DUAL BETAS FROM BULL AND BEAR MARKETS: REVERSAL OF THE SIZE EFFECT

Significant firm-size-related differences in abnormal returns and systematic risks occur in bull and bear market months from 1926 to 1988. Potential differential return premiums between recessions and expansions appear to be captured by the varying risk model and not the constant risk model. Using a dual-beta market model to adjust for risk differences in bull and bear markets, we find that large firm stocks on average earn significant positive excess returns and small firm stocks earn significant negative excess returns. Superior performance of large firm stocks is even more pronounced outside January.     

New Evidence on The January Effect Before Personal Income Taxes

We examine the returns of stocks in Cowles Industrial Index before and after the introduction of personal income taxes in 1917. This is distinct from earlier studies because we cross-sectionally analyze the relationship between the returns of the individual stocks and measures of tax-loss selling potential and size. We find that excess returns at the turn-of-the-year and for the month of January were not significant until after 1917. These results provide strong support for the tax-loss selling hypothesis as an explanation for the January seasonal in the returns of small firms.     

The Role of Beta and Size in the Cross-Section of European Stock Returns

This paper examines the ability of beta and size to explain cross-sectional variation in average returns in 12 European countries. We find that average stock returns are positively related to beta and negatively related to firm size. The beta premium is in part due to the fact that high beta countries outperform low beta countries. Within countries high beta stocks outperform low beta stocks only in January, not in other months. We reject the hypothesis that differences in average returns on size- and beta-sorted portfolios can be explained by market risk and exposure to the excess return of small over large stocks (SMB). Consistent with recent US evidence, we find that after controlling for size, there is no association between average returns and exposure to SMB.     

The tax-loss selling hypothesis,market liquidity,and price pressure around the turn-of-the-year

In this paper, we use intra-day data for all stocks listed on the ISSM and provide new and direct evidence consistent with the tax-loss selling hypothesis. We find that (a) there is abnormal selling pressure prior to the year-end for stocks that have experienced large capital losses in the current and prior years (b) investors delay realizing capital gain by postponing the sale of capital gain stocks until after the new year (c) there is a significant decrease in the average trade size for stocks with large capital losses before the year-end and for stocks with capital gains in the new year, which suggests that individuals, rather than institutional investors, are the major sellers around the year-end (d) the tax-loss selling hypothesis, and not firm size or share price, is the fundamental explanation for abnormal January returns. Further, small or low share priced firms with capital gains do not experience abnormal returns in January. However, conditional on capital losses, small or low share priced firms magnify the turn-of-the-year effect (e) On average, the increase in selling activity adversely affects market liquidity by increasing bid-ask spreads and reducing depths. (f) The tax-loss selling pressure not only causes the price to be at the bid at the year-end, it also temporarily depresses the equilibrium price indicating the short run demand curve is not perfectly elastic (g) the year-end buying activity suggests that large investors buy capital loss stocks prior to the year-end to take advantage of the temporarily depressed price and capital gain stocks after the new year to reinvest the proceeds of the tax-loss selling.     

The Turn-of-the-Year in Canada

A number of investigators have reported that January stock returns in the U.S. exceed returns for other months of the year. This paper documents a similar finding for Canadian stocks over the period 1951–1980. However, Canada did not introduce a capital gains tax until 1973 and the paper reports that January returns in Canada exceed returns for other months of the year before and after this date. Thus, these data do not support the tax-loss-selling-pressure hypothesis as the entire explanation for the turn-of-the-year effect in stock returns, nor, by implication, do they support the tax-loss-selling-pressure hypothesis as the complete explanation for the “small firm” effect in U.S. stocks returns.     

STOCK PRICE AND DEGREE OF NEGLECT AS DETERMINANTS OF STOCK RETURNS

In prior research the neglected firm effect persists even after controlling for firm size. Several recent studies show that the size effect is a stock price effect. In the present study we investigate whether excess returns on neglected stocks are a manifestation of a stock price effect. Although material evidence supporting an independent neglected firm effect is still found, results are much weaker than in prior studies. Examining a large sample of New York Stock Exchange and American Stock Exchange stocks from 1977 to 1988, we find that both January and non-January months do not have a statistically significant neglect effect after controlling for a price effect.     

A Dividend Payment Effect in Stock Returns

This paper presents evidence for the period 7/62-12/89 that individual NYSE and AMEX stocks provide relatively high average excess returns on the payment dates of quarterly cash dividends and several subsequent trading days. Additional results indicate that returns during the payment period: (a) are not a manifestation of the January, monthly or dividend yield anomalies; (b) are positively related to the stock's dividend yield; and (c) are higher for firms that have dividend reinvestment plans. These findings are consistent with a tendency by stock-holders to reinvest dividend income into the stock of the paying firm, thereby increasing demand for the stock and raising its price. Additional evidence links the returns on these days with (previously-documented) excess returns around the ex-dividend date.     

