A Common Features Analysis of Amsterdam and London Financial
Markets During the 18th Century
Greg Dempster
John M. Wells
And
Douglas T. Wills*
RRH: DEMPSTER, WELLS & WILLS: COMMON FEATURES
* The authors would like to thank Robert Hébert, Larry Neal, Farshid Vahid, and an anonymous referee for help
with this paper. Chris Wilkins at QMS provided suggestions instrumental in the programs used herein. Wells and
Wills are also grateful to the National Science Foundation for financial support and to Sarah Millard at the Bank of
England for her assistance. We retain all property rights to any remaining errors.
Dempster: Ph.D. candidate, Dept. of Economics, Auburn University, Auburn, AL 36849-5242; phone: (334) 8442902; fax (334) 844-4615; E-mail: [email protected]
Wells: Corresponding author, Assistant professor, Dept. of Economics, Auburn University, Auburn, AL 368495242; phone: (334) 844-2902; fax (334) 844-4615; E-mail: [email protected]
Wills: Assistant professor, Dept. of Economics, Sweet Briar College, Sweet Briar, VA 24595-0103; phone: (804)
381-6203; fax: (804) 381-6173; E-mail: [email protected]
JEL Codes: F32, G15, N23
1
A Common Features Analysis of Amsterdam and London Financial
Markets During the 18th Century
“If one were to lead a stranger through the streets of Amsterdam and ask him were he was, he
would answer, ‘Among speculators,’ for there is no corner [in the city] where one does not talk
shares.” Joseph de la Vega, 1688
I.
INTRODUCTION
During the 1700’s the distance between London and Amsterdam was three days travel.
Yet the vagaries of wind, sea, and sail often increased this distance to six days or more. Despite
these ancient impediments to the flow of information, which saw no technological improvements
over the 18th century, this paper demonstrates that the financial link between these two cities was
extremely modern.
The issues surrounding this financial linkage were also extremely modern, and can be
viewed in the context of the recent turmoil in Mexico and Asia. In England of the 1700s, as in
our economies of today, it was common for foreign investors to be blamed for dramatic
movements in domestic currencies and stock markets. Since arbitrage and speculation between
London and Amsterdam was not hindered by capital controls, taxes on dividends, or monetary
upheavals at least until the Napoleonic Wars, Dutch investment in English assets grew to
tremendous levels. This generated considerable domestic concern over the effects and
appropriateness of these international capital flows and fear that such flows destabilized financial
markets. Many great economists of the day, such as Jeremy Bentham, David Hume, Adam
Smith, and James Steuart, addressed these issues in their writings.
More recent work has also addressed Dutch investment in Britain, without reaching any
clear conclusions on its effects. Carter [1975] maintains that the Dutch investor was passive and
did not respond to price differences between the markets. Ashton [1959] attributes several
2
London financial panics to Dutch speculation, while Carruthers [1996] and Mirowski [1981;
1987] argue that the London market was inefficient. These topics are also addressed in the
important work of Neal [1990] and Eagly and Smith [1976] as well as the historical analyses in
Riley [1980] and Wilson [1941].
At issue here is the degree to which Dutch speculation can be regarded as active and/or
destabilizing. More generally, the causal linkage between the two markets has never been
established. These same issues are at the forefront of the economic and development discussions
of today, and we attempt to shed light on them by examining a period when two markets were
allowed to interact without the governmental interference that characterizes current capital
markets. Using the prices of stocks traded simultaneously on both the London and Amsterdam
exchanges from 1723 to 1794 in conjunction with recent econometric advances to detect
common features and regime shifts between the two markets, we find a level of financial
integration that rivals that of our present information age. In particular, the prices of Bank of
England, East India Co., and South Sea Co. stocks determined in London appear to share
common trends and common cycles with prices of these same stocks determined in Amsterdam
on the same day. Moreover, individual shocks to these assets are shown to translate quickly and
accurately between the distant markets. We find little evidence that Dutch investment was
destabilizing, but considerable indication that the Dutch were active speculators.
This paper is organized as follows. Section II addresses the issues surrounding these
markets, the data is introduced in Section III, and Section IV discusses the common feature tests
with results following in Section V. A final section concludes.
II.
LONDON AND AMSTERDAM MARKETS IN THE 18TH CENTURY
3
The London Market
The financial developments in England during the last decade of the 17th century are well
documented.1 By the early 1700s, the financial market in London matched in sophistication and
liquidity those of Amsterdam and by the end of century it was the financial center of Europe. The
state was now able to borrow more, both short-term and long-term, at lower interest rates. This
improved ability to raise capital quickly and cheaply allowed the state to finance wars without
plunging the country into financial crises. Concurrent with the growth of an active market in
government debt instruments was an active market in the shares of the major joint-stock
companies of the time.
Shares of the Bank of England were actively traded in London markets from its inception
in 1694. From an initial capital of £1.2 million, the capital of the bank rapidly grew to nearly £9
million within 30 years. The East India Company shares also began to be actively traded during
this period and its capital stock grew to £3.2 million by 1723.
With capital of £9.2 million at its creation in 1711, the South Sea Company took on much
of the government debt accumulated during the War of Spanish Succession in exchange for a
monopoly on south sea trading rights. This infamous company never seriously participated in
trade but was so bold in its financial dealings that it sparked a speculative frenzy in 1720. After
1
See both Dickson [1967] and Neal [1990] for detailed studies of the impact and operation of English capital
markets. The institutional changes that brought about these developments are examined in North and Weingast
[1989] and Wells and Wills [1997].
