ito 2010 focus on the link between news and interest rates and exchange rates respectively.
4
6
look at how the price of foreign securities are affected when their home markets are open or
closed. Security prices are more volatile when the home market is open. Nevertheless, a large
share of price discovery takes place outside these hours (Harvey and Huang 1991; Forster and
George 1995; Werner and Kleidon 1996). This could either point to the minor importance of
news events in the home market, or it might suggest that news originating in the host market
is especially important.
Finally, this paper is related to other papers that use historical settings to study asset
price volatility (Elmendorf et al. 1996; Silber 2001; Brown et al. 2008), in particular studies
of the 18th century securities markets in Amsterdam and London.5 In seminal work, Neal
(1987, 1990) documents that the Amsterdam and London markets were well integrated. Neal
collected daily data for London, but relies on Amsterdam price data with a frequency of two
observations a month (originally collected by Van Dillen 1931). Dempster et al. (2000) use
Neal’s dataset to provide more evidence about the integration of the Amsterdam and London
capital markets. Harrison (1998) studies the properties of returns in London and Amsterdam
(again using Neal’s data) and finds that both he shape of the return distributions and the
autocorrelation patterns are very similar to those of modern markets. Finally, Koudijs (2013)
uses the same historical setting to test the Kyle (1985) model of strategic insider trading,
finding strong evidence in favor of this model.
Relative to this literature, the paper makes the following contributions. The paper focuses
on easily observable and exogenous interruptions to news arrival. This allows for a clean
identification of the impact of different types of information on asset prices. Moreover, it
permits a careful study of volatility in the (virtual) absence of new information. Relative to the
existing literature on financial markets in the 18th century, this paper is the first to (1) employ
data on the sailing of packet boats to measure information flows, and (2) to use Amsterdam
price data at a relatively high frequency (i.e. three observations a week). Furthermore, the
paper tries to go beyond studying market integration and the general characteristics of the
5
This paper is also related to the history of stock market integration: Garbade and Silber (1978); Hoag
(2006); Sylla et al. (2006).
7
return process and aims to identify the deeper sources of asset price volatility.
The paper proceeds as follows. Section 1 discusses the historical background of the paper,
presents summary statistics, and provides evidence about the direction of news flows. Section
2 presents baseline estimates of the return variance in periods with and without the arrival of
a boat. This section also investigates whether packet boats actually conveyed important news
and did not just transmit liquidity shocks. It also studies whether the arrival of packet boats
might have been endogenous to economic and political events. Section 3 discusses potential
explanations for volatility in the absence of news; most importantly private information and
liquidity shocks. The section also provides a simple framework to quantify the impact of these
different components. Section 4 concludes. Additional information and Tables and Figures are
presented in (Web) Appendices A through F.
1
Data and historical background
Before going into the details of communication flows during the 18th century, it is useful to
first discuss the Amsterdam market itself. How developed was this market and to what extent
can it serve as a useful analogue for markets today? What assets were traded and what price
data is available?
1.1
Market structure
Amsterdam and London were the most important securities markets of the period. The Dutch
started to invest in English securities in the early 1700s (Wilson 1941). It provided a good
opportunity to diversify domestic portfolios at a relatively low cost.6 Unlike today’s Anglodutch
“Siamese twins”, the English securities traded on both sides of the North Sea were identical.
The historical record indicates that there was an active trade in English securities on the
Amsterdam exchange (Van Dillen 1931; Van Nierop 1931; Wilson 1941). Although volume
6
Wilson (1941, appendices B-D) provides the identities of some of these investors. Most of these individuals
also owned stock in the most important Dutch stock of the period: the Dutch East India Company (Dutch
National Archives 1.04.02: 7075-7078).
8
data are unavailable for the period, some inferences can be made about the size of the market
for English stocks in Amsterdam. Bowen (1989) and Wright (1999) show that during the 1770s
around one third of the shares in the British companies were in the hands of Dutch investors.
During the 1780s this fell to around a fifth.
The Amsterdam market was organized in a way similar to today. Trade took place in
a centralized location. Around noon there were two official trading hours in front of the
Exchange building (Spooner 1983; Hoes 1986).7 There were no official market makers. Trade
was based on limit orders; a group of approximately 40 official brokers (and an unknown
number of unofficial brokers) convened to execute as many of their principals’ orders as possible.
Regulation proscribed that they were not allowed to trade for their own account (Smith 1919;
Van Nierop 1931). At the end of the afternoon, a committee of so-called sworn brokers reported
the going price of the day (Smith 1919, p. 109). Investors used this information to monitor
their brokers ex post (Polak 1924; Hoes 1986)
Apart from spot trade, a large fraction of the trading activity was related to futures and
options. Derivatives were introduced in the 17th century and their use and trade was fully
institutionalized (Petram 2011). Futures were actively used for shorting (Amsterdam City
Archives 735; 1510). Every three months, future and option positions were cleared during
periods of “rescontre” (Van Dillen 1931). There were no formal margin requirements and the
market was mainly based on reputation.
Similar to today, there was a developed market for margin loans. This system had been
developed in the early 17th century (Gelderblom and Jonker 2004) and was actively used in
the 18th century to fund (spot) positions in the English securities (Smith 1919; Wilson 1941).
These margin loans usually ran for 6 months and had margin requirements. The legal terms
of the contracts are broadly similar to today (Koudijs and Voth 2014).
The historical evidence suggests that the sophistication of market participants was considerable. Temin and Voth (2003) document investor strategies during the South Sea Bubble of
7
Historical evidence suggests that trade continued outside these official hours in coffee shops and in front of
Jewish synagogue (Van Nierop 1931).
9
1720 that were very similar to those observed during the recent Tech Bubble. Frehen et al.
(2012) suggest that during 1720 market participants were perfectly able to differentiate between systematic and idiosyncratic risk. In other dimensions 18th century markets look similar
to today as well (Harrison 1998).
1.2
Data, sample periods, sources, and descriptive statistics
I examine all English securities that were cross-listed in London and Amsterdam and for which
frequent price data is available for the Dutch market. This includes three stocks and two
government debt issues: the British East India Company (EIC), the Bank of England (BoE),
and the South Sea Company (SSC) and the 3 and 4% Annuities. The three companies were
involved in a number of activities. The EIC was a trading company that held large possessions
in India. The company’s prospects were determined by conditions in the East and political
developments in England (Sutherland 1952). Both the BoE, which wasn’t a central bank yet,
and the SSC operated to help finance the British government debt.8 The BoE also provided
large scale credit to the EIC and discounted commercial bills (Clapham 1944).
The analysis is focused on two periods: September 1771 — December 1777 and September
1783 — March 1787. The two periods are the intersection of two sets: (1) periods for which all
the relevant information is available in the historical sources, and (2) periods of peace on the
European continent and the absence of severe financial crises. This final consideration ensures
that important information about the English stocks originated in England; an important
condition for the clean identification of the arrival of news. The starting point of the first
period, September 1771, is determined by data limitations. The period stops in December
1777, a few months before tensions between England and France increased (Amsterdamsche
Courant, February 14, 1778), leading to a declaration of war in March 1778. In 1780 war also
broke out between the Dutch Republic and England.9 The second sample period starts in
8
The SSC originally had the purpose to transport slaves from Africa to the Spanish American colonies.
These activities never really materialized and the company functioned predominantly as an investment vehicle
in British government debt (Neal 1990).
9
There were two minor financial crises in the period. In June 1772 the default of London speculator
10
September 1783, right after the signing of an official peace treaty between France and England
and a preliminary peace treaty between England and the Dutch Republic. The second sample
period stops in March 1787. Domestic tensions in the Netherlands increased in April 1787 (on
April 24 there was a dramatic change in city government in Amsterdam), eventually leading
to domestic conflict in May 1787 and an intervention by the Prussian army in September 1787.
Amsterdam price data was hand-collected for the full period between September 1771 and
March 1787 and comes from the Amsterdamsche Courant (where necessary supplemented by
information from the Opregte Haerlemsche Courant). For each stock, there are three prices
a week available (for Monday, Wednesday and Friday). For London, frequent price data is
only available for the EIC, BoE and the 3% Annuities. Price observations are daily and come
from Neal (1990), where necessary supplemented with data from Rogers (1902).10 In both
Amsterdam and London, prices are reported in Pound Sterling (normalized as a percentage
of the nominal or par value). Returns are adjusted for dividend or coupon payments. Dividends comes from Clapham (1944) and Bowen (2007). Information about the exact dates the
securities went ex-dividend or ex-coupon come from Rogers (1903) and the Amsterdamsche
Courant.
London prices refer to spot transactions. By the second half of the 18th century, a significant
fraction of trade in the English stocks in Amsterdam was concentrated in the futures market
(Van Dillen 1931). As a result, all available price data for the Amsterdam market refers to
futures prices. In order to facilitate comparison between London and Amsterdam, I converted
all Amsterdam future prices into spot prices, using a fixed cost-to-carry rate. See Appendix
A for details. A future contract had 4 possible expiration dates: February 15, May 15, August
15, or November 15. The newspaper reported prices for the future contract ending on the
nearest date. This means that the maturity of a future contract was never longer than 90
Alexander Fordyce led to some turbulence in financial markets in London and Amsterdam. Furthermore, after
Christmas 1772 a consortium of Dutch bankers who had been bulling the market for English stocks defaulted.
This led to a short lived financial crisis in Amsterdam (Wilson 1941). Unreported results indicate that dropping
these two episodes from the estimates does not change the results.
10
Neal (1990) uses data reported by Castaing’s Course of the Exchange. Rogers (1902) uses stock prices
reported by various British newspapers.
11
days. It is unlikely that fluctuations in the short term interest rate attributed significantly to
the volatility of returns. For example, an instantaneous increase in the cost-to-carry rate of
100 basis points would lead to a log-return of 0.05 (0.10)% on a future contract with a median
(maximum) maturity of 1.5 (3) months. This is at the 6th (21st) percentile of the distribution
of absolute EIC returns; meaning that 94 (79)% of returns were larger in size.
Data about boat arrivals in the Dutch Republic comes from the Rotterdamsche Courant
(see Appendix A for more details). The sailing schedule allowed for two boat crossings a
week. Arrival data in England was reconstructed based a series of English newspapers (General
Evening Post, Lloyd’s Evening Post, Lloyd’s Lists, London Chronicle, and Middlesex Journal).
Due to the time intensive nature of this part of the data collection process, I only collected
this information for the two sub-periods. Finally, the paper uses data on weather conditions
from the Zwanenburg observatory, a town close to Amsterdam. This data provides three
observations a day on the wind direction and other weather variables. This data comes from
the Royal Dutch Meteorological Institute (KNMI).
Table 2 provides a number of descriptive statistics. Panel (A) provides information about
the return data for the two sample periods. There are three prices a week available for the
Amsterdam market (Monday, Wednesday and Friday). I calculate percentage log returns11
over two (Fri-Wed and Wed-Mon) or three day periods (Mon-Fri), indexed by subscript :
∆
= 100 ∗ log ( −1 ).12 The Table shows that the variance of returns in the paper’s

