Momentum in the UK Stock Market(英國金融學畢業dissertation由英國dissertation網-留學生dissertation代寫中心提供）
by
Mark Hon
and
Ian Tonks
January 2001
Discussion Paper No. 01/516
Department of Economics, University of Bristol, 8, Woodland Road, Bristol BS8
MOMENTUM IN THE UK STOCK MARKET
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Momentum in the UK Stock Market
Abstract
This paper investigates the presence of abnormal returns through the use of trading strategies that exploit the predictability of short run stock price movements. Based on historical returns of the largest set of individual securities in the UK stock market examined to date, this paper identifies profitable momentum trading strategies as
investment tools over the period 1955-96. Our results show that returns on trading strategies cannot be accounted for by a simple
adjustment for beta-risk. Although we find evidence of size effect in the UK stock market, this phenomenon cannot explain the
momentum profits. However the paper finds that these profitable investment strategies are only apparent in the sub-sample 1977-96,
and are not present in the earlier 1955-76 period. The implication is that momentum is not a general feature of the UK stock market, but is only apparent over certain time periods.
JEL Classification
Keywords: Momentum strategies, Contrarian Strategies
MOMENTUM IN THE UK STOCK MARKET
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Momentum in the UK Stock Market
1. Introduction
Recently there has been much work on the profitability of trading strategies in
stock markets. This work stands in stark contrast to the previously well-accepted
doctrine of the efficient markets hypothesis. Under the null hypothesis of weak-form
market efficiency, the performance of portfolios of stocks should be independent of
past returns. However empirical research has shown that asset returns tend to exhibit
some form of positive autocorrelation in the short to medium term; but mean-revert
over longer horizons. There are two prevalent types of trading methodologies used to
take advantage of serial correlation in stock price returns: momentum trading and
contrarian strategies. At one end of the spectrum, momentum strategies rely on shortrun
positive autocorrelation in returns and generates abnormal profits by buying past
winners and selling past losers. Liu, Strong and Xu (1999) report on the profitability
of momentum strategies in the UK over the period 1977-96. In contrast contrarian
strategies are based on negative serial correlation in stock prices such that selling
winners and buying losers generates abnormal profits.
The current paper assesses the profitability of momentum strategies on the UK
stock market using the most comprehensive set of data available to date. This is
important since any rejection of the efficient markets hypothesis, may be a
consequence of short span of data, and raises the question as to whether the#p#分頁標題#e#
documented rejection of the efficient markets hypothesis is a property of the sample or
whether it is a more detailed empirical regularity. In fact Liu, Strong and Xu (1999)
argue that their momentum results are robust across two sub-samples in their dataset .
However we find that extending the data on UK returns back to 1955, the momentum
effects apparent from 1977 onwards do not exist in the earlier period 1955-76. The
next section presents an overview of both the theoretical and empirical literature on
serial correlation in stock prices. Section III describes the data set in detail, Section
IV covers the methodology and safeguards applied to this study, Section V presents
the empirical results, Section VI explores the empirical findings after controlling for
risk. Section VII further examines the effect of size on the empirical results, and
MOMENTUM IN THE UK STOCK MARKET(英國金融學畢業dissertation由英國dissertation網-留學生dissertation代寫中心提供）

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Section VIII holds the conclusion to the overall findings of this paper.
2. Literature Review
In recent years, there has been a surge of articles on the predictability of asset
returns based on historical returns. DeBondt and Thaler (1985, 1987) identified long
run return reversals; which suggest that contrarian strategies of selling past winners
and buying past losers generates abnormal returns.1 Other papers [Fama and French
(1988), Lo and MacKinlay (1988), Porterba and Summers (1988) and Jagadeesh
(1990)] also documented evidence of negative serial correlation in long horizon stock
returns, but positive correlation at shorter intervals2. Positive autocorrelation at shorttime
intervals suggests that momentum strategies might yield profitable trading
opportunities. Jegadeesh and Titman (1993, 1995) document significant positive
returns when stocks are bought and sold based on short-run historical returns. Firms
with higher returns over the past 3- to 12- months subsequently outperform firms with
lower returns over the same period. Using data from the NYSE and stocks listed on
the American Stock Exchange (AMEX) from 1965 to 1989, they ranked stocks in an
ascending order based on their past 3- to 12- month returns. Based on this ranking,
ten equally-weighted deciles of stock portfolios are formed. The top decile is
classified as the ‘loser’ decile and the bottom decile is known as the ‘winner’ decile.