4
its reorganization following the "Bubble of 1720" its capital was approximately £17 million with
a similar amount of transferable annuities.2
The parallel growth in the liquidity of capital markets, government debt, and joint-stock
firm's capital was not coincidental. Each of these firms was intimately involved in the public
finances of the British government. The government's "funded" debt came in two forms, loans
from joint-stock companies and general public loans that were primarily perpetual annuities. By
the middle of 18th Century, the Bank of England was responsible for £11.6 million of
government debt, East India Company for £4.2 million, and the South Sea Company for £27
million.3
Until the War of Austrian Succession (1739-48), these three firms held nearly all of the
government's funded debt. However, when the government required large amounts of revenue to
fund military activity for wars in 1739-48 and 1756-63, it began obtaining loans directly from the
public. By the end of the 18th Century, total government indebtedness topped £400 million, up
from £72 million in 1755. By this time, perpetual annuities accounted for almost 90% of the
funded debt.
This growth in government debt and in the capitalization of private firms took place
along with increasing public support and confidence. The transfer books and ledgers used to
2
See Clapham [1945] on the Bank of England, Sutherland [1952] and Keay [1994] on the East India Company, and
Sperling [1962] on the South Sea Co. Scott [1910] is the most authoritative account of the early English joint-stock
companies.
3
The British historical documents use the term 'funded debt' or 'the funds' to indicate that the debt was backed by a
special duty, initially on beer, ale, and other liquors. The unfunded debt mainly consisted of promissory bills that
carried interest and often circulated at considerable discount (History of the Earlier Years of the Funded Debt,
1898). On these points, and what follows, see an extremely useful survey in Carter [1975, 123-41].
5
record trades of stock show an increasing volume of transactions over the century, and also
reveal the name, status, and place of residence of the shareholders. They indicate that by midcentury there were over 60,000 total stockholders, and at least 600,000 by 1815. Most of these
investors held less than £1,000 in several stocks, traded without the assistance of a broker, and
most lived in and around London.
The Amsterdam Market
While the London market only began to develop after the events following 1688,
Amsterdam had a lively and mature speculative market in securities as early as 1630. Joseph de
la Vega, writing in 1688, describes in detail daily life on the Amsterdam Bourse indicating that
this market had developed hedging, options for puts and calls, time contracts, and an organized
role for “bears” and “bulls”.
However, the transfer of shares in the Dutch East and West India companies was, despite
the well developed Bourse, quite cumbersome compared to transfers in London. In addition, high
taxes, relatively low rates of interest, increased competition facing Dutch shipping, and a sizable
trade surplus with Britain all caused Dutch merchants to invest in English funds. By 1698, all the
major British joint-stock firms allowed foreign ownership. Of the original 1300 subscribers to
the Bank, only twenty names were undoubtedly Dutch, but their numbers must have grown
considerably since by 1726 there were over 200 Bank investors with the surname ‘van’ alone.4
The Bubble year of 1720 was perhaps the turning point in the financial link between
these two cities, as investors on the Amsterdam Bourse were swept into the speculative frenzy.
4
Wilson [1941, 13-14, ch. III passim] discuss the organization of the Amsterdam Bourse. See Clapham’s preface to
Wilson [1941, xi] with regard to Dutch investment in Bank stock.
6
Wilson [1941, 104] relates the story of small fishing smacks that were supposed to meet the
English ships and speed back to Amsterdam with the latest news related to the Bubble. The boats
“merely took a turn around outside the harbour, and, having invented their own plausible gossip,
came back and sold it to the feverish crowds of speculators”. Within the next few years, the
Amsterdam Bourse provided quotes for English funds and buying could be done in Amsterdam.
Of all foreign investors, the Dutch were the most important, providing over 80% of
foreign investment in the English funds. The stock market transfer books of the Bank of
England, for example, reveal that for the three months between January 1st and March 31st of
1755, Dutch investors were responsible for 11 percent of all sales and 16 percent of all
purchases. Furthermore, Dutch parties to all transactions in Bank stock outnumber those living
elsewhere in Europe by about 5 to 1.5
In the early part of the century, the Dutch invested heavily in Bank of England stock,
perhaps holding as much as one-third of the total stock outstanding. Later, the relative proportion
of Dutch funds in the Bank fell, shifting to the East India stocks. Throughout the post-1721
period, action in South Sea stock was minimal. Carter speculates that as much as 34 million
Dutch guilders found their way into English assets during the Seven Years’ War.6 By the time of
the Fourth Anglo-Dutch War in 1780, there was a further flight from Bank stock and British
Consols as well as loans to France, Russia and the American Colonies became prominent in
5
Carter [1975, 32, 67, 137]. The authors' examination of Bank transfer books for the early part of the 1700’s
provided similar conclusions concerning the extent of Dutch investment.
6
Carter [1971, 140]. Using the average exchange rate over this war of 35 Schellingen Banco per pound sterling and
the average Agio of 3.325, 34 million guilders would translate into approximately 3.2 million pounds sterling.
7
Dutch portfolios. However, as late as 1791, one-sixth of all proprietors of Bank stock still had
addresses in Holland.7
Dutch investment in English shares and the ease at which trade could be conducted on
both exchanges was the envy of other nations. Isaac de Pinto, writing from Paris in 1761
remarked that, “Whoever is in possession of actions, obligations secured by the state, annuities,
or other stock in England, converts them into money at one percent, more or less, according to
the market price at Amsterdam or London. It is a great advantage to the English, that their stocks
are current on the exchange of both countries. It is to be wished that the same commerce were
established in favor of the French funds.”8
While the Dutch presence in the English funds may have generated advantages for the
British, it also generated controversy. There was great concern in both countries over the size
and effects of Dutch holdings. A Dutch pamphleteer, La Leck, put the Dutch proportion of
English debt at three-sevenths of the total in 1777 and urged his countrymen to divest before an
inevitable capital loss. The figure is probably far too high, but the English public was also
uncertain as to the extent of Dutch investment and vexed as to whether or not this investment
was beneficial. Economists Malachy Postlethwayt, James Steuart, Adam Smith and David Hume
all wrote extensively on these issues. Postlethwayt and Steuart in particular viewed the foreign
creditor with great discomfort. Steuart’s [1767] response was typical of the time, suggesting that
the “most important object in paying off debts is to get quit of those due to strangers” and that it
was important to do whatever necessary “towards diminishing the burden of foreign debts.”