On Russell index reconstitution

This paper investigates whether abnormal returns permanently exist in transparent U.S. Russell index reconstitution and provides evidence to disentangle the competing hypotheses associated with the index effect in the literature. Additions to Russell 1000 generate cumulative excess returns of 10.9% from 2 days before May 31 to June 30 while stocks deleted from Russell 2000 Growth Index suffer cumulative loss of 6.6%. The effect of index reconstitution on stocks in the style switching groups is moderate while it is much smaller for stocks in the retention groups. Based on daily trading volume, there is evidence that money managers tied to Russell style indexes tend not to rebalance their portfolios actively until the time of index reconstitution to avoid tracking error. However, for stocks generating large excess returns, money managers trade them actively prior to the reconstitution. This study is supportive of the imperfect substitutes hypothesis in explaining the index effect, given the absence of complete reversal of the event period abnormal returns and of consistent improvement in liquidity for the index additions. In the joint test, the price pressure hypothesis and the liquidity hypothesis explain the marginal index effect at most by 0.12% and 3.05%, respectively, while the imperfect substitutes hypothesis explains it at least by 9.21%. Furthermore, the index effect is not purely driven by individual stock price momentum.     

Size effect,book-to-market effect,and survival

Previous studies find that small stocks have higher average returns than large stocks, and the difference between the returns can not be accounted for by the systematic risk, β. In my analysis of Compustat and CRSP data from 1976 to 1995, and simulation experiments based on the data, I find the size effect can be largely explained by data truncation that is caused by survival. Small stocks’ returns are more volatile, and small stocks are more likely to go bankrupt and less likely to meet the stock exchanges’ minimum capitalization requirements for listing. As a result, they are more likely to drop out of the sample. Including small stocks that do well and excluding those that do poorly, ex post, gives rise to higher returns for small-size portfolios. I conclude that the size effect is largely a spurious statistical inference resulting from survival bias, not an asset pricing ‘anomaly’.     

The predictability of real estate returns and market timing

Recent evidence suggests that all asset returns are predictable to some extent with excess returns on real estate relatively easier to forecast. This raises the issue of whether we can successfully exploit this level of predictability using various market timing strategies to realize superior performance over a buy-and-hold strategy. We find that the level of predicability associated with real estate leads to moderate success in market timing, although this is not necessarily the case for the other asset classes examined in general. Besides this, real estate stocks typically have higher trading profits and higher mean risk-adjusted excess returns when compared to small stocks as well as large stocks and bonds even though most real estate stocks are small stocks.     

Average correlation and stock market returns

If the Roll critique is important, changes in the variance of the stock market may be only weakly related to changes in aggregate risk and subsequent stock market excess returns. However, since individual stock returns share a common sensitivity to true market return shocks, higher aggregate risk can be revealed by higher correlation between stocks. In addition, a change in stock market variance that leaves aggregate risk unchanged can have a zero or even negative effect on the stock market risk premium. We show that the average correlation between daily stock returns predicts subsequent quarterly stock market excess returns. We also show that changes in stock market risk holding average correlation constant can be interpreted as changes in the average variance of individual stocks. Such changes have a negative relation with future stock market excess returns.     

THE WEALTH EFFECTS OF TRACKING STOCK RESTRUCTURINGS

We provide a comprehensive examination of the post‐issue wealth effects of 29 completed tracking stock restructurings. We document that for the parent stock and for the combined firm, tracking stock restructurings lead to insignificant long‐term excess returns. However, we find that shareholders of tracking stocks realize significant post‐issue wealth losses. Unlike spin‐offs and carve‐outs, announcements of tracking stock restructurings are preceded by negative one‐year excess returns, and unlike the positive post‐issue long‐term excess returns to spin‐off stocks and the insignificant long‐term excess returns to carve‐out stocks, tracking stocks experience negative long‐term excess returns.     

EXCESS RETURNS OF INDEX REPLACEMENT STOCKS: EVIDENCE OF LIQUIDITY AND SUBSTITUTABILITY

Excess returns of S&P index replacement stocks are attributed to price pressures and imperfect substitutes in previous research. However, parameter estimates are biased by the use of pre-announcement returns; replacements are characterized by rising stock prices. Using a future estimation period to avoid this bias, we find excess returns do not reverse. Further, we find no relation between excess returns and the amount of stock closely held or the size of index funds. The evidence supports efficient market assumptions: the stock market is liquid and stocks are close substitutes.     

