Efficiency of Markets
Prof. Leighton Vaughan Williams
Professor of Economics and Finance
Nottingham Business School
Nottingham Trent University
Sell in May, Go Away, Buy Again on St.
Leger Day!
•The St Leger
Stakes, established
in 1776, is the
oldest of Britain’s
five Classic horse
races. It is also the
last to be run each
year, and over the
longest distance. It
is held on the 2nd
Saturday in
September, at
Doncaster
racecourse.
The Halloween Indicator, ‘Sell in May and Go Away:
Another Puzzle’ – Ben Jacobsen and Sven Bouman, 2002,
American Economic Review, 92(5), 1618-1635, December.
• “According to these words of
market wisdom, stock market
returns should be higher in the
November-April period than those
in the May-October period ... We
find this ... To be true in 36 of
the 37 developed and emerging
markets studied in our sample.
The Sell in May effect tends to be
particularly strong in European
countries and is robust over time.
Sample evidence ... Shows that
in the UK the effect has been
noticeable since 1694.”
May 2006: S&P index down 3%; Nikkei
225 down 9%.
•Forbes, June 6: “axiom
‘sell in May’ worked like a
charm.
•Financial Times, July 14:
“this year ‘sell in May and
go away’ would have been
a great strategy”.
•Economist, May 25: the
‘sell in May’ adage was “an
explanation of why
investors the world over
have been selling shares
since May 11th.”
So what if you sold your shares on May
1st, 2009?
• On May 1, FTSE 100 stood at
4,243.
• Market closed prior to St
Leger day at 5,011.
• You would have missed out
on an 18% rise in the index of
leading UK stocks. FTSE All
Share Index up 19%.
• “At the Ides of March Buy
Away, Load ‘em up till
Lehman’s day”???
Recent historical record: Over 10 years to
2009, market fell six times, rose four times
between May 1 and St Leger day. Since
1984, the market has fallen on average
0.7% between May and September. 2002
down 28%; 2008 down 19.5%, according to
investment website ‘The Motley Fool’ – see
Telegraph, Sept. 14, 2009 (‘FTSE 100 index
hits a 2009 high despite sluggish start’.)
‘Do Shares Really Suffer a Summer
Slowdown?’ Independent, Sept. 11, 2009.
• Robert Parkes, equity strategist at HSBC, has gone back further, looking
at how the market performed between the middle of May and the middle
of September over 29 years. It rose on 18 occasions, falling on just 11.
“This year is a prime example of why you shouldn’t follow that advice:, he
says. “It is true to say that the summer months do tend to be weaker but
I think it would be very dangerous to make investment decisions on a
saying that says ‘sell in May, go away.”
Is there a summer slowdown?
Does the St Leger Day adage offer any
useful investment advice?
How useful is the Halloween indicator as
an investment guide?
Queries:
1.
Assuming you hold a portfolio of
shares at the beginning of May next
year, would you prefer, if forced to
choose, to SELL some of your
shares or to BUY some more?
2.
If you SELL (BUY) in May, when
later in the year would you choose
to BUY (SELL), if forced to specify a
date in 2010 in advance?
Some Suggested Reading:
Mark Hulbert, ‘Reports of its death
exaggerated. Commentary: No
statistical reason to bet against
Halloween indicator.”
(MarketWatch, Oct 2, 2009).
Wikipedia: Halloween Indicator;
note the reference section, inc.
Jacobsen and Bouman, 2002.
Sell in May and Go Away –
Summer Break also at the Russian
Stock Market? Peter Reichling and
Elena Moskalenko, Jan 2007.
Football
US-style
Can this game
help predict
the stock
market?
Can the Super Bowl Predict the Stock Market?
• An Examination of the Super Bowl Stock Market Predictor, by
Thomas M. Krueger and William F. Kennedy, Journal of Finance,
1990, 45 (2), 691-697.
• “Few prediction schemes have been more accurate, and at the same
time more perplexing, than the Super Bowl Stock Market Predictor,
which asserts that the league affiliation of the Super Bowl winner
predicts stock market direction. In this study, the authors examine the
record and statistical significance of this anomaly and demonstrate that
an investor would have clearly outperformed the market by reacting to
Super Bowl game outcomes.” (Abstract).