two sample periods is considerably lower than during the full period between 1771 and 1787.
According to a Brown-Forsythe test, the difference between the two sample periods and the
years between 1777 and 1783 is highly statistically significant. This reflects the difference
between periods of war and peace. The exception is the EIC, for which the difference between
return variances is relatively small. For comparison, panel (B) provides information about the
11
The use of log rather than simple returns is motivated by the fact log returns have less kurtosis. This
facilitates the statistical analysis.
12
Note that the three day period includes the weekend. During the 18th century, trading continued during
the weekend. However, trade on Sunday was limited due to the absence of Christian traders. Likewise, Jewish
traders did not participate on Saturdays (Spooner 1983, p. 21). There is no evidence for more or less volatility
over the weekend (Table 23 in appendix F). The 3 day weekend returns are therefore not scaled down.
12
full period between 1723 and 1794 for which Amsterdam security prices are available roughly
every two weeks (Van Dillen 1931). The Table reports the variance of two week returns for
the two sample periods and the full period. Results suggest that the sample periods can be
seen as representative of the full 18th century.13 For the SSC and BoE the variance of returns
is roughly the same. For the EIC, the return variance is somewhat higher during the sample
periods.
Panel (C) zooms in on the boat arrival data. The data can be subdivided in periods between
the arrival of boats. During the two sample periods there were 681 such periods, more than
half of which were long enough that, in addition to a price observation reflecting the incoming
boat, there was also a price observation that did not reflect any English news. The median
length of a between boat period was 3 days, but the distribution has a fat right tail. The 95th
percentile is at 7 days, the maximum is at 21 days. The maximum number of price observations
per period is 8.
[Table 2 about here]
1.3
1.3.1
News flows
Official news flows
How exactly did news from England reach Amsterdam? Since 1660, England and the Dutch
Republic had been connected through a system of sailing ships between Harwich (England) and
Hellevoetsluys (Dutch Republic), an important harbor close to Rotterdam (Figure 1). These
packet boats were specifically designed for the route and ensured a swift and regular flow of
information between the two countries. Since Amsterdam did not have a direct connection with
the North Sea (boats had to sail past the island Texel), this was the fastest way information
from London could reach Amsterdam (Hemmeon 1912; Stitt Dibden 1965; Ten Brink 1969;
Rotterdam City Archives 2599). Appendix A has more details.
The packet boats were scheduled to leave Harwich on fixed days: Wednesday and Saturday.
13
Note that this period exclused the South Sea Bubble of 1720.
13
The median sailing time was 2 days (including the day of departure). It took additional time
to transport the news over land. Roads were particularly bad and rivers had to be crossed by
ferry. Even though the news was transported on horseback, transport from London to Harwich
and from Hellevoetsluys to Amsterdam added two days to the traveling time, making a total
of 4 days (including the day of departure).14
The sailing ships often encountered adverse winds and as a result news could be significantly
delayed for days, sometimes even weeks. Around a third of the crossings from England to
Holland were delayed. During such periods of bad weather no news could be transmitted
across the North Sea. The Tatler, an English newspaper of the time, described that there
could be a news blackout in London “when a West wind blows [...], keeping news on the other
side of the Channel” (quoted in Dale 2004, p. 17). The same was true for Amsterdam when
the wind was blowing from the East.
The packet boat system was the main channel through which English information reached
Amsterdam investors. The Dutch newspapers of the time all relied on the packet boat service
to get news from England. All articles with new information from London can be retraced to
the arrival of a specific packet boat. At times, during periods of particularly bad weather, the
English news would arrive in Amsterdam through the harbor of Ostend in today’s Belgium,
which had a regular packet boat service with Dover in England (Figure 1). With a total of
nine times this happened only infrequently during the sample periods. The Dutch newspapers
meticulously reported these episodes and I account for them in the empirical analysis.
The packet boat system was also the main channel for private letters. This is borne out
by the English correspondence from Hope & Co during the sample period. Hope was one of
the biggest Dutch banking houses of the period, had strong connections in England, and very
active in the stock market. All correspondence with their English counterparties took place
14
In London, letters, newspapers, etc. were collected by the end of the day on Tuesday or Friday (day of
departure: day 1). This was transported to Harwich in the early morning, from where a mail packet boat
would set sail in the afternoon (day 2). The boat would usually arrive in Hellevoetsluys on the next day (day
3). After the news had arrived it was quickly sent to Amsterdam where it usually arrived the day after (day
4).
14
through the packet boat system.15 This indicates that important individuals received (public)
news at the same time as the market as a whole. Even Nathan Rothschild, who famously
was the first in London to receive news about the outcome of the Battle of Waterloo in 1815,
only received this information a few hours before the general public (Dale 2004, p. 17).16
Appendices C.1 and C.2 presents empirical evidence confirming that news from England did
not slip through other channels such as privately chartered boats or carrier pigeons.
What was the precise content of the news transmitted from London to Amsterdam? Amsterdam investors would receive all public information available to English investors. Each
packet boat brought in papers and other newsletters with information about recent developments in London including the most recent security prices. Finally, the packet boats brought
in private letters from London correspondents.17
1.3.2
Direction of news
The identification strategy of this paper relies on the assumption that relevant information
about the English stocks originated in England. I picked two sample periods for which this is
likely the case, but in the end it is an empirical question whether this is a correct assumption.
I analyze the flow of information between London and Amsterdam in a simple econometric
framework that explicitly takes into account that communication lags varied and boats arrived
in a discrete way. Figure 3 illustrates this approach and defines the different returns. Log
returns in Amsterdam are calculated over 2 or 3 day periods that have sub-index . They
) or not (∆−
). There can be
either reflect the arrival of a packet boat (∆

+
multiple “no-boat” returns between the arrivals of two ships. This is indicated by . Table 2
15
Most English letters in the Hope archive mention both the date a letter was written in London and the date
it was received and opened in Amsterdam. There are 112 letters available that Hope received from London.
Of these 112, 99 were dated on mail days and were specifically written right before the next mail packet would
leave. For 83 out of these 112 letters, it is possible to identify on what day Hope received and opened these
letters. Out of these 83 letters, 73 were received on days the mail packet arrived in Amsterdam. Five letters
were opened one day late, after the news had arrived in the evening of the previous day. The final five letters
were for some reason only opened a number of days later (Hope & Co, Amsterdam City Archives 735: 78,79,
115 and 1510).
16
The famous story that he received this information through a carrier pigeon, a full day before anyone else,
is a myth (Ferguson 1998, p. 15).
17
Wilson (1941, pp. 74-75); Van Nierop (1931); Hope & Co, SAA 735: 78,79, 115 and 1510.
15
indicates that there are at most seven no-boat returns per period;  ∈ [1 7]. With the arrival
of a boat, market participants in Amsterdam observe the change in the London price since
the last boat departure. This captures (possibly among other things) all new information that
reached the London market during this between boat period. This price change is defined as
∆
−1 , where subindex  stands for a specific period between two boat departures. Similarly,
London returns ∆
are calculated over the same 2 or 3 day periods. Returns ∆


reflect the arrival of a boat from Amsterdam. The Amsterdam return transmitted by this boat
is defined as ∆
−1 .
[Figure 3 about here]
I estimate the following two regressions

∆
= 0 + 1 ∆

−1 + ∆−2 + 

∆
=  0 +  1 ∆

−1 + ∆−2 + 
where coefficients 1 and  1 capture the response of Amsterdam and London to price
changes in the other market between the departure of boats. In turn, these price changes also
respond to past returns from the other market. To avoid attenuation bias I include these past
returns in the regressions. With a slight abuse of notation these are denoted as ∆
−2 and
∆
−2 .
Estimates are presented in Table 3. Since detailed London price data is only available for
the EIC, BoE and the 3% Annuities, results are only available for these three securities. The
Table shows that London price changes had strong explanatory power for Amsterdam. There
is some effect in the other direction, but R-squares are virtually zero and coefficients are 7
to 8 times smaller and only borderline statistically significant (for the BoE the coefficient is
insignificant altogether). This confirms that, at least for the paper’s specific sample periods,
most relevant new information originated in Amsterdam.
[Table 3 about here]
16
I perform a similar analysis using a Vector Error Correction Model (VECM). The advantage
of a VECM is that it takes explicitly into account that Amsterdam and London prices were
co-integrated (Dempster et al. 2000). The disadvantage of this framework is that one cannot
account for the fact that lag lengths varied and that boats arrived in a discrete way. Details
about estimation and Impulse Response Functions are presented in Appendix D. Results are
virtually the same: innovations in the London price have strong predictive power for Amsterdam returns, but not the other way around. Since the VECM estimates do not rely on actual
boat arrivals, I can also perform this analysis for the period of international conflict between
January 1778 and August 1783. Not surprisingly, for this period there is strong evidence that
price changes in Amsterdam had an important impact on London. Results are summarized in
Table 4 in the form of Hasbrouck (1995) information shares. During the two sample periods
that were characterized by peace, between 92 and 99% of relevant information originated in
London. For the intermediate period of international conflict this drops to between 58 and 77
%. This demonstrates the importance of restricting the paper’s main analysis to the periods
1771-1777 and 1783-1787.
[Table 4 about here]
I also test whether returns on English securities in Amsterdam were related to fluctuations
in the price of Dutch securities. Changes in general economic conditions in the Netherlands,
such as movements in the interest rate, should have affected both Dutch and English securities.
Table 5 presents the results from the following regression
= 0 + 1 ∆  + 
∆−

where  is a vector containing % log returns on three Dutch securities: the VOC (Dutch
East India Company), WIC (Dutch West Indies Company) and bank-money. In Amsterdam
two different currencies circulated: cash and bank money which represented current accounts at
the Bank of Amsterdam (Quinn and Roberds 2007). Fluctuations in the exchange rate between
17
the two “types” of money might convey important information about aggregate risk aversion,
investment opportunities and the availability of liquidity.18 . The equation is estimated for days
without boat arrivals. Results in Table 5 show that there is virtually no correlation between
English and Dutch assets during days without news. Returns on VOC stock have some predictive power, but the coefficients are small and statistically insignificant. An exception is the
BoE for which the (economically small) coefficient is marginally statistically significant. Corresponding R-squares are close to zero. These results confirm that general economic conditions
in the Netherlands had a limited impact on the pricing of the English securities.
[Table 5 about here]
Appendix C.3 checks whether there is any evidence that relevant news could also originate
in a third place, like France, the US or the East Indies. Results suggest that news from Paris
played only a limited role and that relevant information from the US and the East Indies
reached London first, before news was transmitted to Amsterdam.
2
Results I: the impact of news
2.1
Baseline estimates
The results in Table 3 confirm that Amsterdam responded very strongly to returns in London
after the arrival of a boat. How big was the impact of these boat arrivals on the volatility of
English security returns in Amsterdam? As a first step, Table 24 in Appendix F documents
the largest 25 returns for each security and indicates whether a return took place after the
arrival of a boat or not. More than 80% of those returns reflect the arrival of a boat.19
Next I explore how important the arrival of boats was for security returns in general.
Figures 19 to 23 in Appendix F presents the kernel densities of these boat (solid line) and
18
I thank Will Goetzmann for making this suggestion.
This analysis follows Cutler et al. (1989) who find that many of the largest 50 daily price changes between
1946 and 1988 are difficult to attribute to specific news events. In a similar vein, Fair (2002) identifies 220
large intradaily price falls between 1982 and 1999; of those only 69 can be attributed to the arival of relevant
new information.
19
18
no-boat returns (dashed line) for EIC stock.20 It is clear that returns are more volatile after
the arrival of new information. The distribution of news returns has considerably more mass
in the tails.
[Figure 19 about here]
Table 6 presents the first four moments of the return distributions for periods with ( =
1) and without a boat arrival ( = 0). Most importantly, and consistent with Figure 19,
the variance of returns is higher after the arrival of a boat. This is consistently true for
all securities. In addition, the fraction of zero returns is significantly higher during no-boat
´
³
´
³
−