7
Carter [1975, 43] and Wilson [1941, xi].
8
Pinto’s work was translated into English in 1774. This passage is from page 71.
8
English politicians were even less sanguine –Daniel Defoe wrote that the Tory Ministry was
sunk due to “Foreigners withdrawing their money” from London markets in 1710.9
The opinion that speculation in Amsterdam had a destabilizing affect on the London
market was fed by the idea that the Dutch were very active and fickle speculators causing crises
to spread from one financial center to the other. Jeremy Bentham [1787, 199-201] summarizes
these concerns: “Two ill effects are attributed to outlandish money [foreign investment]: First,
that the interest paid for it is so much money sent out of the country.” And secondly, “that money
borrowed of foreigners will be perpetually liable to be recalled.” Bentham then goes on to
address these issues, dispensing with the first concern by attacking the mercantilist arguments
underlying it. He deals with the second using the relatively sophisticated asymmetric information
argument that domestic investors are more likely to withdraw their money first for they are more
acquainted with market conditions.
The tremendous volume of contemporary writings on the effects of Dutch investment and
the conflicting views found therein, therefore indicates that this subject was of great concern at
the time. Recent work by economists and historians however, has been no less contradictory.
Wilson [1941, 79, 138], for one, held that the Dutch investors had a considerable and beneficial
impact on London prices, even keeping the stocks from sinking further in time of crisis. Ashton
[1959, 124-6, 130-1] was even more evangelical in stating that several financial crises in London
had their origins in the movement of Dutch funds. Crises in 1748, 1763, and 1783 were, to some
extent, exacerbated by the Dutch as they “threw their holdings on the market”. However, Ashton
[1955, 193] also claimed that English assets were by no means “dragged at the heels of Dutch
9
Smith [1982, 907-47], Hume [1970, 90-107], Steuart [1966, 654-5]. See also Wilson [1941, 70-73], Carter [1975,
21-40], and Dickson [1967, 505, 518-19].
9
finance.” Clapham [1945, 254] confirms that the Bank was forced to curtail its discounting of
bills due to heavy sales of British securities by the Dutch in the early months of 1783.
Carter was agnostic in contrast, maintaining that the Dutch investor was passive, being
content to leave their investments untouched and unchanged; “Certainly Dutch investors do not
seem [in spite of what some have said] to have related their purchases very much to the market
price, even of Bank stock.”10 Riley [1980, 66], considering the same data we examine here,
seems to indicate that the prices of London funds on the Bourse did not respond to relevant news,
but also sees a role for contagion. Neal [1990] actually tests for efficiency and provides some
econometric evidence on the integration of the two markets, but he is only able to speculate on
issues of causality, crisis and destabilizing speculation.
A number of recent theoretical studies have shown how foreign investors can indeed
destabilize domestic markets. Dornbusch and Park [1995] argue that foreign investors pursuing
positive feedback strategies may cause domestic prices to overact, pushing them away from
equilibrium. Investors with such strategies are often seen as destabilizing because their purchases
increase prices further and their sales lead to additional market declines. Also, price
destabilization may be exacerbated by herding, or correlated trading across certain investors. 11
10
Carter is especially dogmatic regarding this point, saying further, “People who have not seen the ledgers at the
Bank will have to take on trust the fact, not assumption, that the Dutch investor is passive” [1975, 35, 63, 137-8].
11
See DeLong, Shleifer, Summers, and Waldman [1990] and Lakonishok, Shleifer and Vishny [1992] for additional
discussions along this line. It is not necessarily true that such trading strategies lead to destabilizing capital flows. In
models that emphasize information asymmetries between domestic and foreign investors, as in Brennan and Cao
[1997], stock prices incorporate domestic information which is then revealed to foreign investors through the
behavior of returns. Foreign investors thus respond to these signals without acting irrationally or destabilizing the
market.
10
For the 18th Century English commentator, as well as his modern counterpart,
“destabilizing” must have meant that actions taken by Dutch investors drove London prices away
from their relationship with fundamentals, while the issue of Dutch passivity questions the extent
to which they responded to market prices at all. Yet, the only thing that is clear from the above
discussions is that there was a large Dutch presence in English funds. The nature and effects of
this presence have not been firmly established. Hence, the important issues addressed in this
paper regard whether the typical Dutch investor was an active speculator, the extent to which
Dutch speculation had a destabilizing affect on the London market, and the nature of the causal
linkages between the markets. After we introduce the data below, we will turn to econometric
tests designed to answer these questions.
III.
DATA
The variables used in this study represent the assets of the three most important jointstock companies traded during the 18th Century. Their prices on the London market are graphed
in Figure 1 for the period August 8, 1723 to December 19, 1794 with a frequency of 2 to 3 times
a month for 1676 total observations on each price. Share prices are quoted as the number of
pounds needed to purchase a nominal value of £100 in the capital stock of the given company.
Also included in this figure is the difference between the Amsterdam and London price for the
Bank of England Stock. This price difference is representative of the other assets examined here
as prices on the Amsterdam exchange track their corresponding London price quite closely.12
12
The source for all of the data used in this paper is the ICPSR data tape at the University of Michigan. Values
quoted on the Amsterdam Bourse represented time prices and are initially from van Dillen [1931], who recorded
data from the Amsterdamsche Courant. Larry Neal matched the Courant’s prices with the spot prices of the same
11
The sample moments of the individual stock returns on each market are presented in
Table 1, along with the same statistics for the daily returns of the Dow Jones Industrial Average
over the period January 1918 to December 1984. This index is included to show the similarity
between the 18th century data and a modern stock index over a similar span of time. Indeed, the
statistics here are not unlike those found for individual securities listed on modern exchanges.13
We also include here the notations we assign to each variable. These will be used through the
rest of the paper.