• ‘If the Super Bowl is won by a team from the old National Football
League (now the NFC), then the stock market is very likely to finish the
year higher than it began. On the other hand, if the game is won by a
team from the old American Football League (now the AFC), the market
will finish lower than it began.’
• (NB Some AFC teams count as NFL wins because they originated in the
old NFL, i.e. Pittsburgh Steelers, Baltimore Ravens (formerly Cleveland
Browns, Baltimore/Indianapolis Colts).
Krueger and Kennedy’s findings
• Over the 22-year history of the Super Bowl to the date of submission of
their study in 1988, they documented a 91% accuracy rate for their
predictor.
• What happened in 1989? The NFC team, San Francisco 49ers, beat the
AFC’s Cincinnati Bengals– the stock market rose 27%.
• Further confirmation of an idea first proposed by New York Times
sportswriter Leonard Koppett, published as ‘The Super Bowl Predictor’ by
investment advisor Robert H. Stovall in the January 1988 issue of
‘Financial World.’
What happened in 1990?
• The NFC’s San Francisco 49ers won a second consecutive victory,
beating the AFC’s Denver Broncos, by 55 points to 10.
• But the stock market fell in 1990, by 4.3%.
But then the Super Bowl Predictor Returned
to Form
• Correctly predicted the direction of the stock market in 1991, 1992,
1993, 1994, 1995, 1996, 1997.
• Since the launch of the Super Bowl this makes for 28 correct predictions
out of 31!!! (a success rate of 90.3%).
• In 2003, Thomas Krueger wrote a follow-up paper with John Sheppard,
in which they conclude:
• “What does appear to be certain is that there is a relationship between
which team wins the Super Bowl and the performance of the stock
market during that year ... This relationship is significant on both
statistical and economic grounds.”
• Title of paper: “An Examination of the Super Bowl Stock Market
Predictor: Unique Factor or Fictitious Correlation.” (Sheppard and
Krueger).
But not so fast!
• The Super Bowl Predictor has predicted correctly only about half the
time since 1997.
• In 2008 the success of the NFC’s New York Giants should have presaged
a stock market surge! Not so, big time!
• In 2009 the Pittsburgh Steelers beat the Arizona Cardinals by 27 points
to 23. But it made no difference to the forecast of a good year for the
stock market, as both teams have their origins in the old NFL. The very
late touchdown changed nothing.
• Summary by Robert Stovell, a strategist for Wood Asset Management in
Sarasota, Florida, and an early champion of the Stock Market Indicator:
• “Nothing seems to be working anymore {in the stock market]”. Used to
be, I was only happy when it was over 90% (accurate), and when it was
still above 80% I was pleased. But certainly 79% is still far above a
failing grade.” (quoted on January 12, 2009, in MarketBeat (WSJ.com’s
‘inside look at the markets’).
“I need to laugh, and when the sun is out,
I’ve got something I can laugh about. I feel
good in a special way. Good Day Sunshine.”
• Can these lyrics help us predict the stock market?
Does the Weather on Wall Street affect stock
prices?
• ‘Stock Prices and Wall Street Weather’, by Edward M. Saunders,
American Economic Review, 1993, 83 (5), 1337-1345.
• “The weather in New York City has a long history of significant
correlation with major stock indexes ... Investor psychology influences
asset prices ... [these findings] cast doubt on the hypothesis that
security markets are entirely rational.”
• ‘Good Day Sunshine: Stock Returns and the Weather’, released 2001 by
David Hirshleifer and Tyler Shumway. Published Journal of Finance,
2003, 58 (3), June, 1009-1062.
• Using a different data set, examining the relation between morning
sunshine and stock returns at 26 stock exchanges, they find that
“Sunshine is strongly correlated with daily stock returns. There were
positive net-of-transaction costs profits to be made from substantial use
of weather-based strategies.”
Some Suggested Further Reading:
• William N. Goetzmann and Ning Zhu, ‘Rain or Shine: Where is the
Weather Effect?’, European Financial Management, 2005, 11 (5), 559578. (Discussion of the possible source of this effect, notably the
relative influence of market-makers [i.e. price setters] and traders).
• ‘Is it the Weather?’, A Comment on the Studies Linking Weather and
Stock Market Behaviour’, Journal of Banking and Finance, 2008, 32 (4),
526-540, Ben Jacobsen and Wessel Marquering.
