The return impact of realized and expected idiosyncratic volatility

We show that the negative relation between realized idiosyncratic volatility, measured over the prior month, and returns is robust in non-January months. Controlling for realized idiosyncratic volatility, we show that the relation between returns and expected idiosyncratic volatility is positive and robust. Realized and expected idiosyncratic volatility are separate and important effects describing the cross-section of returns. We find the negative return on a zero-investment portfolio that is long high realized idiosyncratic volatility stocks and short low realized idiosyncratic volatility stocks is dependent on aggregate investor sentiment. In cross-sectional tests, we find the negative relation is weaker for stocks with a large analyst following and stronger for stocks with high dispersion of analyst forecasts. The positive relation between expected idiosyncratic volatility and returns is not due to mispricing.     

ROBUSTNESS OF THE HEADQUARTERS‐CITY EFFECT ON STOCK RETURNS

Recent studies report that U.S. firms headquartered near each other experience positive comovement in their stock returns, a finding suggestive of local biases in equity trading activity. We investigate the robustness of these findings and find that including additional pricing factors in models for monthly stock returns materially reduces the magnitude of the headquarters‐city effect in stock returns. Additionally, we find that an implicit null hypothesis of zero local return comovement is inappropriate as there is positive comovement between a stock's return and returns on portfolios of stocks from nonheadquarters cities, on average. Nevertheless, results benchmarked against estimates based on resampling methods indicate a significant and robust headquarters‐city effect in stock returns.     

Evidence on the Extent and Potential Sources of Long Memory in U.S. Treasury Security Returns and Yields

Unlike equity returns, many fixed-income return measures appear to display long memory. We show that the extent of long memory differs strongly for gross and excess holding period returns on U.S. Treasury securities. Granger and others have argued that long memory may only reflect infrequent structural breaks. We explore the impact of structural instability on tests for long memory and find only weak indications that it lies behind the long memory in our return series. The evidence of long memory remains strong for yield and term premia series even after accounting for a series of potential underlying structural changes.     

Connecting two markets: An equilibrium framework for shorts,longs, and stock loans

We analyze a reduced-form framework for understanding the equity loan market's impact on share prices. We show that hard-to-borrow stocks will have distinct return patterns, responding more to shocks in the supply of shares available, and to changes in the heterogeneity of investor beliefs, than other stocks. We conduct two empirical tests in which we find strong support for these equilibrium predictions. In our first test, we take advantage of a tax-driven exogenous shock to share loan supply and find that when supply is reduced around dividend record dates, prices of hard-to-borrow stocks increase 1.1% while prices of easy-to-borrow stocks are unaffected. In our second test, we find that hard-to-borrow stocks have 4.8% lower three-month returns than other stocks, with negative returns concentrated in stocks with high heterogeneity in investor beliefs. Thus, we extend the Diether, Malloy, and Scherbina (2002) result that stocks with a greater dispersion of investor beliefs have lower returns.     

Does Trading Volume Contain Information to Predict Stock Returns? Evidence from China's Stock Markets

This paper examines empirical contemporaneous and causal relationships between trading volume, stock returns and return volatility in China's four stock exchanges and across these markets. We find that trading volume does not Granger-cause stock market returns on each of the markets. As for the cross-market causal relationship in China's stock markets, there is evidence of a feedback relationship in returns between Shanghai A and Shenzhen B stocks, and between Shanghai B and Shenzhen B stocks. Shanghai B return helps predict the return of Shenzhen A stocks. Shanghai A volume Granger-causes return of Shenzhen B. Shenzhen B volume helps predict the return of Shanghai B stocks. This paper also investigates the causal relationship among these three variables between China's stock markets and the US stock market and between China and Hong Kong. We find that US return helps predict returns of Shanghai A and Shanghai B stocks. US and Hong Kong volumes do not Granger-cause either return or volatility in China's stock markets. In short, information contained in returns, volatility, and volume from financial markets in the US and Hong Kong has very weak predictive power for Chinese financial market variables.     

Long memory in stock market volatility and the volatility-in-mean effect: The FIEGARCH-M Model

We extend the fractionally integrated exponential GARCH (FIEGARCH) model for daily stock return data with long memory in return volatility of Bollerslev and Mikkelsen (1996) by introducing a possible volatility-in-mean effect. To avoid that the long memory property of volatility carries over to returns, we consider a filtered FIEGARCH-in-mean (FIEGARCH-M) effect in the return equation. The filtering of the volatility-in-mean component thus allows the co-existence of long memory in volatility and short memory in returns. We present an application to the daily CRSP value-weighted cum-dividend stock index return series from 1926 through 2006 which documents the empirical relevance of our model. The volatility-in-mean effect is significant, and the FIEGARCH-M model outperforms the original FIEGARCH model and alternative GARCH-type specifications according to standard criteria.     