The most volatile of the returns is that of the EIC, which might be expected given that it
is a trading company with its fortunes more prone to risk. The SSC’s mean return is actually
negative, but this appears to be a function of its difficulties during the latter decades of the
century. The skewness estimates is equal to zero for a normal distribution, and has a standard
error of 0.06 for our sample. This statistic does differ significantly across markets. The sample
skewness for BOE and AEIC returns is not significantly different than zero. Estimates for
modern stock returns tend to be close to zero or positive for individual stocks, but negative for
stock indexes.
The standard error for the kurtosis estimate under the null hypothesis of normality is
equal to 0.12, so all the returns display excess kurtosis. This implies that the returns have more
mass in the tail areas than would be predicted by a normal distribution. This finding is consistent
with estimates for modern daily stock returns and has lead to the use of fat-tailed unconditional
stocks quoted in London on the same day in The Course of the Exchange. This was no small feat, as the two
countries were on different calendars until 1752 and the markets traded on different days. See Neal [1990].
13
See, for example, table 1.1 in Campbell, Lo, and MacKinlay [1997].
12
distributions with finite higher moments in recent empirical studies. It appears that such
distributions also accurately describe these 18th century stock returns.
IV.
COMMON TRENDS AND COMMON CYCLES
The above discussions were meant to motivate the idea that the Amsterdam and London
financial markets were highly developed and closely related, even during this early period.
Important questions were also raised regarding the behavior of the two markets. To examine
these issues, we interpret them in the context of the time-series techniques of common trends and
common cycles analysis.
The idea that two or more time series may have various distinctive characteristics, or
features, in common, was recently generalized by Engle and Kozicki [1993]. Their example was
that two integrated stochastic variables, y1t and y2t, might be generated by the following
unobserved-components model with two features:
 y1t   λ 
ε 
φ 
  =  ω1t +  ω 2t +  1t 
1 
 ε 2t 
 y 2 t  1 
(1)
where, for our purposes, ω1t and ω2t are features common to both variables with the former being
a common stochastic trend and the latter a common cycle. Here, the linear combination y1t - λy2t
will be stationary and the linear combination ∆y1t – φ∆y2t will have no cycles, where ∆ is the
first-difference operator. This property exists despite the fact that, individually, both variables
are nonstationary and display cyclical behavior. More generally, any feature characterizing two
or more time series is understood to be common if a linear combination of the series fails to have
13
the feature even though that feature is present in each individual series. Before describing tests
for these common features, it is important for us to discuss what these features are and why they
might exist in the stock prices considered in this study.
Sources of Common Features
Our first feature, stochastic trends, is prevalent in modern financial data, and exists in the
six stock prices examined here as well.14 Financial theory implies that most asset prices will
behave like martingales, and thus will be integrated of order one. Campbell and Shiller [1987]
show that the process driving the stochastic trends in stock prices is the income stream whose
present value determines the price, i.e. dividends. This has implications for our study, as the
stochastic trend driving the stocks in each market should be common across each pair of stocks.
If the markets are integrated and investors respond to price differentials across markets, then the
asset pairs should share a common long-run trend because each is responding to the same
dividend stream. Whenever the price on either exchange moves away from this trend, there
should be buying or selling on one or both exchanges to move the prices back in line.
Alternatively, if one finds present value models untenable, the no arbitrage argument
used in Brenner and Kroner [1995] is sufficient to generate common trends, or cointegration, in
the three pairs of stock prices. Cointegration, and the resulting Vector Error Correction Model
(VECM), also allows us to examine the cross-market information flows that define the price
14
We do not report or explore unit-root tests here, as they are common enough in the literature to be considered
trivial. But Philips-Perron tests failed to reject the presence of a unit root in the logs of all the variables using a 10%
critical value.
14
discovery process between the two markets.15 This will provide inference on the existence and
nature of speculative movements in London and Amsterdam.
The second feature we explore, cycles, are the transitory but persistent short-run
movements in a variable, characterized by serial correlation. Individual stock returns may display
serial correlation due to company specific shocks, non-synchronous trading, time varying risk,
wars, or overall macroeconomic conditions. Whatever the cause, we would expect the short-run
movements to be common for the same assets traded in the two markets. Indeed, high short-run
correlations between markets is a sign of increasing economic integration and is often used as a
test of the degree of business cycle synchronization.16
A finding of common cycles across two or more variables is an even stronger test of
market integration than is the discovery of contemporaneous correlation as it allows for the
simultaneous analysis of the persistence of disturbances and comovement. Cycles are called
‘common’ if the returns of the two assets display cycles but a linear combination of the returns
does not. In this case, the cyclical amplitude of each return may be different across the assets but
their phase is the same.17 Hence, the existence of common cycles has the important implication
that investors in the two markets are responding to shocks in the same manner and at the same
time.
15
Note that there is no reason that the price of BOE stock, for example, in Amsterdam and London should be the
same. The prices may diverge from one another even if cointegrated simply because the differentials are not
predictable ex ante. Hence, an apparent arbitrage opportunity may not cover the cost of trading. See Harris, McInish,
Shoesmith, and Wood [1995, 566].
16
See Bayoumi and Eichengreen [1994], and Bekaert [1995], for examples.
17
Engle and Issler [1995, 85]
15
In summary, we expect each pair of asset prices to display common stochastic trends and
common cycles as long as investors are responding to price differentials across markets. Vahid
and Engle [1993] have developed a method to test for both features using reduced rank
regression methods. We now turn to a discussion of these tests.
Testing For Common Trends and Cycles
In order to represent the common feature analysis assume the variables are described as
integrated processes that follow a Vector Autoregression of order p:
A( L) y t = ε t
(2)
where the A(L) is a matrix polynomial in the lag operator L, with A0 = I and the Ai’s are NxN
matrices, yt is a Nx1 vector containing the stock prices, and εt is a Nx1 vector of white noise
disturbances. The VECM for equation (2) is
∆y t = − A(1) y t −1 + A1* ∆y t −1 + A2* ∆y t − 2 + ⋅ ⋅ ⋅ + A*p −1 ∆y t − p +1 + ε t
(3)
where the Ai* = - (Ai+1 + … + Ap), ∀i=1,2,…,p-1. Note that A(1) = γα´, where α´ is the r x N
matrix of cointegrating vectors, γ is the N x r matrix of error correction terms, and r is the rank of
A(1) and the number of linearly independent cointegrating relationships, 0≤ r < N.