• ‘Is it the Weather? Comment’, Journal of Banking and Finance, 2009, 33
(3), 578-582, Mark Kamstra, Lisa Kramer and Maurice Levi.
• ‘Is it the Weather? Response’, Journal of Banking and Finance, 2009, 33
(3), 583-587.
• The JoBF exchange includes a discussion of the impact (or absence of
impact) of SAD (seasonal affective disorder) on stock returns.
Some more suggested reading
• Keef and Roush, ‘Influence of Weather on New Zealand Financial
Securities,’ Accounting and Finance, 45 (3), November 2005, 415-437.
• Keef and Roush, ‘The Weather and Stock Returns in New Zealand’,
Quarterly Journal of Finance and Accounting, Winter 2003.
• Loughran and Schultz, ‘Weather, Stock Returns, and the Impact of
Localized Trading’, Journal of Financial and Quantitative Analysis, 2004.
• Pardo and Valor, ‘Spanish Stock Returns; Where is the Weather Effect’,
European Financial Management, 2003, 9 (1), 117-126.
• Trombley, M., ‘Stock Prices and Wall Street Weather: Additional
Evidence’, Quarterly Journal of Business and Economics, 36 (3),
Summer 1997, 11-21.
• Symeonidis, Daskalakis and Markellos, ‘Does the Weather Affect Stock
Market Volatility?’, Working paper, October 12, 2008, Available at
http://ssrn.com/abstract=1283169
Specialist reading
• ‘Weather Effects on Returns: Evidence from the Korean Stock Market’,
Seong-Min Yoon and Sang Hoon Kang, Physica A: Statistical Mechanics
and its Applications, 388 (5), March 2009, 682-690.
• ‘Weather Effects on Returns and Volatility of the Shanghai Stock
Market’, Kang, Jiang, Lee and Yoon, Physica A: Statistical Mechanics and
Its Applications, 389 (1), January 2010, 91-99, available online from
Sept. 2009.
• ‘Are Stock Market Returns Related to the Weather Effects? Empirical
Evidence from Taiwan’, Chang, Nieh, Yang and Yang, Physica A:
Statistical Mechanics and Its Applications, 364, May 2006, 343-354.
Does physiology affect profitability?
• Coates and Herbert (2008) report the findings of a study in which they
sampled, under real working conditions, endogenous steroids from a
group of male traders in the City of London.
• They found that a trader’s morning testosterone level predicts his day’s
profitability.
• They also found that a trader’s cortisol rises with both the variance of
his trading results and the volatility of the market.
• Their results suggest that higher testosterone may contribute to
economic return, whereas cortisol is increased by risk.
• The authors argue that since testosterone and cortisol have cognitive
and behavioural effects, it is possible that high market volatility may
shift risk preferences and even affect a trader’s ability to engage in
rational choice.
Reference
J.M. Coates and J. Herbert (2008), ‘Endogenous steroids and financial risk
taking on a London trading floor’, Proceedings of the National Academy
of Sciences of the United States of America, 15, (16), 6167-6172.
MARKET 'ANOMALIES‘
• Market anomalies are conditions in a financial market which
systematically offer the opportunity of earning above-average or
abnormal returns.
• e.g.
• Do stocks perform better at particular times of the year, or at particular
times of the week?
• Do the shares of smaller firms perform better than those of larger firms?
• Do shares perform better when the weather is good?
• The Small Firm effect
• The January effect
• The Weekend effect
The Small Firm Effect
• The small firm effect refers to the tendency displayed by smaller firms
to outperform larger firms.
• Early academic evidence of this was reported by Banz (1981), who
identified a negative correlation between the average return to stocks
and the market value of the stocks. Fortune (1991) compared the
accumulated values of two investments notionally made in January,
1926, the first in a portfolio represented by the Standard and Poor 500
(S&P 500) and the second in a portfolio of small-firm stocks. He
reported that the latter portfolio significantly outperformed the former.
• Banz, R.W. (1981), The Relationship Between Return and Market Value
of Common Stocks, Journal of Financial Economics, March, 9 (1), pp. 318.
• Fortune, P. (1991), Stock Market Efficiency: An Autopsy? New England
Economic Review, April, pp. 17-40.