 ∆
) lies between
periods. The ratio of return variances ( ∆+
040 and 058 (depending on the stock) and is statistically significantly different from 1.21
Table 25 in Appendix F replicates these estimates using normal rather than log returns: 100 ∗
( − −1 ) −1 . Results are virtually identical. Average returns are considerably higher
during periods with a boat arrival, especially for the EIC. This is consistent with boat returns
being more volatile and thus more risky. The difference in returns is not statistically significant
though.
[Table 6 about here]
The volatility of returns in the absence of boats did not differ depending on whether a
no-boat period was expected or not. On average there were two boat crossings a week and
three price observations. This means that many no-boat periods were expected. Table 19 in
Appendix C (definition A) shows that volatility was no different if the absence of a boat was
unanticipated.22 Table 26 in Appendix F tests whether returns in the absence of boats were
more volatile if the most recent news from London was relatively old. This could potentially
reflect aversion to ambiguity. The Table shows that this is not the case. Average returns did
20
The Amsterdam EIC return in response to PM Fox’ speech on November 28, 1783 is omitted in all densities,
regression estimates and variance calculations. The corresponding return of -14% is 18 sd’s from the mean of
the EIC return distribution. If included, this datapoint would largely drive the variance results of the EIC.
These results would not be representative for the full sample.
21
Because the return series are non-normal (see the Kurtosis measures in Table 6), a standard F-test on the
equality of variances is not suitable. I follow Boos and Brownie (2004) and use a non-parametric test based on
mean absolute deviation from the median, in this case a Brown-Forsythe (B-F) test.
22
Unreported results indicate that there were no significant differences in average returns either.
19
not differ in any significant way either.
2.2
Boat arrivals and news
To what degree did the arrival of a packet boat imply the transmission of actual news? Following Cutler et al. (1989) and Fair (2002), Table 27 in Appendix F revisits the largest 25 returns
on EIC stock, 22 of which took place after the arrival of a packet boat. The Table documents
the specific content of news brought in from London. In 20 of the 22 cases the arrival of a boat
coincided with the transmission of relevant information, such as significant events in India, or
decisions in Parliament affecting the Company. Table 28 in Appendix F presents similar results
for the 3% Annuities (as a representative of the other four, government related, securities).
In this case a boat arrival implied the transmission of material information in 21 of the 22
cases, such as news about the National Debt, developments in the US revolutionary war, or
the situation in Ireland. For the largest returns (the most important drivers of the variance
estimates) there seems to be a close link between the arrival of a boat and the transmission of
relevant information.
Next, I look at the general relation between the volatility of returns and specific news
content. There were no formal disclosure requirements and it is impossible to isolate important
events such as earnings announcements. In addition, there was no press that specialized in
publicly traded companies. The only English security frequently mentioned in the Dutch
newspapers was the EIC. I test whether the Amsterdam stock price moved more when the
Company was mentioned in the press. In addition, I interact this with other news categories
that were potentially relevant for the EIC.
There are significant measurement issues with this approach. Most importantly, the quality
of the printed material is not good enough to apply machine reading techniques to make the
full text available. Fortunately, one Dutch newspaper, the Leydse Courant, capitalized certain
keywords in the years between 1771 and 1778 to signal the topics it covered and it is possible
to impute those by hand.
20
Results are in Table 7. Apart from direct references to the EIC, the Table focuses on three
categories of keywords that have direct relevance for the Company: (1) financial terms like
“dividends” or “capital”, (2) geographic locations or relevant persons in the East Indies, and
(3) references to the Cape (of Good Hope) Colony. Ships sailing in from India would dock at
the Cape to allow the crew to recover. There was a regular news service with England transmitting the most recent news brought in by these ships. To check whether the capitalization
of words does not reflect something else, the Table also includes a fourth category of words
that signal (stale) background information rather than breaking news (such as “declarations”
or “proclamations”). Table 29 in Appendix F provides more details about the different news
categories.
Table 7, column (1) confirms that a reference to the EIC is associated with higher return
volatility, especially if it is mentioned more than once.23 In the 28 instances where the EIC
was mentioned at least twice, the return variance was more than 3 times higher than the
benchmark. As a comparison; this is at the 75th percentile of news to no-news ratios estimated
for modern securities (Boudoukh et al. 2013). Financial terminology and references to the
Cape are associated with return variance that is about 2.5 times higher than the benchmark,
although only the former is statistically significant. There is no relevant association with the
mentioning of the East Indies. Words signaling background information are associated with
significantly lower volatility. It is likely that, due to space limitations, this information was
only published when there was no other news fit to print. The adjusted 2 of the regression
is 0.05. This is about as high as what Griffin et al. (2010) find for developed countries using
modern data.
In columns (2) to (6) I interact the different news components with the EIC news dummies to unpack the effects. Column (2) shows that if an article uses financial terminology and
contains more than one reference to the EIC, the return variance increases substantially. This
effect, though (borderline) statistically insignificant and based on a limited number of observa23
An, unreported, robustness check indicates that dropping all observations where the newspaper mentions
“stocks” does not change the results.
21
tions, almost fully explains the predictive power of the financial terminology dummy and one
third of the EIC (1) news dummy. Column (3) indicates that an interaction with the India
dummy is not associated with higher volatility. Column (4) shows that there is also a large
and statistically significant interaction effect between the EIC (1) and Cape dummies (again
based on a limited number of observations). This fully explains the predictive power of the
Cape dummy and about a third of the effect of the EIC (1) news dummy. Finally, Column
(5) shows that references to the EIC are not associated with higher volatility if they are accompanied by background stories. The specification in Column (6) includes all interaction terms
and has an adjusted 2 of 0.11 (0.08 if estimated on absolute rather than squared returns).
In sum, there is a statistically significant relation between the arrival of certain types of news
and EIC returns. Given the measurement issues involved, the adjusted 2 is surprisingly high.
[Table 7 about here]
2.3
Boat arrivals and liquidity shocks
These results confirm that the packet boats transmitted relevant information about the English
securities. It is of course possible that in addition to this news, the boats also transmitted
English trading orders that affected volatility in Amsterdam. More specifically, suppose that
a party in London wanted to sell (or buy) for uninformed liquidity reasons.24 Further suppose
that this trade had (transitory) price impact (Grossman and Miller 1988). Then, it is possible
that he executed part of his orders in Amsterdam or that this shock was transmitted to
Amsterdam through arbitrage.
I evaluate the impact of English liquidity shocks in two ways. I first test whether there is
any evidence for Dutch liquidity shocks affecting prices in England. The Dutch held between
20 and 30% of the English securities, and unless arbitrage worked in a dramatically different
way, Dutch liquidity shocks should have affected prices in England in a similar way. To test
this, Table 8 computes the variance of security returns in London on days with or without
24
The impact of informed trades is discussed in Section 3.1.
22
boat arrivals from Amsterdam.25 There is no significant difference. If anything, volatility is
lower on days with news from Amsterdam.
[Table 8 about here]
Second, it is well accepted that liquidity shocks are transitory and revert over time.26 If
liquidity shocks were transmitted from London to Amsterdam (having transitory price impact in both markets) we would expect that London returns predict subsequent reversals in
Amsterdam. This is captured by the following regression:

∆
+ = 0 + 1 ∆−1 + 

where ∆
is the
−1 is the London return observed in Amsterdam at time , and ∆+
subsequent Amsterdam return over period  , where  ranges from a period of the next 2-3
days to 4 weeks (estimates for longer horizons yield similar conclusions). This period excludes
the immediate response to news from London (see Figure 3). Estimates are in panels (2)
of Tables 9 - 11. Results are restricted to the EIC, BoE and the 3% Annuities, the only
securities for which frequent price data in London is available. None of the three stocks
exhibit any (negative) predictability across markets, independent of what time horizon is
used. This suggests that liquidity shocks were not transmitted from London to Amsterdam.
For comparison, panels (1) and (3) present estimates of predictability within the Amsterdam
and London markets. For the BoE and the 3% Annuities there is evidence for within-market
reversals up to one week. The size of the reversal estimates are surprisingly similar to modern
estimates (Tetlock 2010, table 1, reports a coefficient of around −010). There is no evidence
for the reversal of EIC returns.
[Tables 9-11 about here]
These findings seem inconsistent: if it was possible to arbitrage between markets, why did
liquidity shocks only have an impact in the home market? The answer lies in the fact that,
25
As before, frequent prices observations in London are only available for the EIC, BoE and 3% Annuities.
Duffie (2010) provides a useful overview. See also Hendershott and Seasholes (2007), Tetlock (2010), and
Nagel (2012).
26
23
due to communication delays, arbitrage was imperfect. It took at least 7 days (the duration of
a round trip) to complete an arbitrage operation. If liquidity shocks affecting one market were
expected to revert within this period (compare the results above), they should not have affected
the other market. Moreover, arbitrage was risky; prices could change significantly within a
week. To illustrate these points, Figure 24 in Appendix F presents the distributions of price
differences between London and Amsterdam for EIC stock before and after the arrival of a boat
from England (see Figures 25 and 26 in Appendix F for the BoE and the 3% Annuities). Table
30 in Appendix F presents the corresponding moments. Results indicate that price differences
narrowed after the arrival of a boat (more mass around zero) but remained significant. In
summary, although it is possible that English (uninformed) order flow occasionally affected
Amsterdam returns after the arrival of a boat, empirical results indicate that its overall impact
was small.
2.4
Transaction data
It is possible that Amsterdam investors traded significantly less on days without news. This
would imply that liquidity was significantly lower on days without news and that minor order
flow imbalances could have led to large price reactions. There is no volume data available for
this period to test this. However, there is detailed information available about a small sample
of transactions in the Amsterdam market, roughly equally divided between EIC, BoE and SSC
stock and 3% Annuities.27 Of the total of 32 transactions only 4 transactions, or 125%, took
place on days with news (9% when value weighted). The average transaction size of ∼ $1 500
translates into $150 000 in today’s money (Officer 2009). On average, news from London
reached Amsterdam two times a week. If market participants were indifferent between trading
on days with or without news, we would expect that on average 286% of transactions would
take place on days with news. This means that there seems to have been a tendency to trade
27
This is based on two different sources. The first contains trading records of Abraham Cohen de Lara, a
Portuguese-Jewish merchant based in Amsterdam (SAA 334, 722-723). The second has information about the
liquidation of securities that were pledged as collateral in margin loans (SAA 5075, 10603-10611).
24
on days without news. The difference between these percentages is significant with a p-value
of 0028 (based on a binomial distribution and a one-sided test).
2.5
Possible endogeneity news: IV analysis
It is possible that the flow of information from London to Amsterdam was endogenous to
(market) developments in London. For example, packet boats may have left earlier (or later)
than scheduled if there were important developments. Alternatively, boats could have sailed
faster after important events. There is no historical evidence that suggests that this happened.
First, the exact timing of boat departures from England was determined by a fixed sailing
schedule. During the sub-periods under study (1771-1771 and 1783-1787) the packet boats
never deviated from the official schedule. Second, the historical evidence suggests that sailing
times were determined by weather conditions alone (see Appendix B for more details).
To study the possible endogeneity of boat arrivals in further detail, I perform an IV analysis
using the official sailing schedule and weather conditions as instruments. These IV estimates
should correct for any endogeneity that might be present. More specifically, I estimate the
following IV regression.
¡  ¢2
= 0 + 1  + 
∆
(1)
 =  0  +  
The first variable in  measures how many days ago the next news packet expected to arrive
in Amsterdam was scheduled to depart from London:
 =  − 
where  is calendar time in Amsterdam, and  is the date news was scheduled
to depart from London.28 The longer a packet boat was at sea (a larger  ), the more probable
28
Variable  can be negative, in that case news hasn’t left London yet.
25
the arrival of news on day  . The marginal change in arrival probability is not necessarily
constant in  . For example, a longer time since departure may indicate that conditions at sea
are relatively bad so that the marginal increase in arrival probability falls with  . To account for
this I include  2 and  3 as well (higher order polynomials are insignificant, both economically
and statistically). The sailing schedule itself is fixed and  is therefore exogenous. The second
set of variables in  capture weather conditions. Appendix B discusses that sailing boats
cannot fully adjust their sails to adverse winds. If the sailing direction is in the so-called no-go
zone a boat needs to tack (constantly change direction in a zigzag fashion). This increase the
effective distance and slows down sailing. For every day I calculate what fraction of available
wind observations featured a no-go zone; ∈ {0 13  12  23  1}. I include this information as dummy
variables. I also include a dummy for temperatures less than 3 degrees Celcius29 . The first
stage has a good fit. The F-value lies around 59 and the adjusted 2 is 0.29 (see Appendix B
for more details).
Table 12 presents the corresponding IV results. The IV estimates are highly statistically
significant at the 1% level (for the BoE at the 5% level). The differences between the IV and
OLS estimates are small. For the BoE the OLS and IV coefficients are virtually the same; for
the other securities the IV estimates are somewhat higher. A Hausman test indicates that there
is no significant statistical difference between the two sets of coefficients. The p-values range
between 0.17 and 0.96. In sum, there is no evidence that the arrival of boats was endogenous
to stock market conditions or important economic or political events.
[Table 12 about here]
29
Hellevoetsluys was situated in the mouth of several rivers. Ice floating downstream could make it hard to
reach the harbor. A dummy for temperatures below 3 degrees has most explanatory power.
26
3
Results II: explaining volatility in the absence of boat
arrivals
This section discusses a number of possible explanations for the volatility of the English securities in Amsterdam in the absence of boat arrivals. The first two subsections deal with two
potential explanations, fluctuations in short term interest rates and delayed response to public
news, that appear to be less relevant. The section then turns to privately informed trading
and trading costs that do turn out be important.
3.1
Shocks to the Amsterdam short term interest rate
As noted in Section 1.2, Amsterdam prices reflected future transactions. I converted Amsterdam prices into spots using a fixed cost-to-carry rate (details are in Appendix A). It is possible
that short term fluctuations in the interest rate contributed to the variance of returns. The
maturity of future contracts was relatively short; it varied between 1 day and 3 months. The
simple calculation on p. 12 suggests that interest rates fluctuations at such short maturities
probably played a limited role. Table 13 provides an additional robustness test. If fluctuations
in the cost-to-carry rate are quantitatively important, we would expect that returns on futures
with a longer time to maturity (e.g. between 11 and 13 weeks) would be more volatile than
returns on futures with longer maturities (e.g. up to 3 weeks). More formally, the return on a
future with a maturity  is given by
+  ∆
∆ = ∆

