The Delisting Bias in CRSP's Nasdaq Data and Its Implications for the Size Effect

We investigate the bias in CRSP's Nasdaq data due to missing returns for delisted stocks. We find that the missing returns are large and negative on average, and that delisted stocks experience a substantial decrease in liquidity. We estimate that using a corrected return of −55 percent for missing performance-related delisting returns corrects the bias. We revisit previous work which finds a size effect among Nasdaq stocks. After correcting for the delisting bias, there is no evidence that there ever was a size effect on Nasdaq. Our results are inconsistent with most risk-based explanations of the size effect.     

The impact of share price on seasonality and size anomalies in Australian equity returns

Given the high correlation between a firm's stock price and market capitalisation, it is possible that the well-documented size anomaly is masking a share-price effect. Using a seemingly unrelated regression model to accommodate contemporaneous correlation between portfolios, we estimate the separate effects of firm size and share price on returns to Australian equity portfolios. The analysis is also extended to estimate seasonal components of size and price effects. Our major findings are: (i) firm size and share price have significant and independent effects on portfolio returns averaged over all months, (ii) the familiar negative relation between size and returns is confirmed across all months, and (iii) the relation between share price and returns is negative in July and positive in all other months (with the exception of January where no price effect occurs). These findings, which are consistent across sub-periods and robust to method variations, highlight the need for future research to provide an economic foundation for the relation between average returns, size and price.     

The Delisting Bias in CRSP Data

I document a delisting bias in the stock return data base maintained by the Center for Research in Security Prices (CRSP). I find that delists for bankruptcy and other negative reasons are generally surprises and that correct delisting returns are not available for most of the stocks that have been delisted for negative reasons since 1962. Using over-the-counter price data, I show that the omitted delisting returns are large. Implications of the bias are discussed.     

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 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.     

Seasonals,price adjustment delays and estimated systematic risk

This article analyzed potential interactions between seasonals and price adjustment delays on estimated systematic risk. It was shown that seasonals in unobservable true security returns can induce inconsistencies into the generalized Scholes and Williams estimator of systematic risk. An alternative estimator was proposed that is consistent in the presence of seasonals in the unobservable true returns. The direction of induced bias is unpredictable a priori, thereby representing a potentially important research consideration in market efficiency tests using abnormal returns. NASDAQ and Dow Jones 30 Industrial return data for the period 1983–87 were used to evaluate the proposed estimator against the OLS and generalized Scholes and Williams (GSW) alternatives. The absolute difference between the GSW and our estimator, that is the seasonal-induced bias, for NASDAQ stocks was negatively correlated with market capitalization. Moreover, seasonal-induced bias was larger for NASDAQ stocks than more highly capitalized Dow stocks. These empirical findings indicate that seasonals and price adjustment delays can interact to bias estimated systematic risk, where price adjustment delays would be projected to be more acute for smaller capitalization stocks.     

On the Stability of the Cross‐Section of Expected Stock Returns in the Cross‐Section: Understanding the Curious Role of Share Turnover

In this paper, we shed further light on cross‐sectional predictors of stock return performance. Specifically, we explore whether the cross‐section of expected stock returns is robust within stock groups sorted by past monthly return. We find that the book/market and momentum effects are remarkably robust to sorting on past returns. However, share turnover is negatively related to future returns for stocks with abnormally low stock price performance in the recent past, but postively related to returns for well‐performing stocks. This casts doubt on the use of turnover as a liquidity proxy, but is consistent with turnover being a proxy for momentum trading which pushes prices in the direction of past price movements. Our results are robust to both NYSE/AMEX and Nasdaq stocks, and also robust to stratifying the sample by time period.     

Sell‐Side Analyst Research and Stock Comovement

We document that a stock's price around a recommendation or forecast covaries with prices of other stocks the issuing analyst covers. The effect of shared analyst coverage on stock price comovement extends beyond analyst activity days. A stock's daily returns covary with the returns of other stocks with which it shares analyst coverage. These links between stock price comovement and shared analyst coverage are consistent with the coverage‐specific information we find in earnings forecasts; analysts who cover both stocks in a pair expect future earnings of the stocks to be more highly correlated than do analysts who cover only one stock from the pair. Collectively, our evidence indicates that analyst research produces coverage‐specific spillovers that raise price comovement among stocks that share analyst coverage. The strength of these spillovers is comparable to spillovers from broad industry and market information in analyst research.     

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.     

Institutional investors,intangible information,and the book-to-market effect

This paper establishes a robust link between the trading behavior of institutions and the book-to-market effect. Building on work by Daniel and Titman (2006), who argue that the book-to-market effect is driven by the reversal of intangible returns, I find that institutions tend to buy (sell) shares in response to positive (negative) intangible information and that the reversal of the intangible return is most pronounced among stocks for which a large proportion of active institutions trade in the direction of intangible information. Furthermore, the book-to-market effect is large and significant in stocks with intense past institutional trading but nonexistent in stocks with moderate institutional trading. This influence of institutional trading on the book-to-market effect is distinct from that of firm size. These results are consistent with the view that the tendency of institutions to trade in the direction of intangible information exacerbates price overreaction, thereby contributing to the value premium.     