It is the matrices γ and Ai* that we plan to exploit to determine the degree of destabilizing
speculation between the two markets. Note that γ shows the adjustment of ∆yt to the previous
periods’ disequilibrium, α´yt-1, and the Ai* indicate the degree of short-run feedback within and
between markets. If Dutch investors had a destabilizing influence on the London market, one
16
would expect large price movements in the Dutch market to be followed by large price
movements in London, as actions taken on the Bourse drove London prices away from
fundamentals.18 In this context, the elements of γ relating to the London market and the elements
of Ai* relating to the effects of lagged Amsterdam returns on the London market should be larger
(in absolute value) and more significant than the parameters estimated for the Amsterdam
market.
In testing for the number of cointegrating relationships in yt, we use the MLE methods as
found in Johansen [1988]. Here, the number of linearly independent cointegrating relationships
and the cointegrating vectors are identified from the largest squared canonical correlations and
corresponding eigenvectors of the product matrices produced from regressing ∆yt and yt-1 on the
lags of ∆yt. With r cointegrating relationships, N – r common trends drive the long-run behavior
of the N variables in yt.
Similarly, the number of common cycles is given by the number of linearly independent
combinations of the elements of ∆yt which have no dependence on the relevant past. The lagged
∆yt’s and α´yt-1 will explain all the serial correlation of ∆yt. Denote the parameter matrix that
eliminates the serial correlation of ∆yt as δ. This is commonly referred to as the cofeature matrix,
and if the data have common cycles, then δ′∆yt = δ′εt.
After specifying the VECM in (3) with the number of error correction terms equal to the
cointegrating rank, the common cycles tests are performed by considering the number of linear
combinations of the ∆yt’s which are uncorrelated with any linear combination of the variables on
the right-hand-side of (3). These tests are computed as the canonical correlations between ∆yt
and Xt, where Xt is the vector of lagged ∆yt’s and the error correction terms. The cofeature rank,
18
See DeLong, Shleifer, Summers and Waldman [1990], Brennan and Cao [1997] and Choe, Kho, and Stulz [1998].
17
denoted by s with 0 ≤ s < N, is the number of statistically zero canonical correlations. The test
statistic for the null hypothesis that there are at most N – s common cycles, is given by Vahid
and Engle [1993] as
s
C ( p, s ) = −(T − p − 1)∑ log(1 − λi )
(4)
i =1
where T is the sample size, and the λi’s (i = 1,…,s) are the s smallest squared canonical
correlations between Xt and ∆yt. This likelihood ratio test statistic is distributed as a χ2 with s2 +
sN(p-1) + sr –sN degrees of freedom under the null.19
V.
EMPIRICAL RESULTS
We now turn to the estimation of the number of common trends and cycles in the stock
prices. The first step in this process is the determination of the appropriate lag length and
deterministic terms for each pair of series as well as the six variable system. Starting from a lag
length of 8 in an unconstrained VAR in levels, the Akaike information criteria indicates that the
two-variable VAR’s for Bank and EIC stocks each require four lags, a constant and trend, while
the VAR for South Sea stocks only requires three lags, and a constant. A six variable VAR for all
19
With r linearly independent cointegrating vectors there can be at most N – r linearly independent cofeature
vectors. There is no assurance that N-r = s, but when this equality holds Vahid and Engle [1993] show that a special
trend-cycle decomposition exists that allows one to separate the movements in the yt’s into their common random
walk trends and common cycles. Since our focus is simply on the existence of common trends and cycles, and
because the behavior of these elements is not interesting in our context, we do not perform this decomposition.
18
the stocks together necessitate three lags with the trend and constant. We maintain these
specifications throughout our tests below.
Cointegration Results
In Table 2 we list the null hypotheses, eigenvalues, likelihood ratio statistics, the
corresponding five percent critical values for the cointegration tests, and the normalized
cointegrating vectors. Throughout, an asterisk denotes significance at the 1% level. Part A shows
the results for the two Bank of England stocks, Part B is for the two East India Stocks, C for
South Sea stocks, and D is a six variable VECM containing all the stocks. In the first three cases
we find one cointegrating vector between the pairs of stocks suggesting that the stock prices in
London share a common trend with their counterparts in Amsterdam.20
The six-variable model in Part D confirms these observations, as we cannot reject the
hypothesis that there are only three cointegrating relationships between the six variables. The
fact that more cointegrating vectors are not found here indicates that Bank, East India, and South
Sea stocks have stochastic trends that evolve separately from one another. Indeed, Richards
[1995] has shown that there is no reason for us to expect the stocks of different companies to
move together in the long run. There is some evidence however, that the three cointegrating
relationships depend on stocks other than the London and Amsterdam pairings. Imposing the
restriction that the three long-run relationships only contain the pairings from the same company
results in a χ2(6) statistic of 61.68, which clearly rejects the restrictions. This rejection appears to
20
Each of the cointegrating vectors appear proportional to the vector (1, -1)’, but this hypothesis is rejected for the
Bank and South Sea stocks. The hypothesis cannot be reject for the AEIC/EIC paring. (All empirical results in this
19
be solely a function of the cointegrating relationship between AEIC and EIC, since neither the
relationship between the two Bank stocks nor the two South Sea stocks reject the hypothesis that
they are the only assets involved in their long-run vectors.
The estimation results from the Error Correction Models, obtained using iterative
seemingly unrelated regression, are summarized in Table 3. The absolute value of the t-statistics
are reported in parentheses next to each coefficient and chi-squared tests for the joint
significance of the lagged differences are given below each equation.