where  and 
are log future and spot prices in Amsterdam. Under the assumption

and ∆ are independent,
that ∆

(∆ ) = (∆
) +  2 (∆ )

27
If interest rate fluctuations are quantitatively important, (∆ |11 ≤  ≤ 13)  ( | ≤
3). Table 13 shows that is not the case. If anything, the variance returns on futures with longer
maturities is lower. This suggests that fluctuations in interest rates had little impact on overall
volatility.
[Table 13 about here]
3.2
Slow response to public news
Amsterdam no-news returns might be explained by a slow response to information from London.30 I test for this by looking at the time series properties of returns in Amsterdam. A slow
response to news is usually associated to initial underreaction, followed by return continuation
or momentum (Hong and Stein 2001).
It is not straightforward to identify a slow response to news through return continuation.
In the presence of liquidity shocks (Section 2.3) returns will also have the tendency to revert.
Simple auto-correlation coefficients aggregate the two effects; potentially cancelling them out.
A solution to this problem is to differentiate between the continuation (or reversal) of past
London and Amsterdam returns, where the former approximates news shocks, and the latter
picks up liquidity shocks. More specifically, I run the following regressions. For all returns in
Amsterdam I determine whether a boat arrived from London during the previous period or
not (−1 = 1 0). For the cases where −1 = 1, I estimate the following equation

∆
= 0 + 1 ∆
+ 2 ∆
−1

−2 +  if −1 = 1
(2)
where ∆
can reflect the arrival of a boat or not, ∆
is the Amsterdam news return
−1


over period  − 1, and ∆
−2 (with a slight abuse of notation) is the London news return that
was observed in Amsterdam during the preceding period. Coefficient 1 measures reversals of
30
For evidence on the slow response to news see for example Huberman and Regev (2001), Chan (2003),
Cohen and Frazzini (2008), DellaVigna and Pollet (2009); Hirshleifer et al. (2009); Tetlock (2011), Carvalho
et al. (2011), Savor (2012) and Gilbert et al. (2012).
28
news returns in Amsterdam. A slow response to news from London implies that 2  0.31
Regression results are presented in Table 14. Because London price data only covers EIC
and BoE stock and the 3% Annuities, results are restricted to these three securities. There is
no evidence in favor of momentum; 2 is close to zero for all three securities. Consistent with
results in Tables 10 and 11, there is evidence for reversals for the BoE and the 3% Annuities.
[Table 14 about here]
3.3
Private information
In addition to public news, private information might have been important as well. Research on
current day markets indicates that informed trading plays an important role (e.g. Hasbrouck
1991; Easley et al. 1997). Anecdotal evidence suggests that the 18th century was no different.
English insiders used both the London and the Amsterdam market to benefit from their private
signals. In addition to public news, the packet boats transmitted private letters. London
insiders would communicate private information to their agents in Amsterdam and these agents
would trade on the information. The historical record is rife with examples of informed trading
(Sutherland 1952, 206-208, 228; Van Nierop 1931, p. 68; Koudijs 2013). It is likely that this
set into motion a process of price discovery that affected prices both after the arrival of a boat
and during subsequent periods without boats. For example, if insiders traded strategically, it
would have taken considerable time before prices reflected a certain piece of information (Kyle
1985; see Glosten and Milgrom 1985 for an alternative setup). This could explain some of the
return volatility in Amsterdam on the days no boats arrived from England.
We can test for this in the following way. Suppose that, after the arrival of private signals,
the insiders’ agents in Amsterdam slowly traded on their information. This would have affected
) and during subsequent no-boat periods
prices both after the arrival of a boat (∆

). At the same time, the private information also affected the contemporaneous
(∆−
+
31
There is one problem with the estimation: ∆
−2 is not a perfect measure of news because returns in
London presumably reflect local liquidity shocks as well. This means that equation (2) suffers from attenuation

bias. Specifically, 2 is biased downwards. Nevertheless, as long as ∆
−2 conveys some relevant information
(as suggested by Tables 27, 7, and 28) then the coefficient should still be positive.
29

(but as of yet unreported) price change in London (∆
); the private signal was slowly

incorporated into prices there as well (Figure 3). The co-movement between Amsterdam
returns after the arrival of a boat and London price changes after the departure of that
boat should therefore reflect the revelation of private information. It is important to note that
this measure of private information excludes the revelation of short lived private signals that
immediately affected London prices. From the perspective of the Amsterdam investor this is
part of the public news transmitted by packet boats.
Table 15 presents estimates of the following regression equations

∆
= 0 + 1 ∆
+ 2 ∆


−1 + 

∆−
=  0 +  1 ∆
+ 

+
for the three securities for which security prices in both Amsterdam and London are available.
), while
Columns (1) to (3) present the estimates for the Amsterdam boat returns (∆

). For all three
Columns (4) to (6) present the estimates for the no-boat returns (∆−
+
securities there is a significant correlation between the returns. Unreported estimates indicate
that the regression results are robust to the inclusion of past price changes in Amsterdam and


London (∆
−1 , ∆−1 and ∆−2 ).
It is unlikely that these results are driven by the (slow) execution of English liquidity trades
in the two markets. As before, it is natural to assume that liquidity trades have transitory
price impact. If the London agent, who receives the liquidity shock, splits his trades across
London and Amsterdam, we would expect to see reversals across markets. More specifically,
that reflect this liquidity shock should negatively predict future price
London returns ∆

changes in Amsterdam that capture the reversal of this shock. Tables 9 to 11 indicate that
there is no evidence for this.
[Table 15 about here]
These results support the idea that an important fraction of the return variance is driven
30
by private information. The presence of informed trading also affects the estimates of the
importance of public information. The remainder of this section sets out a simple framework
that can identify both the impact public and private information. The intuition behind this
simple variance decomposition can be formalized as follows. London price changes between
the departures of two boats can be written as
∆
=   +  + 

where   and  are public and private signals that are revealed in London over period .
Innovation   is unknown to everyone at the beginning of  and is revealed to the public during
the course of the period. Innovation  is is privately observed by an insider at the beginning
of period . For simplicity I assume that  is fully revealed to the public by the end of .32
Public and private signals   and  are assumed to be independent and iid. Innovation  is
a residual. Throughout I assume that all innovations are normally distributed.
Returns in Amsterdam after the arrival of a boat can be expressed as
∆
=e
  + 0  + 

and consist of three parts. Variable  is a noisy version of private information  . The insider’s
agent in Amsterdam trades on the private information he has received from London and, by
assumption, his trades are not fully informative. Instead, he generates a signal  ; without loss
of generality (   ) =  2 and  2   2 . The term 0  captures the response in Amsterdam
to this noisy revelation of private information. The Amsterdam news return also responds to
public information e
  . This consists of public signal  −1 and that part of private signal −1
that was not yet incorporated into Amsterdam prices during the previous period. Innovation
 is a residual.
32
This setting can accomodate private information that is longer lived (as long as innovations are iid). The
corresponding identification strategy is slightly more complicated and relies on the covariance of Amsterdam
returns with returns in London that take place further into the future. Unreported results indicate that these
future covariances are small and add little to the simple analysis presented here.
31
Amsterdam no-boat returns can be written as
∆−
=  + + +
+
(3)
for  ∈ [1 7]. First of all the insider’s agent keeps trading on the private signal. This trading
generates an additional signal + ; again without loss of generality (  + ) =  2 and
 2+   2 . For simplicity I assume that the different signals   +1  are iid.33 Finally, +
is a residual. It follows from the projection theorem that
 =
2
(  + )
= 2
(+ )
 +
(4)
for b ∈ [0 7]. Using this expression it is straightforward to show that
³
´
 ∆
=

´
³
−
=
 ∆+
2
|{z}
+
 (∆
∆

−1 )
  2
|{z}
0  2
|{z}
+  2
(5)
 (∆
∆
)


+  2+
(6)
 (∆−
∆
)