The price impact of foreign institutional herding on large-size stocks in the Taiwan stock market

This study constructs a panel threshold regression model to explore the price impact of foreign institutional herding of firms listed in the Taiwan Stock Exchange during January 2000 to June 2008. Our panel threshold model is constructed to explore the price impact of foreign institutional investors?? herding in the Taiwan stock market after controlling the firm size. By examining the presence of threshold effect, this study analyzes whether firm size would obviously and asymmetrically affect the explanation for the effect of changes in foreign investors?? share ownership on abnormal returns. The empirical results of this study find the significant evidence of threshold effect which divides the stocks into large-size and small-size firms. It is found that foreign institutional investors in the Taiwan stock market tend to hold large-size stocks listed in the Taiwan Stock Exchange. There is an apparent increase in the subsequent abnormal returns on large-size stocks bought in bulk by foreign investors. The signals of changes in share ownership initiated by foreign institutional investors would reveal further information for improving the performance of asset reallocation decisions in Taiwan. The panel threshold model constructed in this paper well describes the price impact of institutional herding yet eschews the possibly subjective data snooping issue resulting from the two-pass sorting method as proposed by previous related researches.     

Firm Size,Book-to-Market Ratio,and Security Returns: A Holdout Sample of Financial Firms

Fama and French (1992) document a significant relation between firm size, book-to-market ratios, and security returns for nonfinancial firms. Because of their initial interest in leverage as an explanatory variable for security returns, Fama and French exclude from their analysis financial firms, thus creating a natural holdout sample on which to test the robustness of their results. We document that the relation between firm size, book-to-market ratios, and security returns is similar for financial and nonfinancial firms. In addition, we present evidence that survivorship bias does not significantly affect the estimated size or book-to-market premiums in returns. Our results indicate data-snooping and selection biases do not explain the size and book-to-market patterns in returns.     

A re-examination of the index effect: Gambling on additions to and deletions from the S&P 500's ‘gold seal’

This study examines the abnormal returns, trading activity, volatility and long-term performance of stocks that were added to the S&P 500 index. By using a three-factor pricing model that allows for firm size and value characteristics as well as market risk, we are able to shed new light on the widely observed ‘index effect’. We find that the CAPM tends to overstate the performance of large firms and to understate the performance of small firms. We also find a transitory increase in trading volume between the announcement and a few days after the effective date. In terms of the firm's operating performance, we find a significant increase in earnings per share after inclusion, which combines with the stock price rise to leave the average price-earnings ratio largely unaltered. Examining a unique sample of deletions of international companies and replacements with US companies, we find that deleted stocks experienced a considerable and permanent fall in price, inconsistent with the Investor Recognition Hypothesis. The “seal” of S&P 500 index membership has very long-term effects and inclusion appears not to be an information-free event.     

Biases in computed returns: An application to the size effect

Previous estimates of a ‘size effect’ based on daily returns data are biased. The use of quoted closing prices in computing returns on individual stocks imparts an upward bias. Returns computed for buy-and-hold portfolios largely avoid the bias induced by closing prices. Based on such buy-and-hold returns, the full-year size effect is half as large as previously reported, and all of the full-year effect is, on average, due to the month of January.     

Systematic liquidity risk and stock price reaction to shocks

This study examines the relationship between systematic liquidity risk and stock price reaction to large 1‐day price changes (or shocks). We base our analysis on a yearly updated constituents list of the FTSE All share index. Our overall results are consistent with the price continuation hypothesis, which suggests that positive (negative) shocks will be followed by positive (negative) abnormal returns. However, further analysis indicates that stocks with low systematic liquidity risk react efficiently to both positive and negative shocks, whereas stocks with high systematic liquidity risk underreact to both positive and negative shocks. Our results are valid irrespective of various robustness tests such as size of the shock, size of the firm, month‐of‐the‐year and day‐of‐the‐week effects. We conclude that trading on price patterns following shocks may not be profitable, as it involves taking substantial liquidity exposure.     

DO DIVIDEND FLOWS AFFECT STOCK RETURNS?

We examine price impacts from dividend flows. Event‐study estimates show that stocks experience abnormal returns on the dividend distribution day. Results also show a spillover effect to non‐dividend‐paying stocks that are likely to be part of the same benchmark portfolio as the dividend‐paying stocks. Regression results indicate that the effect is dependent on the ownership share by professional investors. The temporary nature of the effect on returns is in line with the literature's demand‐driven price pressure hypothesis.     

The January Anomaly: Effects of Low Share Price,Transaction Costs,and Bid-Ask Bias

The January effect is primarily a low-share price effect and less so a market value effect. In the recent 1977–1986 period, after-transaction-cost raw and excess January returns are lower on low-price stocks than on high-price stocks. Failure of informed traders to eliminate significantly large before-transaction-cost excess January returns on low-price stocks is potentially explained by higher transaction costs and a bid-ask bias. At the least, the January anomaly found in prior tests is not persistent, and thereby, not likely to be exploitable by typical investors.     

Idiosyncratic Risk,Investor Base,and Returns

Using four different proxies for a firm's investor base we demonstrate that idiosyncratic risk premiums are larger for neglected stocks and smaller or economically insignificant for visible stocks. Since neglected stocks have greater idiosyncratic volatility (IV), the total IV risk premium (price × quantity) for neglected stocks will be greater than that of visible stocks. Additionally, we find a positive size effect and negative beta effect after controlling for IV. Overall, our results provide strong support for Merton's theory that market segmentation induced by incomplete information is an important component of the influence of IV in the cross‐section of returns.     

Interaction of size,book‐to‐market and momentum effects in Australia

This study seeks to disentangle the effects of size, book‐to‐market and momentum on returns. Initial results show that each characteristic has a role in explaining returns, but that there is interaction between size and momentum, as well as between size and book‐to‐market. Three key findings emerge. First, the size premium is the strongest, particularly in the loser portfolios. Second, the value premium is generally limited to the smallest portfolios. Third, the momentum premium is evident for the large‐ and middle‐sized portfolios, but loser stocks significantly outperform winner stocks in the smallest size portfolio. When these interactions are controlled with multivariate regression, we find a significant negative average relation between size and returns, a significant positive average relation between book‐to‐market and returns, and a significant positive average relation between momentum and returns.