Focusing on the bivariate results in Part A, it is clear that the returns in both markets are
very responsive to the error correction terms for each stock. The parameters on the error
correction terms indicate how much time is necessary for the prices to adjust to the long-run
relationship between markets. The significance of the adjustment parameters indicates that error
correction occurs in both markets to maintain cross-market equilibrium. Note that the adjustment
coefficients are significantly smaller in the London market suggesting that reactions in London
to price differences are smaller than the response in Amsterdam. Hence, the adjustment to longrun equilibrium is much greater in Amsterdam than in London. 21
The size of the adjustment parameter differs across stocks, but they are remarkably large
given the primitive nature of information flows that existed during the 18th century. The limits of
wind and sail prevented news from traveling between these two cities any quicker than three
paper were obtained using programs written in Eviews 2.0 and 3.0. These programs are available from the authors
upon request.)
21
It is common to associate these adjustment parameters with the speed of adjustment to long-run equilibrium (e.g.,
Johansen [1988]), but as a recent paper by Rossana [1998] and a comment by a referee point out, this interpretation
is often misleading.
20
days and, as the entries in Luttrell [1969] indicate, it often took six days or more because
westerly winds hindered Dutch boats.22
Though the adjustment parameters indicate that most of the variation in returns in each
market is due to error correction, there is a role for short-run relationships in some of the stocks.
Likelihood ratio tests show that returns on the East India Co. traded in Amsterdam respond
significantly to its own lagged returns and the lagged returns of the London price. South Sea
stock appears to exhibit a bi-directional relationship with the return in each market being
significantly affected by returns in the other market. In general, the equations explain much more
of the variation in Amsterdam returns than in London returns, but it is clear that both markets
contributed to the price discovery process for each stock.
Turning to the multivariate results from the six-variable VECM in Part B, we restricted
the cointegrating relationships to those between the stock pairings even though this restriction
was rejected above. This seemed appropriate given that we are only interested in how the
separate stocks responded to deviations from the long-run relationships between the three pairs
of assets. In this context, it is interesting to note that only the two Bank stocks respond to all
three error correction terms. Though the Bank response to EIC/AEIC- and SSC/ASSC-errors is
quite small, both the magnitudes and signs are similar across the two markets. ABOE responds
significantly to short-run movements in EIC, while ASSC and SSC are each influenced by shortrun movements in ABOE. Again we must conclude that actions taken in each market had some
impact on the determination of asset values in the other.
22
These adjustment parameters can be compared favorably with results on modern asset markets. For example,
Frankel and Schmukler [1996] find that the prices of Mexico country fund assets traded in New York adjust at the
rate of 26 to 44 percent per two week period to their corresponding Net Asset Values determined in Mexico City.
21
Common Cycle Results
We now consider whether the individual stock returns determined in London share a
common cycle with their Amsterdam counterparts. This is an additional restriction on the
comovement of the variables and is in no way implied by the fact the variables share a long-run
trend. Using the same VECM’s estimated in Part A of Table 3, we estimate the canonical
correlations and the cofeature rank based on equation (4) for each of the stock pairings. Table 4
lists the null hypothesis, eigenvalues, test statistics, degrees of freedom (DF) and the resulting pvalues for these tests.
Note that we cannot reject the presence of one common cycle between the BOE/ABOE
and EIC/AEIC stock pairings, while the hypothesis that there exists more than one common
cycle is soundly rejected. Hence, there must have been considerable activity in these markets that
forced these two stocks to move together even in the very short run. This gives further credence
to the conjecture that the Amsterdam and London financial markets were highly integrated and
that investors responded to price differentials during this early period.
In contrast, we reject the presence of a common cycle for the South Sea stocks. This
indicates that prices of South Sea stock on the different exchanges could drift far apart before
arbitrage eventually brought the prices back in concert. The failure to find a common cycle for
this asset is exactly the result one would predict if Carter were correct about Dutch passivity. We
also have contemporary evidence from Carter suggesting that this stock was the least popular of
the three among Amsterdam investors. Indeed, Neal [1990, 155] maintains that actions in South
Sea stock were relatively dormant for much of the period after 1730.
22
As a further check on the degree of comovement between the Amsterdam and London
markets, we performed two additional tests on the vector of stock prices. We first tested the sixvariable VECM for the number of common cycles and again found significant comovement only
between the two Bank stocks and the two EIC stocks; the hypothesis of three or more common
cycles between the six stocks was rejected.
Next, we tested for significant regime shifts in two markets to determine if turning points
in a VAR for London prices is matched by turning points in a VAR for Amsterdam prices. The
method used here is that of Wells and Wills [1997] which is based on the break-point analysis of
Bai, Lumsdaine, and Stock [1998]. In total, we found 18 instances over the sample where the
vector of London prices moved significantly up or down for 50 observations or more. Eleven of
these regime shifts occur either on the same day as shifts in the vector of Amsterdam prices (4 of
11) or within one observation of each other (the remaining 7). In five of the seven cases where
one market broke before the other, it is the London market that is leading the Amsterdam market,
which is consistent with our causality results above and Bentham’s asymmetric information
argument. Relevant movements in the market seem to have originated in London and there is no
evidence that London stocks start falling because of a withdrawal of Dutch investors. Moreover,
the direction of the breaks is almost identical across the markets with the exception of the signs
on the movement in South Sea prices, which break in different directions on four occasions. Two
additional regime shifts occur three observations earlier in London than in Amsterdam. But both
of these breaks are related to events during the War of Austrian Succession (1741-48) where the
23
Amsterdam response may have been slowed due to French naval activities in the English
Channel.23
This additional evidence further supports the notion that these two markets were
intimately connected and remarkably efficient despite the technological impediments on
information flows. Taken as a whole, the common features and break point analyses put us in the
position of disagreeing with some contemporary authors and the more recent writings of a few
economists. If Dutch investors were passive and did not relate their purchases to the price of
assets then finding common trends and cycles and such large adjustment parameters on the
Bourse would be very surprising. If Dutch investment was destabilizing and the primary cause of
crises in London then we would expect to find (1) larger adjustment parameters in London, (2)
London returns significantly predicted by Amsterdam returns, and (3) regime shifts occurring in
Amsterdam first and then in London. Instead, it appears the London market was essentially the
price setter and Amsterdam investors responded to its signals. Amsterdam prices did have some
impact on London prices, but this was mainly confined to the long run and these effects are
swamped by the large adjustments in Amsterdam.