+
These equations imply that the covariances between Amsterdam and London identify the
importance of public and private information shocks for the overall return variance. Estimates
are in Table 17: public information explains between 47 and 56% of the variance of boat
returns. Private information accounts for between 15 and 39% of the variance of boat returns
and between 27 and 39% of the no-boat returns.
3.4
Trading costs
It is possible that the volatility of security returns in Amsterdam in the absence of news was
driven by trading costs. Specifically, if market makers face order-handling costs or have limited
risk bearing capacity, trades have an impact on prices (see O’Hara 1997 and Biais et al. 2005
33
This is the case in both the Kyle (1985) and Glosten and Milgrom (1985) models. In Appendix E I show
that the results go through for the more general case where (+−1  + ) 6= 0 for  ∈ [1 7].
32
for overviews). This could drive the return volatility in the absence of news.
3.4.1
Order-handling costs
I first consider order-handling costs. The Amsterdam market operated through limit orders
and the newspaper reported the going price of the day. In theory, the reported price should
not reflect these direct transaction costs. Nevertheless, it is possible that there were de facto
market makers who charged a bid-ask spread to cover order-handling costs. During certain
days the market might have cleared at either the bid or the ask. This would have led to a bidask bounce and this could have significantly contributed to the volatility of security returns.
To analyze this, I estimate a Roll (1984b) model. In this model price dynamics are described
by the following two equations
 = −1 + 
(7)
 =  + 
(8)
where  is the log fundamental value of the security,  is the log transaction price that is
observed. Parameter  is the direct trading cost, and  is the direction of the trade on which
the reported transaction price is based: -1 for sales and +1 for purchases. The fundamental
value of the asset is a random walk; innovations  are iid and normally distributed with zero
mean and variance  2 . The process for  is assumed independent of  . Crucially, trading
costs generate negative autocorrelation in the return series. This simple model implies that
∆ =  + ∆
so that
p
 = − (∆  ∆−1 )
The return variance that can be attributed to order-handling costs is given by (∆ ) = 22 .
33
Calculations are in Table 16 and are based on both boat and no-boat periods. The underlying assumption is that transaction costs do not depend whether a boat arrives or not.34
For comparison, the Table also present the official brokers’ fees; they lie very close to the Roll
measures. For the EIC the first order autocovariance is positive and the Roll measure can not
be calculated. I therefore estimated the Roll model using a Bayesian approach advocated by
Hasbrouck (2009) that can handle a positive autocovariance. This yields a Roll measure of
 = 0047 with an implied variance of 0004. I applied Hasbrouck’s approach to the other four
securities as well. Without exception, the Bayesian approach leads to lower Roll measures (and
lower implied variances) for these securities. For comparison, transaction costs on the NYSE
between 1993 and 2005 were approximately twice as large as the (raw) Roll measures in the
1770s and 1780s (Hasbrouck 2009, Table 2).35
[Table 16 about here]
3.4.2
Inventory costs
The Roll model abstracts away from other sources of transaction costs such as inventory costs
where intermediaries have to be compensated for taking a position in the asset (because of risk
aversion or inventory management). This could be highly relevant in the limit order setting
of the 18th century. If, for example, there are a lot of transactions on one side of the market,
trades will take place deeper in the limit order book and this affects all transaction prices
during the trading day. Consider the following stylized setup where there is a competitive
group of intermediaries that sets prices according to
 =  − 
(9)
where  is defined by equation (7) and  is the intermediaries’ total inventory in the asset. If
investors have predominantly sold the asset in the past,  will be positive and prices will be
34
35
Section 3.5 relaxes this assumption.
See Biais and Green (2007) for another example where historical trading costs are lower than modern ones.
34
below the fundamental value. This compensates the intermediaries for taking a long position
in the asset. Parameter  captures market makers’ risk bearing capacity. Returns are given
by
(10)
∆ =  + 
where  is either a buy or sell order. In practical terms, this setting differs from the Roll
(1984) model if it takes time for intermediaries to off-load their inventories. In that case, the
initial price impact of  will dissipate slowly and the autocovariance of returns will be negative
at longer lag lengths. Results in Tables 9 to 11 suggest that, in the data, there is evidence for
negative autocorrelations up to a period of a week.
To analyze the impact on returns, I simulate a simple inventory cost model. Following
Garman (1976) and Ho and Stoll (1981) I assume that orders arrive as follows
 =
where
½
1 with probability  
−1 with probability 1 −  
¾
½
∙
¸ ¾
1
  = min max
+ (−1 − −1 ) 1  0 
2
(11)
In other words, the probability of either a buy or sell order depends on the log-difference
between the asset’s fundamental value and the market price during the previous period. If
the asset is undervalued, it is more likely that a buying order will hit the market and, as a
result, prices will rise. This process generates negative autocorrelation in the order flow and
the return series: a buy order today will drive up prices and this will make a selling order
tomorrow more likely. Parameter  captures how fast the market responds to mispricing. A
low value implies a longer time before prices move back to fundamentals, leading to negative
auto-covariances at longer lag lengths. Figure 27, panel (a) in Appendix F illustrates this with
two examples where  = 12 and  ∈ {14 34}.
I simulate price paths of 10,000 observations for different values of  and  and compare the
35
implied autocovariances (∆  ∆− ) over different lag lengths with the actual ones in the
data, again using both boat and no-boat returns (the first 1,000 observations for each simulated
price series are dropped). Lag lengths can vary between 1 and 12 periods,  ∈ [1 12], where
each lag represents 2 or 3 days. The longest lag is equivalent to a period of 4 weeks. Table 16
presents, for each individual security, the values of  and  for which the mean square error is
minimized. Figure 27, panels (b) to (f) in Appendix F present the actual and model-simulated
autocovariances for these parameter estimates. For the EIC there is no evidence for a negative
autocovariance at the first two lags and  = 0 provides the best description of the data. For
the SSC, the autocovariance of returns is only negative at a lag length of 1, consistent with a
relatively large value for . For the BoE and the two Annuities autocovariances are negative
at the first three lag lengths, and  has a relatively small value.
Table 16 presents estimates of the return variance that this process generates. These
estimates, that are based on the simulated return series, are somewhat higher than those of
the Roll model. As a cross-validation I check these numbers against the (limited) transaction
data available. Of the 32 individual transactions that could be identified, 9 took place (a) in
the absence of boat arrivals, (b) on days for which prices were listed (Mondays, Wednesdays
and Fridays), and (c) for future contracts with the same maturity as those reported in the
newspaper. For these 9 transactions, the mean squared difference between the transaction and
reported price is 0.079. This is in the same ballpark as the inventory cost model’s implied
variance.
3.5
Summing up
Table 17 summarizes the contributions of the different components driving security returns.
On days with a boat arrival, between 47 and 54% of the return volatility can be attributed
to the arrival of public information. For the EIC, private information is of key importance as
well and explains about 39% of the return variance. For the BoE and the 3% Annuities this
number lies between 15 and 20%. For the EIC trading costs are estimated to be zero, while
36
they account for between 22 and 25% of the volatility of BoE and the 3% Annuities. For days
without a boat, private information accounts for between 27 and 39% of volatility. Trading
costs are again estimated to be zero for the EIC and account for between 38 and 54% of the
return variance for the other two securities. Overall, public news accounts for between 35 and
42%, private information for between 17 and 39%, and trading costs explain about between 0
and 32% of the total return variance. Taken together, these estimates explain between 82 and
92% of the overall volatility.
[Table 17 about here]
The exception is EIC returns on days without a boat; more than 60% of the return variance
is left unexplained. This is puzzling and to dig deeper into this issue, Table 18 presents more
detailed information about the EIC’s one period autocovariance. The Table presents the results
of the following regressions

= 0 + 1 ∆
∆

−1 + 2 ∆−1 ∗ −1 + 3 −1 + 
(12)