In each overlapping period, the strategy was to buy the winner decile and sell the loser
decile with holding periods of 3- to 12- months. Abnormal returns were documented
with this trading strategy; however, the profits generated in the first year after
portfolio formation dissipates in the following two years. In addition Grinblatt and
Titman’s (1989) paper indicated the success of mutual funds which use momentum#p#分頁標題#e#
strategies as an investment tool for selecting stocks. Grundy and Martin (1998), use
the Fama-French three factor risk-adjusted returns model to document profitability of
1 For the UK Power, Lonie and Lonie (1991), MacDonald and Power (1991), and Dissanaike (1997)
find that contrarian strategies based on monthly returns of UK companies yield abnormal profits.
Though Clare and Thomas (1995) using randomly selected UK annual returns data from the period of
1955 to 1990 conclude that the documented overreaction was a manifestation of small firm effect.
2 These findings are contentious, and a number of arguments have been suggested that would
reduce the profitability from exploiting these contrarian patterns: risk [Chan (1988), Ball and Kothari
(1989), Fama and French (1996)], size effects [Zarowin (1990)], microstructure effects [Kaul and
Nimalendran (1990), Lo and Mackinlay (1990)].
MOMENTUM IN THE UK STOCK MARKET
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more than 1.3 percent per month using momentum strategies on NYSE and AMEX
stocks over the period 1966 to 1995. Moskowitz and Grinblatt (1999) document
strong momentum effect across industries. When stocks from past winning industries
are bought and stocks from past losing industries sold, the strategy appears to be
highly profitable, even after controlling for cross-sectional dispersion in mean returns
and likely microstructure differences. Conrad and Kaul (1998) in a study on NYSE
and AMEX securities between the period of 1926 to 1989 document the success of
contrarian strategies at long horizons and momentum trading strategies at medium
horizons. Chan, Jegadeesh and Lakonishok (1996) find that momentum effects are
distinct from post-earnings announcement drift.
The momentum anomaly is not confined to the US. Rouwenhourst (1998) tested
the profitability of momentum strategies in international equity markets. Monthly
total returns from 12 European countries during the period 1980 to 1995 are used to
form relative strength portfolios. After correcting for risk, it was found that winner
portfolios outperform loser portfolios by more than 1 percent per month and the
overall returns all momentum portfolios are significantly correlated to findings for the
US market by Jegadeesh and Titman (1993). Using monthly returns from stock
indices of 16 countries for the period of 1970 to 1995, Richards (1997) find that the
momentum effect is strongest at the 6-month horizon with an annual excess return of
3.4 percent. For horizons longer than one year, ranking period losers begin to
outperform winners with an average annualised excess returns of more than 5.8
percent. A recent paper by Liu, Strong and Xu (1999) show the presence of
momentum profits using weekly UK stock prices over the period of January 1977 to
December 1996. Controlling for systematic risk, size, price, book-to-market ratio, or#p#分頁標題#e#
cash earnings-to-price ratio did not eliminate momentum profits. They further
conclude that the momentum effect is derived from market underreaction to firmspecific
information.
It is apparent from the above studies that over short to medium-term (i.e., 3- to
12- month) horizons, momentum strategies are most profitable; while contrarian
strategies prove to be more profitable over the very short-term (i.e., 1- to 4- week) and
long-term (i.e., 36- to 60- month) horizon.
MOMENTUM IN THE UK STOCK MARKET
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While most of the empirical works point to some level of predictability in stock
returns, there is widespread disagreement about the underlying explanation for this
type of predictability. A number of theoretical models of investor behaviour have been
proposed to explain these serial correlation properties in stock prices. Daniel,
Hirshleifer and Subrahmanyam (1998) base their theory on investor overconfidence,
and changes in confidence arising from biased self-attribution. DeLong et al. (1990)
and Jegadeesh and Titman (1993) point out that positive feedback traders tend to force
prices of equities to overreact and move away from their long-run values temporarily
as these “trend-chasers” reinforce stock price movements even in the absence of
fundamental information. Baberis, Shleifer and Vishny (1998) present a model
consisting of a representative investor who believes that earnings tend to move
between two different “states” or “regimes” (i.e., earnings either mean-revert or
trend); even as earnings follow a random walk in the model. Berk, Green, and Naik
(1999) suggest that when firms exploit advantageous investment opportunities, they
tend to change their non-systematic risks in a predictable manner, which will generate
predictable patterns in returns. Hong and Stein (1999) propose a model which focuses
on externalities that result from interactions between heterogeneous agents rather than
the psychology of the representative agent. In a follow up paper Hong, Lim and Stein
(2000), test this model and conclude that firm size and residual analyst coverage play
an important role in determining profitability in momentum strategies.