VI.
CONCLUSION
This paper has built on the work of Neal [1990] by applying modern time-series
techniques to a group of stock prices traded on the London and Amsterdam stock markets over
the period 1723 to 1794. We have shown that, in spite of the primitive communication
23
These results are available from the authors by request. An interesting extension that would provide insights into
market movements and information flows would be to link up the regime changes with specific events from the
historical records. This was done for the American Civil War by Willard et al. [1996], and the War of Spanish
Succession by Wells and Wills [1997]. We leave this topic to other researchers interested in these markets.
24
technology impeding the flow of information between the two financial centers, Bank of
England, East India Co. and South Sea Co. stock prices recorded in London shared common
features with prices determined in Amsterdam on the same day. Prices on both exchanges moved
together in the long run and short run, and seventy-two percent of the turning points on the
London market were matched by changes on the Amsterdam market. It appears that the events in
London were the primary determinant of prices in Amsterdam, but both markets played a role in
the price discovery process.
The South Sea stock is an outlier in many respects, as the cycles and regime shifts in this
asset do not match up well across markets. We attribute this to the relative inactivity in this stock
over the latter half of the century. Nonetheless, the previous conclusions that the Dutch were not
active speculators or that the Dutch were the dominant cause of crises in London must be called
into question.
As instability of capital flows is again an issue today, and speculators are viewed with
much disdain, we, like Neal [1990, 165], are impressed that these markets functioned so well
with so little government intervention or controls on capital flows. Indeed, these markets helped
finance the industrial revolution and the rise of Britain as a world power. The developing nations
of this century may have much to learn from their behavior.
25
Figure 1. London Stock Prices and the difference between Amsterdam and London Prices for
Bank of England Stock, August 1723 to December 1794
300
250
Pe rc e n t o f Pa r
200
E a s t I n d ia
150
Ban k
100
S o u th S e a
50
0
A m s ter d a m m in u s L o n d o n B a n k
-5 0
200
400
600
800
1000 1200 1400 1600
Ob se rv a ti o n
26
Table 1
______________________________________________________________________________
Amsterdam and London Stock Market Returns, 1723 to 1794
Mean
Maximum Minimum Std. Dev. Skewness Kurtosis
Variable
Bank of England Price in Amsterdam (ABOE)
0.016
14.377
-10.845
1.651
0.345
12.064
Bank of England Price in London (BOE)
0.016
13.727
-9.812
1.602
0.059
12.149
East India Co. Price in Amsterdam (AEIC)
0.024
14.760
-12.636
2.232
0.049
9.494
East India Co. Price in London (EIC)
0.023
16.455
-12.883
2.243
-0.159
10.373
South Sea Co. Price in Amsterdam (ASSC)
-0.022
13.645
-26.236
1.838
-1.334
33.483
South Sea Co. Price in London (SSC)
-0.022
10.274
-19.347
1.879
-1.147
18.035
Dow Jones Industrial Avg. 1918 to 1984
0.018
13.800
-14.479
1.145
-0.114
17.575
______________________________________________________________________________
Notes: Summary statistics for percentage returns over the period 8/08/1723 to 12/19/1794 (1676 observations). The
standard error for the skewness estimate under the null hypothesis of normality is 0.06, while that for the kurtosis
estimate is 0.12.
27
Table 2
______________________________________________________________________________
Johansen Cointegration Tests
Eigenvalue
LR
Critical Value
Null Hypothesis
Part A. Test for ABOE and BOE
No cointegrating vectors
0.2225
433.270*
25.32
At most one cointegrating vector
0.0038
6.378
12.25
cointegrating vector = ABOE - 0.984BOE
Part B. Test for AEIC and EIC
No cointegrating vectors
0.2019
386.304*
25.32
At most one cointegrating vector
0.0054
9.024
12.25
cointegrating vector = AEIC - 0.999EIC
Part C. Test for ASSC and SSC
No cointegrating vectors
0.1010
182.070*
19.96
At most one cointegrating vector
0.0031
5.211
9.24
cointegrating vector = ASSC - 0.961SSC
Part D. Test for 6 Variable System
No cointegrating vectors
0.2884 1200.953*
114.90
At most one cointegrating vector
0.2272
632.029*
87.31
At most two cointegrating vectors
0.1002
201.003*
62.99
At most three cointegrating vectors
0.0069
24.507
42.44
At most four cointegrating vectors
0.0059
12.906
25.32
At most five cointegrating vectors
0.0017
2.871
12.25
cointegrating vector 1 = ABOE - 0.980BOE - 0.009EIC + 0.006SSC
cointegrating vector 2 = AEIC + 0.030BOE - 1.018EIC + 0.005SSC
cointegrating vector 3 = ASSC + 0.016BOE + 0.010EIC - 0.976SSC
______________________________________________________________________________
Notes: BOE is the Bank of England stock price, EIC is the East India Co. stock price, and SSC represents the South
Sea Co. stock price, as recorded on the London market. The letter “A” before these designations indicates that the
stock price is take from the Amsterdam market. Cointegration results based on a VECM with a constant, trend, and
3 lags for ABOE/BOE and AEIC/EIC. The VECM for ASSC/SSC and the 6 variable system only included 2 lags, a
constant and a trend. An asterisk denotes significance at the 1% level. Lag lengths determined by the Akaike
information criteria and dummy variables were included in each equation for ex-dividend days.