=  0 +  1 ∆
∆


−1 +  2 ∆−1 ∗ −1 +  3 ∆−1 ∗  +  4 ∆−1 ∗ −1 ∗ (13)
+ 5 −1 +  6  +  7 −1 ∗  + 
where  ∈ {0 1} indicates whether new information reached Amsterdam in period . These
regressions test whether return reversals differed if the previous and/or current period featured
the arrival of a boat. In order to facilitate interpretation, Table 18 sums up the different
interaction coefficients to calculate what the total return reversal is for each of the different
scenarios. Column (1) presents the estimates of equation (12), where boat and no-boat returns
are allowed to revert differently (−1 = 0 1). Columns (2) and (3) present the estimates of
equation (13), where reversals coefficients are also allowed to be different depending on whether
the current period features the arrival of a boat or not ( = 0 1).
Previous results (Table 9 and Figure 27, panel (b) in Appendix F) indicate that there is
no negative autocovariance for the EIC. Table 18 uncovers that there are reversals, but only
37
of no-boat returns (−1 = 0). Interestingly, these reversals only take place on news days
( = 1). For comparison, the Table also presents these estimates for the BoE and the 3%
Annuities. For the latter, return reversals are also especially important for no-boat returns,
although returns tend to revert equally strong independent on whether a boat just arrived or
not. For the BoE, no differences in reversals are visible.
[Table 18 about here]
There are two different interpretations for these results. The first is based on liquidity
trades and adverse selection costs. The analysis of Section 3.4 suggests that trading costs in
EIC stock were practically zero. These calculations yield incorrect results if liquidity trades
were mainly concentrated in periods without a boat arrival. It is possible that due to the
presence of privately informed agents, uninformed traders concentrated their trades in periods
without a boat arrival (Admati and Pfleiderer 1988). According to Table 17, 39% the EIC
return variance on news days was driven by private information. This is significantly more
than for the other securities. In addition, Table 15 suggests that privately informed trading
in EIC stock was approximately twice as important on boat than on no-boat days. This
would have led to additional adverse selection costs on days boats arrived, and uniformed
agents may have decided to cluster their trades in no-boat periods. The price impact of these
liquidity trades only reverted after the arrival of the next boat. It is possible that at that point
some uncertainty was revealed, decreasing the risk premium charged by intermediaries. This
interpretation is consistent with the limited trading data available. There are 11 transactions
in EIC stock, all of which took place on no-boat days. This is statistically different (with a
p-value of 0.025) from a random allocation of trades over days with or without a boat arrival
(based on a one-sided binomial test).
Alternatively, the excess volatility of EIC stock on no-boat days could have been the result
of sentiment shocks, rumors, or differences of opinion that were especially important during
periods between the arrival of boats. In this interpretation, beliefs converged again after the
arrival of news from England, leading to return reversals. Since the EIC was, by far, the
38
most volatile stock of the period, it is possible that sentiment shocks were most important
for this security (Baker and Wurgler 2006). It is impossible to differentiate between these
two interpretations. The transaction evidence is not conclusive, since differences of beliefs are
generally also associated with higher trading volume (Harris and Raviv 1993).
4
Conclusion
What are the roles of public news, private information, liquidity trades and sentiment shocks in
explaining the short term volatility of stock returns? It is challenging to answer this question
in today’s world of around-the-clock news coverage and instant, long-distance data exchange.
News arrives all the time and this makes it difficult to separate the impact of news arrival from
the trading process.
The key contribution of this paper is to offer a way around this problem. The paper looks at
the trade of English securities in 18th century Amsterdam. Communication between England
and the Dutch Republic relied on sailing boats. This has two distinct advantages. First, it
is possible to precisely reconstruct the arrival of news. This allows for a clean identification
of information flows and enables us to study stock price movements in the absence of news.
Second, interruptions in information flows were exogenous; sailing delays were the result of
adverse winds and other weather conditions. The arrival of news was unrelated to developments
in the stock market or general economic and political events.
At the same time, Amsterdam and London, the markets in question, were already quite
developed in the 18th century. They were the most important securities markets of the period
and can be seen as precursors to modern stock markets. They shared many of the important
features of today: a centralized exchange, trade in derivatives (with the ability to short) and
a well-developed system of margin loans to finance positions. In a quantitative sense 18th
century markets look similar to today as well. Harrison (1998) argues that the return process
has changed very little over time, leading him to conclude that 18th century markets were not
39
so different from the 1990s. The current paper’s estimates of trading costs and return reversals
are surprisingly similar to modern figures (compare Hasbrouck 2009 and Tetlock 2010). The
fact that communication technology itself was less developed back then is not directly relevant.
What matters is how markets respond to information, not how that information arrives to the
market.36
Results indicate that the arrival of public information was important; the variance of Amsterdam returns was significantly higher after the arrival of boats. Public news explains about
40% of the overall volatility in Amsterdam. At the same time, price movements in the absence
of boats were considerable. Estimates suggest that they can almost be fully explained by private information and liquidity shocks (in approximately the same proportions). The exception
is the EIC. Returns in the absence of boat arrivals remain largely unexplained for this security.
It is possible that, due to adverse selection costs, uninformed agents concentrated their liquidity
trades in periods between the arrivals of boats, leading to additional volatility. Alternatively,
investor sentiment may have shifted during periods when no new objective information was
available.
36
It is possible that the market’s response to information itself may have changed. More advanced information technology could facilitate more efficient information processing. It is not clear that this is the case.
Bai et al (2012) show that even after decades of fast technological progress, markets have not become more
informative. In addition, the results of this paper indicate that the Amsterdam market was not slow to respond
to information.
40
References
Historical newspapers:
Amsterdamsche Courant, City Archive Amsterdam
Opregte Haerlemsche Courant, Dutch Royal Library, The Hague
Rotterdamsche Courant, City Archive Rotterdam
General Evening Post; Lloyd’s Evening Post; Middlesex Journal; London Chronicle, Burney Collection, British Library
Lloyd’s Lists, Maritime Museum Rotterdam
Dutch National Archives (NA), City Archives Amsterdam (SAA) and Rotterdam (OSA):
Hope & Co, SAA 735: 894, 895, 1155, 1156 (ledgers) and 78, 79, 115 and 1510 (letter books)
Archive Notary Van den Brink (Amsterdam Notary Archives), SAA 5075: 10527, 10593 - 10613
Archive Cohen de Lara (Archive Portuguese-Israelite Community), SAA 334: 722-723, Oct 1771
- March 1776
Stukken betreffende het postverkeer met Engeland [Documents regarding the postal connection
with England], 1687, 1697-1710, OSA 2599
Grootboeken van de uitdelingen op aandelen van de kamer Amsterdam [Ledgers dividend payments VOC stock in Amsterdam], 1704 - 1787, NA 1.04.02: 7075-7078
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Figures and Tables
Figure 1: Map North Sea Area
46
Figure 2: Examples news arrival
47
Figure 3: Structure return data
48
Table 1: Summary of potential explanations for price movements in Amsterdam
Explanation:
Public news
Private information Liquidity trades
Sentiment
Mechanism:
Boat arrival /
Trading activity
announcements
Centered on London:
Boat returns:
Y
Y
N
N1
No-boat returns:
N
Y
N
N
Centered on Amsterdam:
Boat returns:
N2
N
Y (except EIC)
N
2
No-boat returns:
N
N
Y
?3
This table summarizes the potential factors influencing stock prices in Amsterdam during periods
with and without boat arrivals, making a distinction between explanations centered on London or
Amsterdam. “Y/N”: explanation relevant yes/no according to empirical evidence.
1
Under the assumption that London sentiment shocks revert within 4 weeks.
2
There is some evidence that Amsterdam returns affected London, but the impact is quantitatively
unimportant.
3
Amsterdam EIC returns in the absence of boat arrivals might be effected by sentiment shocks or
differences in beliefs.
49
Table 2: Descriptive statistics
Panel (A): Return information 1771 - 1787 (2/3 day returns)
EIC
SSC
Sep. 1771 - Dec. 1777 & Sep. 1783 - Mar. 1787 (sample periods)
Mean
0.056
0.031
Variance
0.528
0.264
N (total)
1162
1162
N (news returns)
681
681
N (no-news returns)
481
481
BoE
3% Ann.
4% Ann.
0.032
0.188
1162
681
481
0.040
0.323
1162
681
481
0.033
0.237
1162
681
481
0.034
0.519
1900
(65.3)***
0.044
0.396
1767
(79.4)***
Sep. 1771 - Mar. 1787 (sample periods & intermediate war period)
Mean
0.051
0.034
0.040
Variance
0.618
0.443
0.292
N (total)
1900
1900
1900
B-F statistic on equality return variance (12.4)*** (51.9)*** (52.8)***
Panel (B): Return information 1723 - 1794 (non-overlapping two week returns)
Sep. 1771 - Dec. 1777 & Sep. 1783 - Mar. 1787 (sample periods)
Mean
0.079
-0.003
0.019
Variance
5.173
1.920
2.448
N
133
133
133
Nov. 1723 - Dec. 1794 (full period for which data is available, with gaps)
Mean
0.037
0.047
0.007
Variance
3.876
2.236
2.534
N
957
957
957
B-F statistic on equality return variance (4.24)**
(0.039)
(0.046)
N/A
N/A
Panel (C): Between boat periods (Sep. 1771 - Dec. 1777 & Sep. 1783 - Mar. 1787)
Total number of between boat periods
681
Those involving a no-boat return
383
min
5th perc. median 95th perc.
max
Length between boat periods (days)
1
2
3
7
27
Number of price observations per period
0
1
2
3
8
Panel (A): descriptives of Amsterdam security returns for the paper’s two sample periods (1771-1777
and 1783-1787) and the full period between 1771 and 1787. Returns are calculated as ( −1 ) in %
for two or three day periods (see main text for details). Panel (B): descriptives of Amsterdam security
returns for non-overlapping two week periods, based on the data collected by Van Dillen (1931). A
Brown-Forsythe test statistic is reported on the equality of different return variances. Panel (C): descriptives of between boat periods for the two sample periods. Periods involving a no-boat return are
periods that are long enough so that there is at least one price change in the absence of news.
50
Table 3: Response to news, Amsterdam and London
AMS (response to LND)
LND (response to AMS)

∆
∆

EIC
BoE
3% Ann. EIC
BOE
3% Ann.
Observed LND return 0380
0425
0464
(∆
(0041)∗∗∗ (0058)∗∗∗ (0070)∗∗∗
−1 )
Past AMS return
(∆
−2 )
−0021
(0038)
−0004
(0003)
−0042
(0039)
Observed AMS return
(∆
−1 )
0056
(0033)∗
0053
(0038)
Past LND return
(∆
−2 )
−0026
(0035)
−0068 −0056
(0052) (0036)
Constant
0064
(0033)∗
0054
0018
0035
0006
0019
0016
(0015)
(0022)∗∗ (0032) (0019) (0017)
(0032)∗
N
594
602
621
636
629
756
2
021
026
023
000
000
001
Adj. R
This table presents estimates of the response of news returns in Amsterdam (AMS) and
= 0 + 1 ∆
London (LND) to returns observed in the other market: ∆

−1 +
 




=  0 +  1 ∆−1 +  2 ∆−2 +  . Returns ∆
and
2 ∆−2 +  and ∆
 
reflect the arrival of a boat from the other market and are calculated as % log
∆
returns over 2/3 day periods (Mon-Wed-Fri). Returns observed in the other market,



∆
−1 and ∆−1 are defined as price changes between the moments boats depart. ∆−2
and ∆
−2 are past London and Amsterdam returns. Robust, bootstrapped (1000 reps.)
standard errors reported in parantheses. Periods: Sept. 1771 - Dec. 1777 and Sept. 1783
- Mar. 1787. ***, **, * denote statistical significance at the 1, 5, 10% level respectively.
51
Table 4: Hasbrouck information shares
Panel (A): Sep. 1771 - Dec. 1777 & Sep. 1783 - Mar. 1787
Ordering
LND first
AMS first
Midpoint
EIC
0.998
0.867
0.933
BoE
0.992
0.854
0.923
3% Ann.
0.999
0.973
0.986
Obs.
1498
Panel (B): Jan. 1778 - Aug 1783
EIC
0.799
0.552
0.676
BoE
0.730
0.424
0.577
3% Ann.
0.864
0.647
0.756
Obs.
834
Hasbrouck information shares based on VECM models that use prices
in Amsterdam and London for three days a week (Monday, Wednesday,
and Friday). The VECM models are estimated using 5 lags; each lag represents 2 or 3 days. Reported information shares for different Cholesky
orderings (either London or Amsterdam first) and the simple mean of the
two. Panel (A): results for the two sample periods that are characterized
by peace. Panel (B): results for the intermediate period of international
conflict.
Table 5: Correlation between English and Dutch security returns in Amsterdam
∆−
+
EIC
SSC
BoE
3% Ann. 4% Ann
−
∆
VOC
0078
0061
0078
0034
0061
∗
(0039)
(0058) (0048) (0041) (0046)
WIC
0031
0001
−0005
(0023) (0021) (0018)
−0006
(0023)
−0005
(0024)
Bank money (agio) 0290
0171
−0021
(0333) (0266) (0224)
−0137
(0261)
−0071
(0301)
Constant
0013
0015
0003
0003
0014
(0025) (0021) (0017) (0021)
(0018)

426
426
426
426
426
2
001
001
002
000
001

Regressions of English security returns on Dutch security returns in Amsterdam. Returns
over periods without news from London only. VOC: Dutch East India Company, WIC:
Dutch West Indies Company, Bank money (agio): return on deposit money at Bank of
Amsterdam relative to cash. % log returns calculated over 2/3 day periods. Periods: Sep.
1771 - Dec. 1777 and Sep. 1783 - Mar. 1787. * denotes statistical significance at the
10% level.
52
Table 6: Benchmark results
∆

EIC
SSC
BoE
3% Ann
0084
0037
0043
0059
0018
0023
0017
0013
(1526)
(0456)
(1032)
(1350)
mean
boat ( = 1)
no-boat ( = 0)
(t statistic)
variance
0703
boat ( = 1)
0279
no-boat ( = 0)
(B-F statistic)
(423)∗∗∗
4% Ann
0040
0022
(0635)
0325
0179
(208)∗∗∗
0229
0131
(248)∗∗∗
0415
0193
(229)∗∗∗
0300
0150
(304)∗∗∗
skewness boat ( = 1)
no-boat ( = 0)
0189
0030
−0030
0649
0169
0523
0279
0134
−0504
1372
kurtosis
boat ( = 1)
no-boat ( = 0)
752
868
852
788
799
1095
1108
827
1039
1086
% zero
boat ( = 1)
no-boat ( = 0)
135
270
380
547
228
383
295
410
506
653
Obs
boat ( = 1)
no-boat ( = 0)
681
481
681
481
681
481
681
481
680
481
0397
0550
0575
0466
0498
(∆−
)
+
(∆
)

Descriptive statistics of Amsterdam % log returns over 2 or 3 day periods (denoted ) with
or without a boat arrival ( = 1 or  = 0). Periods: Sep. 1771 - Dec. 1777 and Sep. 1783
- Mar. 1787. The equality of the variance of boat and no-boat returns is tested using a BrownForsythe (B-F) test (0 :  = 1). ∗∗∗ Indicates statistical significance at the 1% level.
53
Table 7: The impact of different news categories on EIC returns
(∆
)2

