1. Asset-Pricing Anomalies

2. Outline Classification of Anomalies Calendar Anomalies
Month-of-the-Year Effect, Weekend Effect
Incorrect Market Reaction to Information
Returns Correlation
Contrarian Investing
Momentum Strategies

3. Outline Forecasting Returns on the Basis of a Firm’s Characteristics
Small-Size Effect
Book to Market Equity
Violation of the Law of One Price
Closed-End Funds Puzzle
Twin Stocks
Pricing of Mother- and Daughter-Company Shares

4. Classification of anomalies
The aim of this chapter (CH 4) is to systematize and discuss the wide range of publications on anomalies in asset pricing.
1. Violating the law of one price. We can observe in stock markets three major groups of phenomena where the law of one price is violated. These are close-end funds puzzle, quotations of the so-called twin stocks, and the simultaneous listing of mother- and daughter- company shares. The main reason for which the law of one price is broken is the reduced effectiveness of arbitrage when it comes to redressing incorrect stock valuation.

5. Classification of anomalies
2. Calendar anomalies. Calendar anomalies involve regular patterns in returns occurring over time. A number of studies have shown that returns may be influenced by the month of the year or the day of the week on which they occur.
3. Incorrect market reaction to information. Extensive research has been dedicated to market reacting to information imprecisely. Whether overreaction is more common than underreaction remains debatable.
Underreaction. The evidence include:

6. Classification of anomalies
Positive autocorrelation of indexes and portfolio returns may suggest that new information spreads slowly in the market (e.g., Lo and MacKinlay, 1988, 1990).
The profitability of momentum strategies relying on short-term market trends (Jegadeesh and Titman, 1993, 2001, 2002).
Evidence of post-announcement drift in some event studies. Examples of market underreactions include corporate earnings announcements, stock splits, changes in dividend policies, and stock repurchases.

7. Classification of anomalies
Overreaction. Ample direct evidence also exists to demonstrate market overreactions.
Initial public offerings (IPOs) and secondary equity offerings (SEOs) (Ritter, 1991; Loughran and Ritter, 1995; Spiess and Affleck-Graves, 1995; Lee, 1997). The persistently negative abnormal returns that usually follow are deemed to constitute gradual market adjustments of initial overvaluations.
Negative correlations across long-term returns (Fama and French, 1988; Poterba and Summers, 1988) and the effectiveness of long-term contrarian strategies (De Bondt and Thaler, 1985, 1987).

8. Classification of anomalies
4. Forecasting returns on the basis of a firm’s characteristics. Fama and French (1996) showed that a number of anomalies in this category overlap and may in fact be reduced to just two major phenomena: company-size effect and book-to-market effect.

9. Calendar anomalies Month-of-the-Year Effect
This effect, also called the January effect, is one of the best described anomalies of the seasonal distribution of returns. Many publications demonstrated that average returns are much higher in the first month of the year compared to other months. Furthermore, Roll (1981), Keim (1983), and Reinganum (1983) documented that in the United States the phenomenon is typical for small-cap companies. Gu (2003) suggest that the January effect might just as well affect big companies, and the phenomenon was much weaker over the last two decades of the twentieth century.

10. Calendar anomalies
The most widely known explanation of the January effect is the tax-loss selling hypothesis. At year end, investors sell stocks whose prices fell over the last 12 months in order to deduct the realized loss from their tax base. Supply pressure deflate prices in late December. Early January, investors start buying stocks that are now underpriced due to the December sellout. This activity generates high returns.
Why they would wait to realize losses right up to the last moment in December.

11. Calendar anomalies
This is related to strong loss aversion and the disposition effect. Investors wait until the last moment hoping that the trend will turn allowing them to avoid losses. This attitude may be related to overconfidence and unrealistic optimism.
As psychologically driven biases are more typical for individual investors who usually dominate small companies, supply pressure at year end will mostly affect small-cap stocks.

12. Calendar anomalies
Postponing investment decisions until the beginning of January is in turn caused by the psychological phenomenon of mental accounting.
Supporting evidence
Dyl(1977), Tkac (1999), and Chen and Singal (2000) confirm the hypothesis, finding that shares whose prices fell in the previous year are traded much more often in December (tax pressure) and January (demand pressure). Reinganum (1983) documents the great majority of the total number of stocks sold in order to settle losses are small company shares.

13. Calendar anomalies
Reinganum (1983) documents that the January effect is also observed in the case of small companies whose shares generated profit in the previous year.
Ritter (1988) suggests high returns in the first days of January are caused not only by repurchasing the previously sold shares, but also by investing free cash in other securities that did not necessarily bring a loss in the previous year.
The January effect was also present in countries where there capital profits are not taxed (e.g., Japan—Kiyoshi, 1985) as well as those where the

14. Calendar anomalies
end of the tax year does not correspond to the end of the calendar year (e.g., the United Kingdom— Reinganum and Shapiro, 1987; Hillier and Marshall, 2002; Zhang and Jacobsen, 2013; Australia—Brown et al., 1983).

