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Efficiency of Markets

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!.

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Efficiency of Markets

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  1. Efficiency of Markets Prof. Leighton Vaughan Williams Professor of Economics and Finance Nottingham Business School Nottingham Trent University

  2. 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.

  3. 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.”

  4. 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.”

  5. 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”???

  6. 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’.)

  7. ‘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.”

  8. 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: • 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? • 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?

  9. 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.

  10. Football US-style Can this game help predict the stock market?

  11. 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).

  12. 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.’

  13. 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%.

  14. 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).

  15. 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’).

  16. “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?

  17. 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.”

  18. Some Suggested Further Reading: • William N. Goetzmann and Ning Zhu, ‘Rain or Shine: Where is the Weather Effect?’, European Financial Management, 2005, 11 (5), 559-578. (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.

  19. 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

  20. 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.

  21. 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.

  22. 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.

  23. 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

  24. 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. 3-18. • Fortune, P. (1991), Stock Market Efficiency: An Autopsy? New England Economic Review, April, pp. 17-40.

  25. 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%.

  26. 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.

  27. 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).

  28. 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.

  29. 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.

  30. 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.

  31. 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.

  32. 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).

  33. 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. 711-719. • 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.

  34. 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.

  35. 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%). 

  36. 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.

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