3. Methodology and Data
The aim of this paper is to test the null hypothesis of weak form stock market
efficiency that states that time series returns are independent over any time horizon.
That is, when returns are defined over short intervals, they are serially independent
under the Efficient Market Hypothesis (EMH). To the extent that the null hypothesis
is rejected, returns observed over different time intervals will display some form of
serial correlation or predictability based on the prescribed set of trading strategies.
Portfolios are formed on the basis of past returns. The top batch of the ranked and#p#分頁標題#e#
sorted stocks is labelled as the ‘loser’ portfolio and the bottom ‘winner’ portfolio.
Momentum strategies form portfolios on the basis of past short-run returns, by buying
winner portfolios and selling loser portfolios.
MOMENTUM IN THE UK STOCK MARKET
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We test the empirical implications of forming winner-loser portfolios in the UK
market which involve the simultaneous sale of loser stocks and the purchase of winner
stocks; hence resulting in zero net investment. The efficiency market hypothesis
predicts that these winner-loser portfolios will yield zero profits. However if asset
prices exhibit mean-reversion or overreaction, the winner-loser portfolios will
generate profits over some horizons in the sample period.
The test for the profitability of momentum trading strategies in the paper will be
based on the methodology used by DeBondt and Thaler (1985, 1987) and Jegadeesh
and Titman (1993)3. These papers assess the profitability of JxK trading strategies,
where securities are assigned to portfolios according to a ranking in period t based on
the previous J months’ returns. In month t, we form a winner-loser portfolio, where an
investor goes short on the loser portfolio and takes on a long position on the winner
portfolio for the following K month horizon. Thus, based on J months of historical
data, portfolios are held on for horizon of K months after being executed in month t.
Jegadeesh and Titman (1993) classify the top decile of performing stocks as winners
and the bottom decile as losers. Of course different definitions of winners and losers
may actually produce significantly different results. We follow the Jegadeesh and
Titman (1993) and Liu, Strong and Xu (1999) methodology of decile portfolios.
This paper examines a large sample of historical returns from January 1955 to
December 1996 of all companies on the London Business School London Share Price
Database (LSPD) tape. This tape consists of all companies quoted on the London
Stock Exchange since 1975. For the period before 1975 the file is made up of a
number of different samples. As well as a random sample of 33% of the companies
quoted on the Exchange between 1955 and 1974, there are 33% of new issues in each
year 1955-74. The tape also includes the 500 largest companies by market value in
January 1955, and the 200 largest in December 1972, plus all 100 companies in the
brewing industry. There are a total of 1,571 securities in the sample starting in January
3 This study will be based on log returns instead of raw returns. Conrad and Kaul (1993) and Ball et al.
(1995) point out that results documented by DeBondt and Thaler (1985, 1987), Chan (1988), Ball and
Kothari (1989), and Chopra et al. (1992), suffer from measurement errors as raw returns were used in
MOMENTUM IN THE UK STOCK MARKET#p#分頁標題#e#
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1955, and as securities enter and leave the Exchange over the next 40 years, there are
over 6,600 securities in total over the entire sample period. Portfolios will be formed
based on historical share price performance and the monthly stock returns data are
also taken from the LSPD tapes.4
For every stock i on the LSPD tape without any missing values between test
intervals, an equally weighted portfolio of losers and winners are formed based on
cumulative monthly returns. The procedure is repeated up to 64 times (i.e., once for
each J x K trading strategy) using non-overlapping observations starting January 1955
to December 1996.
Securities are selected based on their returns over the past 3 to 24 months.
Holding periods examined will also vary from 3 to 24 months. The trading strategy
consists of three basic steps. First, individual stocks are ranked according to
Cumulative Continuous Returns (CCR) for each stock i on past J months of
continuously compounded monthly returns in the initial portfolio formation period.
å-
=-
=
J
t
i it CCR R
1
where
ORJ >  @ = + -