28
Table 3
______________________________________________________________________________
Estimation Results from Error Correction Models
Dependent Variable:
ABOE
Part A. Bivariate Results
Amsterdam price (-1) -0.075(1.46)
Amsterdam price (-2) -0.066(1.53)
Amsterdam price (-3) -0.035(1.01)
London price (-1)
0.096(1.81)
London price (-2)
0.086(1.89)
London price (-3)
0.041(1.11)
Error Correction Term -0.490(8.75*)
Tests: Amst price=0
2.66
London price=0
4.07
BOE
AEIC
EIC
0.091(1.72)
0.058(1.28)
0.013(0.36)
-0.075(1.36)
0.003(0.06)
-0.016(0.42)
0.183(3.12*)
3.32
3.86
-0.168(3.20*)
-0.048(1.06)
0.014(0.41)
0.219(4.08*)
0.097(2.08)
0.053(1.44)
-0.441(7.74*)
15.13*
18.08*
0.015(0.26)
0.112(2.23)
0.045(1.19)
0.014(0.23)
-0.028(0.55)
0.012(0.30)
0.225(3.58*)
8.15
1.56
-0.047(1.67)
-0.033(1.26)
0.038
2.02
0.118
2.00
0.075
2.02
0.044(0.61)
-0.025(0.44)
-0.029(0.50)
0.090(2.14)
0.048(1.03)
0.033(0.77)
-0.061(0.78)
0.006(0.10)
0.034(0.57)
-0.021(0.46)
0.067(1.74)
0.006(0.16)
-0.115(1.40)
0.285(4.44*)
-0.042(1.55)
1.06
7.84
1.37
1.09
1.41
3.24
0.180(3.19*)
0.016(0.36)
-0.059(1.33)
0.003(0.09)
-0.136(3.81*)
-0.061(1.81)
0.089(1.47)
0.021(0.45)
0.040(0.89)
0.043(1.22)
0.077(2.58*)
-0.027(0.97)
-0.098(1.54)
-0.050(1.02)
-0.171(8.16*)
11.96*
2.54
15.53*
2.47
1.53
10.26*
0.157(2.66*)
0.019(0.42)
-0.017(0.35)
-0.022(0.64)
0.025(0.66)
0.016(0.44)
0.071(1.13)
0.020(0.40)
0.074(1.56)
0.090(2.39)
-0.092(2.95*)
-0.041(1.41)
-0.092(1.37)
0.025(0.49)
0.098(4.47*)
8.09*
0.42
0.53
1.39
5.85
9.08*
0.040
2.03
0.151
2.02
0.105
2.03
adj. R-squared
0.200
0.073
0.207
Durbin-Watson
2.03
2.01
2.02
Part B. Multivariate Results from 6 Variable VECM
ABOE(-1)
-0.059(1.20)
0.075(1.47) 0.091(1.37)
ABOE(-2)
-0.060(1.57)
0.022(0.55) -0.021(0.42)
AEIC(-1)
-0.074(1.92) -0.054(1.34) -0.188(3.62*)
AEIC(-2)
-0.024(0.84) -0.023(0.79) -0.029(0.76)
ASSC(-1)
0.084(2.70*) 0.029(0.91) 0.034(0.81)
ASSC(-2)
0.036(1.25)
0.018(0.59) 0.029(0.73)
BOE(-1)
-0.009(0.16) -0.132(2.41) -0.061(0.86)
BOE(-2)
0.004(0.09) -0.040(0.94) 0.014(0.25)
EIC(-1)
0.110(2.75*) 0.083(2.03) 0.205(3.85*)
EIC(-2)
0.068(2.21)
0.081(2.50) 0.068(1.63)
SSC(-1)
-0.003(0.12)
0.056(2.10) 0.031(0.90)
SSC(-2)
-0.003(0.12) -0.006(0.25) 0.003(0.09)
EC terms: BOE -0.562(10.15*) 0.160(2.75*) -0.012(0.16)
EIC 0.097(2.25)
0.090(2.01) -0.448(7.72*)
SSC -0.049(2.68*) -0.046(2.40) -0.028(1.13)
Tests: ABOE=0
2.77
2.23
3.38
AEIC=0
3.74
1.85
14.63*
ASSC=0
7.73
0.98
0.97
BOE=0
0.08
6.21
1.62
EIC=0
8.10*
6.74
15.92*
SSC=0
0.02
5.39
0.86
adj. R-squared
Durbin-Watson
0.207
2.03
0.085
2.01
0.205
2.02
ASSC
SSC
0.147(4.96*)
0.050(1.80)
0.143(5.09*) -0.024(0.81)
-0.004(0.14) -0.010(0.37)
-0.174(8.57*) 0.107(5.06*)
3.49
24.81*
28.66*
0.68
______________________________________________________________________________
Notes: For variable definitions, see Table 1 or 2. Estimates obtained from iterative seemingly unrelated regressions.
The absolute values of the t-statistics are given in parentheses next to each coefficient. Chi-squared tests for the joint
significance of the lagged terms are given below each equation. An asterisk denotes significance at the 1% level in
each case. Error correction terms (EC terms) are defined in Table 2. A trend, constant, and ex-dividend dummies are
included in each equation, except for the ASSC/SSC equation in Part A where the trend is omitted.
29
Table 4
_________________________________________________________________
Tests For Common Cycles
Null Hypothesis
Eigenvalue Test Stat.
Part A. Cycles in ABOE and BOE
0.0030
4.926
Number of cycles (s) > 0
0.3443
708.001
Number of cycles (s) > 1
Part B. Cycles in AEIC and EIC
0.0056
9.301
Number of cycles (s) > 0
0.3839
816.353
Number of cycles (s) > 1
Part C. Cycles in ASSC and SSC
0.0108
18.130
Number of cylces (s) > 0
0.1164
224.567
Number of cycles (s) > 1
DF
p-value
6
14
0.553
0.000
6
14
0.1573
0.000
4
10
0.001
0.000
__________________________________________________________________
Notes: For variable definitions, see Table 1 or 2. Common cycle results based on canonical correlation analysis of
VECM specifications given in Table 3. Test statistic is defined in equation (4).
30
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