15. Calendar anomalies Weekend Effect
A number of studies have demonstrated that average returns between session close on Friday and Monday were much lower than the average rates on other weekdays, their typical values being even negative.
The observed negative returns are realized between session close on Friday and the Monday opening or perhaps during the Monday session?
The answer is not definite. See Rogalski (1984a, 1984b), Smirlock and Starks (1986) and Harris (1986).

16. Calendar anomalies
Damodaran (1989) put forward a hypothesis that low Monday returns may be rationally explained by information-related factors. Friday afternoon is the usual time when companies announce negative news.
DellaVigna and Pollet (2005) partially confirm Damodaran’s intuitions.
They document the Friday announcements of financial results are 25 percent more likely to contain a negative surprise.
They also argue that investors attach less importance to the information announced on Fridays.

17. Calendar anomalies
They also observed a strong post-announcement drift after Friday. Insufficient market reaction to information announced during Friday’s trading is also evident in studies carried out by Bagnoli, Clement, and Watt (2004).

18. Incorrect market reaction to information
Returns Correlations
Lo and MacKinlay (1988, 1990) document autocorrelation of portfolio returns and index changes, but not for individual securities.
Significant positive correlation coefficients for short-term returns may suggest that the market is too slow reflecting incoming information.
Lo and MacKinlay (1988, 1990) document that autocorrelation is greater in small-cap portfolios. They also find returns of small-cap portfolios may be predicted on the basis of returns from large-cap portfolios.

19. Incorrect market reaction to information
Slow reaction to information is not the only possible explanation for positive correlation of portfolio returns. We may observe autocorrelation of returns as a product of overreaction to information.
In the long term, market overreaction will be eventually adjusted, triggering long-term negative correlations across returns (Poterba and Summers, 1988; Fama and French, 1988).
Portfolio return correlation might even be totally independent of fundamental news, being linked to changes in investment sentiment among noise traders.

20. Incorrect market reaction to information
We cannot exclude that short-term continuations and long-term reversals of the returns are caused by the behavior of professional players (Lakonishok et al., 1992).
Behavioral finance does not give a straightforward answer as to which of the three scenarios outlined earlier is more responsible for the observed autocorrelation and cross-correlation of returns. Most probably, the market under- and overreacts in different situations.

21. Incorrect market reaction to information
Contrarian Investing
In the strategy of contrarian investing, it is generally recommended to purchase assets that have recently lost a lot of value and sell those whose price has increased.
1. Short-term contrarian investing
Lehmann (1990) constructed the portfolios as purchasing securities that performed worse than the market in the previous week while selling those that brought higher returns than the market average.

22. Incorrect market reaction to information
The weekly return on the portfolio thus created amounted to the average of 1.21 percent and was positive in about 90 percent of weeks in the period studied.
Lehmann interpreted the results as a product of the market reacting too violently to information.
Jegadeesh(1990) tested the contrarian strategy over one month. The portfolio made up of 10 percent of shares that brought the lowest returns in the previous month performed on average 1.99 % better (1.75% excluding January) in the following month than the

23. Incorrect market reaction to information
portfolio containing 10 percent of companies whose results were previously the best.
Jegadeesh and Titman (1995) confirm the profitability of the short-term contrarian strategy both for the monthly and weekly time horizons. They also demonstrate that it works best for small- cap companies. They claim that investors overreact to information related to individual companies, but are too slow to process information that is common for the entire market.

24. Incorrect market reaction to information
The profitability of short-term contrarian strategies was also confirmed on other global markets. In practical terms, the importance of the strategy of short-term contrarian investing is very limited. Given the high frequency of transactions, the amount of transaction costs remains the key.
2. Long-term contrarian investing
De Bondt and Thaler (1985) carried out the following simulation. In the sample period, every three years, there was one portfolio constructed with 35 stocks that performed worst over the previous

25. Incorrect market reaction to information
three years and another with the best performing stocks. Next, a cumulative abnormal return was calculated for each portfolio for the following 36 months.
Portfolios made up of companies whose value increased the fastest in the previous three years, performed 5 percent worse than the market in the following period. On the other hand, portfolios of former losers brought results that were on average percent above the market in the following 36 months.

26. Incorrect market reaction to information
Almost all cumulative growth of the former losers took place in each of the three consecutive Januaries. Nevertheless, January effect is only relevant for the January of the first year after the portfolio was constructed.
The efficiency of long-term contrarian strategies was also demonstrated on markets in other countries. Scowcroft and Sefton (2005) documented the winner–loser effect in the global context analyzing returns on shares making up the MSCI Global Equity Index covering the total of 22 capital markets in developed countries.