(1)
(2)
(3)
(4)
(5)
(6)
Obs.
EIC (1)
-0.023
0.029
-0.022
0.077
0.015
0.095
101
(0.184)
(0.189)
(0.203)
(0.184)
(0.225)
(0.241)
EIC (1)
1.487
1.063
1.462
1.040
1.701
1.434
28
(0.843)*
(0.802)
(0.961)
(0.838)
(0.927)*
(1.151)
Finance
1.133
0.152
1.128
0.115
1.088
0.122
15
(0.659)*
(0.501)
(0.724)
(0.398)
(0.669)
(0.482)
India
-0.243
-0.349
-0.292
-0.485
-0.277
-0.334
32
(0.260)
(0.238)
(0.145)** (0.215)**
(0.274)
(0.142)**
Cape
0.916
0.162
0.907
-0.449
0.878
-0.457
11
(0.915)
(0.591)
(0.944)
(0.131)***
(0.886)
(0.119)***
Background
-0.388
-0.348
-0.386
-0.346
-0.297
-0.257
98
(0.130)*** (0.120)*** (0.126)*** (0.123)*** (0.145)**
(0.142)*
EIC (1)*
0.151
0.156
5
Finance
(0.922)
(0.910)
EIC (1)*
5.355
-1.176
3
Finance
(3.537)
(1.234)
EIC (1)*India
0.038
-0.029
18
(0.270)
(0.276)
EIC (1)*India
0.190
-1.141
5
(1.717)
(1.439)
EIC (1)*Cape
-0.322
-0.311
3
(0.212)
(0.251)
EIC (1)*Cape
9.181
10.480
2
(1.720)***
(1.494)***
EIC (1)*
-0.166
-0.250
16
Background
(0.266)
(0.252)
EIC (1)*
-1.806
-1.612
3
Background
(0.941)*
(1.164)
Constant
0.665
0.689
0.667
0.702
0.649
0.681
(0.102)*** (0.099)*** (0.103)*** (0.100)*** (0.107)*** (0.102)***
Obs.
512
512
512
512
512
512
2
0.051
0.077
0.047
0.111
0.051
0.106
Adj. 
Adj. 2 using
0.045
0.063
0.041
0.083
0.046
0.077
|
|∆

Regression results relating squared Amsterdam EIC news returns to the publication of relevant capitalized words in articles with news from England published in the Leydse Courant between September 1771 and December 1777.
EIC : East India Company; Finance: financial terminology (excludes "stocks"); India: reference to
regions, towns, and/or persons in the British East Indies; Cape: references to the Cape (of Good
Hope Colony); Background: words signalling background information (Table 29 in Appendix F
provides further details). News related to the EIC is introduced as two seperate dummy variables:
EIC (1) has a value of 1 if the EIC is mentioned once in an article. EIC (1) has a value of 1 if the
EIC is mentioned at least twice. The other news variables are also included as dummies, where a
dummy has a value of 1 if a specific category is referenced at least once. The ‘Obs.’ column indicates for how many observations a certain (combination of) dummy(ies) is turned on.
Robust, bootstrapped (1000 replications) standard errors are reported in parantheses. ***, **, *
54
denote statistical significance at the 1, 5 and 10% level respectively.
Table 8: Volatility in LND - days with/without a boat arrival from AMS
∆

EIC
BoE
3% Ann
mean
boat ( = 1)
0013
0014
0019
0066
0051
0034
no-boat ( = 0)
variance
boat ( = 1)
no-boat ( = 0)
(B-F statistic)
0629
0630
(009)
0228
0274
(160)
0218
0328
(537)∗∗
skewness boat ( = 1)
no-boat ( = 0)
−0090
−0762
0390
−0072
0214
0008
kurtosis
boat ( = 1)
no-boat ( = 0)
1049
1294
1240
1488
922
1284
% zero
boat ( = 1)
no-boat ( = 0)
1716
1695
1546
1827
2342
2073
Obs
746
731
854
boat ( = 1)
no-boat ( = 0)
590
613
685
Descriptive statistics of % log London returns over 2 or 3 day periods (Mon-Wed-Fri)
denoted ) with or without the arrival of a boat from Amsterdam ( = 1 or  = 0).
Periods: Sept. 1771 - Dec. 1777 and Sept. 1783 - Mar. 1787.
The equality of variances for news and no-news periods is tested using a BrownForsythe test (0 :  = 1). ∗∗ denotes statistical significance at the 5% level
55
Table 9: Predictive regressions - EIC
Panel (1): Future Amsterdam EIC returns (∆
+ )
2/3 days 4/5 days
1 week
2 weeks
3 weeks
4 weeks
Current Amsterdam EIC
0.001
-0.010
-0.019
-0.035
-0.012
0.021
)
(0.029)
(0.044)
(0.050)
(0.082)
(0.085)
(0.099)
returns (∆

Constant
0.032
0.063
0.092
0.174
0.255
0.341
(0.018)* (0.026)** (0.032)*** (0.046)*** (0.058)*** (0.068)***
N
1540
1535
1530
1515
1500
1485
2
0.00
0.00
0.00
0.00
0.00
0.00
Adj. R
London news EIC
returns (∆
−1 )
Constant
N
Adj. R2
Panel (2): Future Amsterdam EIC returns (∆
+ )
2/3 days 4/5 days
1 week
2 weeks
3 weeks
4 weeks
0.042
0.032
0.084
0.055
0.072
0.176
(0.043)
(0.060)
(0.060)
(0.077)
(0.103)
(0.115)
-0.015
0.003
0.057
0.124
0.153
0.209
(0.028)
(0.039)
(0.048)
(0.067)*
(0.087)*
(0.102)**
737
736
735
731
724
722
0.00
0.00
0.00
0.00
0.00
0.00
Panel (3): Future London EIC returns (∆
+ )
2/3 days 4/5 days
1 week
2 weeks
3 weeks
4 weeks
Current London EIC
0.020
-0.011
0.025
0.042
0.063
0.146

(0.046)
(0.062)
(0.081)
(0.082)
(0.099)
(0.104)
returns (∆ )
Constant
0.027
0.042
0.053
0.106
0.157
0.199
(0.023)
(0.032)
(0.039)
(0.055)*
(0.067)**
(0.077)***
N
1167
1302
1322
1335
1336
1336
2
0.00
0.00
0.00
0.00
0.00
0.00
Adj. R
This table tests whether, and over what horizons, returns revert. Panel (1) looks at reversals within
the Amsterdam market. The table presents estimates of the following regression:

∆
+  , where ∆
+ = 0 + 1 ∆
+ is the Amsterdam return from  to  , where  lies

between 2/3 days and 4 weeks. ∆
is the usual Amsterdam 2/3 day return (Mon-Wed-Fri).
Panel (2) focuses on reversals across markets and presents the estimates of:


∆
+ =  0 +  1 ∆−1 +  for  = 1, where ∆+ is defined as before,  = 1 indicates

that a boat arrived in , and ∆−1 is the London return observed during that period. See Figure 3
for more details. Note that ∆
+ does not capture the Amsterdam response to the news from
London. Panel (3) looks at return reversals within the London market. Presented estimates are for:


+  where ∆
is the London 2/3 day return (Mon-Wed-Fri) and
∆

+ =  0 +  1 ∆
 
∆+ is the return in London between  and  . Robust, bootstrapped (1000 replications) standard
errors are presented in parentheses. ***,**,* denotes significance at the 10, 5, 1% level respectively.
56
Table 10: Predictive regressions - BoE
Panel (1): Future Amsterdam BoE returns (∆
+ )
2/3 days 4/5 days
1 week
2 weeks
3 weeks
4 weeks
Current Amsterdam BoE
-0.049
-0.086
-0.049
0.009
0.032
0.154
)
(0.036)
(0.044)**
(0.054)
(0.077)
(0.096)
(0.118)
returns (∆

Constant
0.027
0.056
0.079
0.155
0.233
0.315
(0.012)** (0.016)*** (0.020)*** (0.028)*** (0.036)*** (0.043)***
N
1540
1535
1530
1515
1500
1485
2
0.00
0.00
0.00
0.00
0.00
0.00
Adj. R
London news BoE
returns (∆
−1 )
Constant
N
Adj. R2
Current London BoE
)
returns (∆

Constant
N
Adj. R2
See Table 9 for details.
2/3 days
0.018
(0.057)
0.029
(0.017)*
707
0.00
Panel (2): Future Amsterdam BoE returns (∆
+ )
4/5 days
1 week
2 weeks
3 weeks
4 weeks
0.099
0.091
0.206
0.211
0.355
(0.078)
(0.081)
(0.103)**
(0.130)
(0.157)**
0.059
0.072
0.145
0.254
0.316
(0.023)*** (0.028)*** (0.039)*** (0.051)*** (0.059)***
707
705
699
692
689
0.01
0.01
0.01
0.01
0.02
Panel (3): Future London BoE returns (∆
+ )
2/3 days 4/5 days
1 week
2 weeks
3 weeks
4 weeks
-0.049
-0.106
-0.038
0.049
0.086
0.207
(0.045)
(0.054)**
(0.069)
(0.087)
(0.106)
(0.118)
0.029
0.054
0.081
0.165
0.235
0.311
(0.014)** (0.018)*** (0.021)*** (0.030)*** (0.038)*** (0.045)***
1156
1319
1339
1344
1344
1344
0.00
0.01
0.00
0.00
0.00
0.00
57
Table 11: Predictive regressions - 3 % Annuities
Panel (1): Future Amsterdam 3% Ann. returns (∆
+ )
2/3 days
4/5 days
1 week
2 weeks
3 weeks
4 weeks
Current Amsterdam 3%
-0.107
-0.185
-0.200
-0.159
-0.149
0.027
Ann. returns (∆
)
(0.034)***
(0.049)***
(0.063)***
(0.082)*
(0.102)
(0.122)

Constant
0.033
0.064
0.093
0.168
0.251
0.332
(0.015)** (0.020)*** (0.023)*** (0.032)*** (0.039)*** (0.046)***
N
1540
1535
1530
1515
1500
1485
2
0.01
0.02
0.02
0.01
0.00
0.00
Adj. R
London news 3% Ann.
returns (∆
−1 )
Constant
N
Adj. R2
Current London 3% Ann.
returns (∆
)

Constant
N
Adj. R2
See Table 9 for details.
Panel (2): Future Amsterdam 3% Ann. returns (∆
+ )
2/3 days
4/5 days
1 week
2 weeks
3 weeks
4 weeks
0.043
0.122
0.114
0.208
0.222
0.437
(0.047)
(0.059)**
(0.080)
(0.093)**
(0.116)* (0.134)***
0.007
0.042
0.067
0.141
0.245
0.311
(0.019)
(0.026)
(0.029)** (0.040)*** (0.050)*** (0.058)***
865
864
860
852
842
838
0.00
0.01
0.01
0.01
0.01
0.03
Panel (3): Future London 3% Ann. returns (∆
+ )
2/3 days
4/5 days
1 week
2 weeks
3 weeks
4 weeks
-0.031
-0.083
-0.059
0.035
0.064
0.191
(0.039)
(0.052)
(0.068)
(0.099)
(0.113)
(0.125)
0.026
0.056
0.075
0.151
0.233
0.315
(0.013)** (0.018)*** (0.022)*** (0.031)*** (0.039)*** (0.046)***
1516
1537
1539
1539
1539
1539
0.00
0.00
0.00
0.00
0.00
0.00
58
Table 12: IV estimates
EIC
SSC
(∆
)2