Can analysts predict rallies better than crashes?

We use the copula approach to study the structure of dependence between sell-side analysts’ consensus recommendations and subsequent security returns, with a focus on asymmetric tail dependence. We match monthly vintages of I/B/E/S recommendations for the period January–December 2011 with excess security returns during six months following recommendation issue. Using a mixed Gaussian–symmetrized Joe–Clayton copula model we find evidence to suggest that analysts can identify stocks that will substantially outperform, but not underperform relative to the market, and that their predictive ability is conditional on recommendation changes.     

Profitability of the CRISMA System: From World Indices to the Hong Kong Stock Market

Many studies show that the use of technical analysis can generate excess returns. We test the “CRISMA” technical trading rule introduced by [Pruitt and White J. Portfolio Managt. Spring, 1988, 55–58] on global equity indices and common stocks in Hong Kong. Out study shows that no excess returns could be found in indices except those in Asia. This validates the claims that the Asian stock markets are not as efficient as other stock markets and hence can be exploited by technical analysis. How does CRISMA perform on common stocks in Hong Kong? Generally speaking, CRISMA does not fair better than the buy and hold strategy. Further analysis reveals excess returns for stocks with very large turnover. This is consistent with other recent research on CRISMA conducted on US and UK stock markets. We also amend part of the original CRISMA rules to yield better performance: shrinking the moving average window sizes can increase both the number of trade signals and the excess returns. Therefore CRISMA can be made to work with some judicious choice of parameters, depending on the turnover.     

Institutional Investors, Analyst Following, and the January Anomaly

Average stock returns for small, low stock price firms are higher in January than for the rest of the year. Two explanations have received a great deal of attention: tax-loss selling and gamesmanship. This paper documents that seasonality in returns is not a phenomenon observed only for small firms' stock or those with low prices. Strong seasonality in excess returns is reported for a sample of widely followed firms. Sample firms have unusually low excess returns in January and returns adjust upward over the year. These results are consistent with the gamesmanship hypothesis, but not the tax-loss-selling hypothesis.     

Institutional Investors, Analyst Following, and the January Anomaly

Average stock returns for small, low stock price firms are higher in January than for the rest of the year. Two explanations have received a great deal of attention: tax-loss selling and gamesmanship. This paper documents that seasonality in returns is not a phenomenon observed only for small firms' stock or those with low prices. Strong seasonality in excess returns is reported for a sample of widely followed firms. Sample firms have unusually low excess returns in January and returns adjust upward over the year. These results are consistent with the gamesmanship hypothesis, but not the tax-loss-selling hypothesis.     

AN EVALUATION OF THE PERFORMANCE OF PORTFOLIOS SELECTED FROM VALUE LINE RANK ONE STOCKS: 1976–1982

In this study, the performance of portfolios selected from among Value Line rank one stocks is compared with portfolios consisting of randomly selected New York Stock Exchange and American Stock Exchange stocks. Results indicate that before considering transactions costs, active traders who invest in Value Line rank one stocks can earn positive excess returns. However, after considering transaction costs, neither active traders nor passive investors in rank one stocks can earn returns that are statistically greater than returns achieved by portfolios of randomly selected stocks. These results are not sensitive to variations in portfolio size.     

Probability of information-based trading and the January effect

We investigate the seasonality in the probability of information-based trading (PIN)–return relationship, the ‘January PIN effect’. We find that on average stock returns decrease with PIN in January, in contrast to other calendar months. This pattern is more apparent for small stocks. We argue that this seasonality is related to the January effect. According to the analysis, the December selling pressure associated with the January effect decreases in PIN, especially for small stocks. This suggests that when the price bounces back in January, low-PIN stocks will exhibit a larger return within a small stock group, leading to the negative PIN–return pattern. Furthermore, this seasonality is not the same as other January anomalies associated with momentum and idiosyncratic risk.     

Return seasonality in stocks and their underlying assets: tax-loss selling versus information explanations

Results of tests contrasting tax-loss selling with intertemporalinformation variation as explanations of the January seasonalin stock returns are reported. Closed-end fund shared displaythe typical size-related January seasonal while their net assetvalues do not. Interpreting the net asset value return as aproxy for information about under-lying assets, this resultindicates information variation is not a necessary conditionfor the January effect in stocks. The share returns at the turnof the year are negatively related to their mean preceding yearreturns and positively related to the standard deviations oftheir preceding year returns. These results are consistent withtax-loss selling.