The January Effect
• The January effect is the idea that stock performance improves
or is unusually good in January.
• Traceable to work by Rozeff and Kinney (1976).
• Rozeff and Kinney reported a 3.5% stock return average in January,
compared with 0.5% in other months.
• Keim (1983) calculated the return to a portfolio of stocks of small firms
in various months, concluding that it was significantly larger in January
than the rest of the year.
• Guletkin and Guletkin (1983) studied January return patterns in 17
countries including the US. They found much higher returns in January
than other months for all the countries they studied.
• Kato and Shallheim (1985) examined the relationship between size and
the January effect for the Tokyo stock exchange. They find higher
returns in January and a strong relationship between return and size,
the smallest firms returning 8% and the largest less than 3%.
References
• Kato, K. and Shallheim, J. (1985), Seasonal and Size Anomalies in the
Japanese Stock Market, Journal of Financial and Quantitative Analysis,
20, 2, June, pp. 243-260.
• Keim, D.B. (1983), Size-Related Anomalies and Stock Return
Seasonality: Further Empirical Evidence, Journal of Financial Economics,
12 (1), June, pp. 13-32.
• Guletkin, M.N. and Guletkin, N.B. (1983), Stock Market
Seasonality: International Evidence, Journal of Financial Economics, 12,
pp. 469-481.
• Rozeff, M.S. and Kinney, Jr., W.R., Capital Market Seasonality: The
Case of Stock Returns, Journal of Financial Economics, 3, pp. 379-402.
The Weekend Effect
• The original weekend effect, traceable to findings by Cross (1973) is the
proposition that large stock decreases tend to occur between the Friday
close and the Monday close. Other seminal studies include:
• Gibbons and Hess (1981) found, for the period 1962-1978 that
Monday's return was -33.5% on an annualized basis. This pattern was
confirmed even when splitting the data set into two separate samples.
• Harris (1986), in a study of data covering the period December 1981
to January 1983, confirmed the large negative Monday return.
• Half of this fall occurred between the close on Friday and the opening of
business on the following Monday, and most of the remaining decline
occurred in the first 45 minutes of trading.
• N.B. Although the January and weekend effects are the best
documented of the calendar effects, they are not the only ones. There
is, in particular, also significant evidence of a 'holiday effect' (see, for
example, Ariel (1990), Liano and White (1994).
References
• Ariel, R.A. (1990), High Stock Returns Before Holidays: Existence and
Evidence on Possible Causes, Journal of Finance, 45, pp. 1611-1626.
• Gibbons, M.R. and Hess, P.J. (1981),
• Day of the Week Effects and Asset Returns, Journal of Business, 54, pp.
579-596.
• Liano, K. and White, L.R. (1994),
• Business Cycles and the Pre-Holiday Effect in Stock Returns, Applied
Financial Economics, 4, pp. 171-174.
• Harris, L. (1986),
• A Transaction Data Study of Weekly and Intradaily Patterns in Stock
Returns, Journal of Financial Economics, 14, May, pp. 99-117.
The Winner’s Curse and Loser’s Blessing
• The so-called winner's blessing and loser's curse anomaly was first
developed by De Bondt and Thaler (1985, 1987, 1990).
• Traceable to work by Kahneman and Tversky (1973, 1982) on the
psychology of decision-making, which reported that individuals, in
revising their beliefs, tend to overweight fresh information and
underweight prior data.
• De Bondt and Thaler (1985) used monthly data for NYSE stocks,
January 1926 to December 1982.
• Conclusion: After test periods of 36 and 60 months after portfolios
formation, portfolios of prior 'losers' (stocks that have experienced a
recent reduction in their price/earnings ratio) performed significantly
better than portfolios of prior winners.
• This is evidence in favour of a contrarian trading strategy.
References
• De Bondt, W. and Thaler, R. (1985), Does the Stock Market
Overreact? Journal of Finance, July, 40 (3), pp. 793-805.
• De Bondt, W. and Thaler, R. (1987), Further Evidence on Investor
Overreaction and Stock Market Seasonality, Journal of Finance, July, 42
(3), pp. 557-581.
• De Bondt, W. and Thaler, R. (1990), Do Security Analysts
Overreact? American Economic Review, 80 (Papers and Proceedings),
pp. 52-57.