27. Incorrect market reaction to information
The explanation for the winner-loser effect
Those who try to explain the winner–loser effect are traditionally divided into efficient market proponents and financial behaviorists.
1. Rational explanation
Chan (1988) and Ball et al. (1995) raise the problem of beta coefficient changeability in time.
French (1992, 1993, 1996) insist that reversals in long-term returns may be caused by additional risk factors going beyond the traditional beta measure.

28. Incorrect market reaction to information
2. Behavioral explanation
Market overreaction (De Bondt and Thaler [1985, 1987]; Lakonishok et al. [1994]). The primary reason is that investors fall victim to the extrapolation bias. Noise traders attach too much importance to historical performance of companies. They are too optimistic extrapolating prospects of those companies that have so far been successful on the market, underestimating the potential of weaker companies.

29. Incorrect market reaction to information
Daniel, Hirshleifer, and Subrahmanyam (1998) suggested further that market overreaction may be related to overconfidence. Investors overestimate the value of the information they have, which makes them react too strongly. Overreaction is, in turn, aggravated by selective attribution.
Results generated by winner and loser portfolios are not symmetrical with respect to the returns on the market portfolio. Excess returns on the loser portfolios are much higher than the absolute returns on winner portfolios.

30. Incorrect market reaction to information
Momentum strategy
Momentum strategy recommends to buy shares of companies whose value has recently increased significantly and to sell those that have lost the most. The key difference with contrarian investing is the time frame in which companies are assessed before portfolios are constructed as well as the length of the holding period.
Jegadeesh and Titman (1993) carried out 32 different trading strategies in which the forming and holding periods varied between 3 and 12 months.

31. Incorrect market reaction to information
The data used came from NYSE and AMEX records
for the years 1965–1989. Each time, they purchased an equal-weighted portfolio made up of a decile of stocks that were most successful in the previous period and sold an equal-weighted portfolio made up of a decile of stocks with the lowest returns. All strategies generated positive returns.
On average, the difference between the portfolio with best and worst companies amounted to percent per month.

32. Incorrect market reaction to information
Jegadeesh and Titman (1993) demonstrated that the differences in profit from individual portfolios cannot be explained by beta coefficients, while Fama and French (1996) showed the three factor model does not help either.
In later studies covering a larger sample, Jegadeesh and Titman (2001) further confirmed the importance of the momentum effect. The momentum effect was also observed outside the American market.

33. Incorrect market reaction to information
The explanation for momentum effect
1. First, there is the slow market reaction to incoming information. Nonetheless, if it was the only systematic irregularity in information processing, the long-term winner–loser effect would be difficult to explain.
2.Momentum effect takes place only as a result of market overreaction.
3.The market may both underreact and overreact to information depending on the time horizon or the type of information signals.

34. Incorrect market reaction to information
Jegadeesh and Titman (2001) showed that the portfolios they constructed based on the momentum strategy bring average negative returns starting from 13 to 60 months from the moment of their construction. This leads Jegadeesh and Titman (2001) to believe that at least part of the momentum effect may be attributed to market overreaction.
4. Chan, Jegadeesh, and Lakonishok (1996) link the momentum effect with the delayed investor response to reported unexpected earnings.

35. Incorrect market reaction to information
They demonstrated that over half of the profit from a six-month-long momentum strategy is realized over several sessions around the days when successive quarterly reports are published.
Investors display cognitive conservatism. They are first wary of unexpectedly good or bad results achieved by a company and are not able to immediately estimate the probability of the company continuing the good or bad trend. Their optimism or pessimism gets gradually stronger (in the case of the latter, the process is slower) until eventually the extrapolation bias makes it irrationally powerful.

36. Incorrect market reaction to information
5. Grinblatt and Han (2005) present a model in which the disposition effect comes in as an explanation for the momentum effect.
Most of the mentioned publications document asymmetrical behavior of momentum portfolios. Returns on short positions are higher than those from long positions. This is yet another evidence that the market reacts slower to bad news.
Another question about the momentum effect is that whether the effect is the result of a wrong reaction to company-specific or systemic information.

37. Incorrect market reaction to information
Moskowitz and Grinblatt (1999), O’Neal (2000), as well as Swinkels (2002) document industry momentum. A portfolio made up of industries that have performed best (worst) over the previous six months will continue the good (bad) trend in the following half year.
The momentum effect may be also potentially triggered or at the very least strengthened by activities of professional market players.
Grinblatt, Titman, and Wermers (1995) and Chen, Jegadeesh, and Wermers (2002) demonstrate that

38. Incorrect market reaction to information
investment funds are more likely to buy previously
rising shares, selling those that lost value.
Sias (2004) and Fong, Gallagher, Gardner, and Swan (2005) point out that there is a strong tendency for herd behavior among fund managers, but it is directed not so much at the same assets as companies from the same industry.