BoE
3% Ann
4% Ann
OLS
IV
First
stage:
boat ( = 1)
0.429
0.144
0.099
0.212
(sd)
(0.079)*** (0.040)*** (0.030)*** (0.057)***
0.151
(0.041)***
constant
(sd)
0.150
(0.023)***
0.279
0.177
0.132
0.194
(0.036)*** (0.022)*** (0.019***) (0.023)***
Obs
1159
1159
1159
1159
1159
constant
boat + constant
0.394
0.551
0.571
0.478
0.498
F
Adj R2
59.48
0.29
59.48
0.29
59.48
0.29
59.48
0.29
59.48
0.29
0.198
(0.061)**
0.102
(0.054)*
0.243
(0.092)***
0.196
(0.052)***
0.219
0.146
0.130
0.176
(0.073)*** (0.031)*** (0.036)*** (0.046)***
0.124
(0.027)***
0.533
Second boat ( = 1)
stage:
(sd)
(0.148)***
constant
(sd)
Obs
1159
1159
1159
1159
1159
constant
boat + constant
0.291
0.424
0.560
0.420
0.388
Hausman
Chi2
0.69
1.36
0.00
0.18
1.87
(p-value)
(0.407)
(0.244)
(0.960)
(0.672)
(0.172)
OLS and IV estimates of the impact of boat arrivals on squared security returns in Amsterdam. % log returns calculated over 2 or 3 day periods (denoted ) with or without the
arrival of a boat.  = 1 captures the difference in variance between boat and no-boat
observations. The constant captures the benchmark no-boat variance.
Instruments used for the arrival of boats: days since departure next expected news packet,
wind direction in the no-go zone, and temperature below 3 degrees Celcius (dummy
variable). Information about the wind direction in the no-go zone is included as follows.
For each wind observation (availability: 2 or 3 observations a day) I determine whether
the wind is in the no-go zone (55 degrees on either side of a strictly eastern direction).
For every day I calculate the fraction of no-go zone observations (0 13  12  23  1). I include
this information as dummy variables.
Period: Sept . 1771 - Dec. 1777 and Sept. 1783 - Mar. 1787
A Hausman test is reported on the equality of the boat coefficients (0 :  = 0).
***,**,* denotes statistical significance at the 1, 5 and 10% level. Robust, bootstrapped
standard errors (1000 replications) are reported in parentheses.
59
Table 13: Time to expiration
(∆ |10 ≤  ≤ 12)
(∆ | ≤ 3)
B-F test
(p-value)
EIC
SSC
BoE
0.463
0.160
0.265
0.252
0.204
306
0.588
0.197
0.299
0.393
0.242
290
1.71
(0.192)
1.64
(0.201)
0.03
0.16
0.21
(0.858) (0.686) (0.650)
3% Ann 4% Ann Obs.
Variances of security returns in Amsterdam. % log returns calculated over 2
or 3 day periods (denoted ).
Each year there were 4 possible expiration dates (the 15th of February, May,
August, and November). The newspaper reported the price for a future contract ending on the next expiration date  . This means that an (observed)
contract had a maximum maturity of 13 weeks. For each return we can calculate the time to expiration in weeks:  = ( − ) 7 where  is the calendar
date associated with  . The table presents the return variance for two
different sub-samples:
- Amsterdam returns on future contracts with  ≤ 3
- Amsterdam returns on future contracts with 11 ≤  ≤ 13
Periods: Sept. 1771 - Dec 1777 and Sept. 1783 - Mar. 1787. Brown-Forsythe
tests are performed on the equality of variances for returns on contracts with
long and short time to expiration. If shocks to the cost-to-carry component
drive a significant fraction of returns, we would expect (∆ |11 ≤  ≤
13)  (∆ | ≤ 3)
60
Table 14: Momentum
Current Amsterdam return: ∆

EIC
BoE
3% Ann.
Amsterdam news return, previous period
0.033
-0.055
-0.079

(0.052)
(0.064)
(0.057)
(−1 = 1): ∆−1
London news return, observed in previous
period (−1 = 1): ∆
−2
-0.002
(0.053)
-0.038
(0.064)
-0.008
(0.065)
Constant
0.011
(0.029)
623
0.00
0.032
(0.017)*
634
0.01
0.031
(0.020)
655
0.001
N
Adj. 2
Estimates of the following regression equation

= 0 + 1 ∆
+ 2 ∆
∆
−1

−2 +  for −1 = 1
where −1 = 1 indicates that a boat arrived in period  − 1.
is the current Amsterdam return that can take place either in the presence of absence of a
∆

is the previous period’s Amsterdam boat return, ∆
boat. ∆
−1
−2 is the London news return
observed in the previous period. Coefficient 1 measures the reversal of Amsterdam returns, 2
measures the continuation of London news returns. Robust, bootstrapped (1000 replications) standard errors are reported in parantheses. * indicates statistical significance at the 10% level.
Table 15: Private information: news and no-news returns
Amsterdam boat returns
Amsterdam no-boat returns

)
(∆−
)
(∆
+
EIC
BoE
3% Ann.
EIC
BoE
3% Ann.
London post-departure
0.233
0.138
0.154
0.138
0.168
0.134

(0.042)*** (0.050)*** (0.070)** (0.034)*** (0.043)*** (0.051)***
returns (∆ )
London pre-departure
returns (∆
−1 )
0.373
0.405
0.486
(0.042)*** (0.054)*** (0.068)***
Constant
0.045
0.025
0.031
0.006
0.003
0.006
(0.028)
(0.017)
(0.021)
-0.024
-0.016
-0.019
N
622
640
669
467
465
479
2
Adj. 
0.286
0.250
0.254
0.053
0.066
0.034
This table presents estimates of the impact of private information on security returns in Amsterdam

= 0 + 1 ∆
+ 2 ∆
on days with and without a boat arrival: ∆


−1 +  for  = 1
=  0 +  1 ∆
+  for  = 0. Returns are defined in Figure 3. London returns
and ∆

+
 
∆
are defined as the price change after the departure of a boat (up to the departure of the next
boat) and capture contemporaneous (but as of yet) unreported returns. For comparison, the Table
reports the impact of ∆
−1 , the return in London before the departure of a boat, capturing the impact of the arrival of public news. Robust, bootstrapped (1000 replications) standard errors are reported in parantheses. ***, and ** denote statistical significance at the 1 and 5% respectively.
61
Table 16: Trading costs
EIC
SSC
BoE
(A) Order handling costs
Fees (%)
Roll measure (raw) (%)
Implied variance
Roll measure (Hasbrouck) (%)
Implied variance
0.078
0
0.047
0.004
0.142
0.196
0.077
0.101
0.020
0.090
0.122
0.030
0.054
0.006
3% Ann.
4% Ann.
0.159
0.225
0.101
0.111
0.025
0.142
0.192
0.074
0.099
0.020
(B) Inventory costs
Lamda
0
0.263
0.222
0.323
0.283
Rho
0
3.111
1.899
2.869
1.455
Implied variance
0
0.069
0.049
0.105
0.080
Estimates of trading costs. Panel (A): fees indicate official broker’s fees. Fees
were 0.125% of a security’s nominal or par value. To arrive at the fees presented
here, the “nominal” fee is multipled with the average
p security price. Estimates of
Roll’s (1984) "raw" trading cost measure:  = − (∆  ∆−1 ).
For the EIC, the raw Roll measure cannot be calculated since the first-order autocovariance is positive. The Table therefore also presents estimates using Hasbrouck’s
(2009) Bayesian estimation. The implied variance is given by (∆ ) = 22 .
Panel (B): estimates of inventory costs, based on the simple model presented in
in the main text: ∆ = ∆ +  where  = {1 −1} with prob. {   1 −   },
where   = min {max [12 + (−1 − −1 ) 1]  0}. Estimates of  and  are
based on minimizing the MSE of the empirical and model implied implied autocovariances (up to 12 lags; 4 weeks). Figure 27 in Appendix F gives a graphical
presentation of the fit of the data and the parameters that minimize this MSE.
The implied variance is calculated based on the simulated return series.
62
Boat returns
Attributed to:
Public news
Private information
Trading costs
Residual
N
No-boat returns
Attributed to:
Private information
Trading costs
Residual
N
Table 17: Summary results
EIC
BoE
3% Ann.


(∆
) % total (∆
) % total (∆
) % total

0.703
0.229
0.415
0.380
0.277
0
0.047
681
54.0%
39.3%
0%
6.7%
0.279
0.110
0
0.169
481
0.129
0.045
0.049
0.006
681
56.4%
19.5%
21.6%
2.5%
0.131
39.4%
0%
60.6%
0.051
0.049
0.030
481
0.195
0.060
0.105
0.055
681
47.0%
14.6%
25.2%
13.3%
0.193
39.3%
37.7%
23.0%
0.052
0.105
0.036
481
26.9%
54.2%
18.9%
All returns
0.528
0.188
0.323
Attributed to:
Public news
0.223
42.2%
0.076
40.2%
0.114
35.3%
Private information
0.208
39.4%
0.047
25.2%
0.057
17.6%
Trading costs
0
0%
0.049
26.3%
0.105
32.4%
Residual
0.097
18.5%
0.016
8.42%
0.047
14.7%
N
1162
1162
1162
This Table presents the results of a simple variance decomposition. The impact of public
and private information is identified through the covariances with London pre- and post

departure
returns,
³
´ ∆ ³ and ∆−1 , i.e. ´
³
´
 ∆
=  ∆
+  ∆
+  2 , and
 ∆
 ∆

−1

´
³
´
³ 

=  ∆−
+  2+ (see section 3.3 for details)
 ∆−
 ∆

+
+
The impact of trading costs are identified through simulations of a simple inventory cost
model. See Section 3.4 and Table 16 for details.
63
Table 18: Amsterdam return reversals and the arrival of boats
(1)
(2)
(3)
(4)
Current return, ∆

EIC
All
 = 1
 = 0
∆
Previous return, −1 = 1
0.033
0.082
-0.023
-0.105

=
0
-0.117
-0.160
0.067
0.227**
∆
−1
−1
∆
-0.150*
-0.242**
0.090
BoE
Previous return, −1 = 1
−1 = 0
∆
−1
∆
-0.074
-0.051
0.023
-0.088
-0.068
0.020
-0.063
0.070
0.133
0.025
0.139
3% Annuities
Previous return, −1 = 1 -0.072
-0.048
-0.088
-0.040

∆−1
−1 = 0 -0.273
-0.280
-0.183
0.097
∆
-0.201**
-0.232**
-0.095
This Table provides more details about reversal patterns in security returns. It
= 0 + 1 ∆
presents coefficients of the regression ∆

−1 +  for different
scenarios, indicated by −1   ∈ {0 1} which indicate whether periods  and
 − 1 featured the arrival of a boat or not. Reported reversal coefficients are
based on regression equations (12) and (13) (see main text on p. 37) and sum up
the different interaction effects. Differences in coefficients are indicated with ∆.
***, **, and * indicate whether differences are statistically different at the 1, 5,
and 10% level respectively. The statistical test are based on robust, bootstrapped (1000 replications) standard errors that come from estimating (12) and (13).
64