• Kahneman, D. and Tversky, A. (1973), On the Psychology of
Prediction, Psychological Review, 80, pp. 237-251.
• Kahneman, D. and Tversky, A. (1982), Intuitive Prediction: Biases
and Corrective Procedures, In Kahneman, D., Slovic, P. and Tversky, A.
(eds.), 1982, Judgement under Uncertainty: Heuristics and Biases,
Cambridge: Cambridge University press, pp. 414-421.
The Value Line effect
• The Value Line Investment Survey produces reports on several
hundred publicly traded firms, listing their stocks on a scale from 1 to 5
in order of their desirability of purchase (their 'timeliness'). Studies
dating back to Black (1973) reported that more 'timely' stocks, as
defined by Value Line, generated significantly higher returns than less
'timely' stocks.
• Holloway (1981) compared the results of active and passive trading
strategies based on the Value Line recommendations.
• Active = change stocks before year-end if downgraded in order of
'timeliness'. Passive = Buy and hold.
• The active strategy generated higher returns than the passive strategy,
but the advantage was reversed net of transactions costs.
• Stickel (1985) also identified information contained in Value Line
recommendations which was not reflected in prices, although this was
stronger for small stocks.
Explaining the Value Line effect?
• Lee and Park (1987) contend that the 'better' stocks, as assessed by
Value Line, were also the most risky (volatile) relative to the market.
• Such stocks earned a higher return, therefore, but this was simply fair
compensation for the additional risk. As such, the 'Value Line' effect
was totally consistent with the Efficient Markets Hypothesis.
• Fama (1991) placed the findings within a general theoretical
perspective, arguing that
• "… because generating information has costs, informed investors are
compensated for the costs they incur to ensure that prices adjust to
information. The market is then less than fully efficient … but in a way
that is consistent with rational behaviour by all investors." (p. 1605).
References
• Black, F. (1973), Yes, Virginia, There is Hope: Tests of the Value Line
Ranking System, Financial Analysts Journal, 29, Sept/Oct, pp. 10-14.
• Holloway (1981), A Note on Testing an Aggressive Investment
Strategy using Value Line Ranks, Journal of Finance, 36, June, pp. 711719.
• Stickel, E. (1985), The Effect of Value Line Investment Survey Rank
Changes on Common Stock Prices, Journal of Financial Economics, 14,
pp. 121-144.
• Lee, C.F. and Park, H.Y. (1987), Value Line Investment Survey Rank
Changes and Beta Coefficients, Financial Analysts Journal, Sept/Oct, pp.
70-72.
• Fama, E.F. (1991), Efficient Capital Markets II (1991), Journal of
Finance, 46, December, 1575-1617.
Further reading
• David Porras and Melissa Griswold (2000), ‘The Value Line Enigma
Revisited’, Quarterly Journal of Finance and Accounting, Autumn,
available at FindArticles.com
• Choi, J.J. (2000), ‘The Value Line Enigma: The Sum of Known Parts’,
Journal of Financial and Quantitative Analysis, 35, 485-498.
• Zhang, Y., Nguyen, G.X. And Le, S.V., ‘Yes, The Value Line Enigma is
Still Alive: Evidence from Online Timeliness Rank Changes’, The
Financial Review, forthcoming, available online.
Dartboard analysis
• Stael von Holstein (1972) and Yates, McDaniel and Brown (1991)
suggest that so-called 'experts' are not in fact able to outperform a
random dart-throwing approach to stock-picking. In an analysis of
dartboard contests surveyed between January 1990 and December
1992, Metcalf and Malkiel (1994) reported that the experts beat the
market 18 times out of 30 (yielding a total return of 9.5%), while the
'darts' beat the market 15 times (yielding a total return of 6.9%).
But...
• Metcalf and Malkiel failed to reject the hypothesis that the experts won
by chance at conventional levels of significance. The 'superior
performance' of the professionals is in any case explained by MM as a
consequence of the tendency of the 'experts' to choose riskier (more
volatile) stock than would a random approach, and also to a favourable
publicity or announcement effect. The stock chosen by the professionals
was in fact 40% more volatile than the market, compared to just 6% for
the darts. Adjusting for the risk, they concluded that the margin of
superiority of the professionals fell to just 0.4 per cent, and that ignores
any announcement effects.