Nonparametric event study tests

This paper provides the first documentation of the power and specification of the generalized sign test, which is based on the percentage of positive abnormal returns in an estimation period. In simulations using daily stock return data, the generalized sign test is well specified with both exchange listed and NASDAQ stocks. A rank test is more powerful under ideal conditions. However, the rank test is more sensitive to increases in the length of the event window, to increases in return variance, and to thin trading. The generalized sign test is a viable alternative to the rank test under these conditions.     

Conflict of interest and certification in the U.S. IPO market

We examine the long-term return performance of U.S. IPOs underwritten by relationship banks. We show that, over one- to three-year horizons, IPOs managed by relationship banks experience buy-and-hold benchmark-adjusted returns that are similar to those observed for a matching sample of stocks managed by non-relationship underwriters. This result holds even when the returns' skewness and cross-sectional correlation is accounted for. Further, we examine the calendar-time returns on a portfolio that is long the stocks underwritten by relationship banks and short ex-ante similar stocks taken public by non-relationship institutions. Again, we conclude that the two groups of IPOs yield similar long-run returns. These findings support the certification role of relationship banks and suggest that, in this respect, the effect of the 1999 repeal of Sections 20 and 32 of the Glass–Steagall Act has not been negative.     

Stock return volatility,operating performance and stock returns: International evidence on drivers of the ‘low volatility’ anomaly

This study highlights the link between stock return volatility, operating performance, and stock returns. Prior studies suggest that there is a ‘low volatility’ anomaly, where firms with a low stock return volatility out-perform firms with a high stock return volatility. This paper confirms that low volatility stocks earn higher returns than high volatility stocks in emerging markets and developed markets outside of North America. We also show that low volatility stocks have higher operating returns and this might explain why low volatility stocks earn higher stock returns. These results provide a partial explanation for the ‘low volatility effect’ that is independent from the existence of market anomalies or per se inefficiencies that might otherwise drive a low volatility effect. We emphasize the importance of controlling for stock return volatility when analyzing operating performance and stock performance.     

Idiosyncratic risk,costly arbitrage,and the cross-section of stock returns

We test a new cross-sectional relation between expected stock return and idiosyncratic risk implied by the theory of costly arbitrage. If arbitrageurs find it more difficult to correct the mispricing of stocks with high idiosyncratic risk, there should be a positive (negative) relation between expected return and idiosyncratic risk for undervalued (overvalued) stocks. We combine several well-known anomalies to measure stock mispricing and proxy stock idiosyncratic risk using an exponential GARCH model for stock returns. We confirm that average stock returns monotonically increase (decrease) with idiosyncratic risk for undervalued (overvalued) stocks. Overall, our results support the importance of idiosyncratic risk as an arbitrage cost.     

Limited attention and the earnings announcement returns of past stock market winners

We document that stocks with the strongest prior 12-month returns experience a significant average market-adjusted return of 1.58% during the five trading days before their earnings announcements and a significant average market-adjusted return of −1.86% in the five trading days afterward. These returns remain significant even after accounting for transactions costs. We empirically test a limited attention explanation for these anomalous returns—that stocks with sharp run-ups tend to attract individual investors’ attention and investment dollars, particularly before their earnings announcements. Our analysis suggests that the trading decisions of individual investors are at least partly responsible for the return pattern that we observe.     

The information content of stock split announcements: Do options matter?

We provide a new test of the informational efficiency of trading in stock options in the context of stock split announcements. These announcements tend to be associated with positive abnormal returns. Our traditional event study results show abnormal returns that are significantly lower for optioned than non-optioned stocks, whether traded on the NYSE, Amex, or Nasdaq. After controlling for market returns, capitalization, book-to-market ratio, and trading volume, we find that the abnormal returns are significantly lower for NYSE/Amex optioned than non-optioned stocks. Although the results for Nasdaq stocks are not as clear, the overall effects tend to be lower after optioning. These findings are consistent with the hypothesis that the prices of optioned stocks embody more information, diminishing the impact of the stock split announcement. They provide new evidence of the beneficial effects of options on their underlying stocks.     

Dynamic Volume-Return Relation of Individual Stocks

We examine the dynamic relation between return and volume ofindividual stocks. Using a simple model in which investors tradeto share risk or speculate on private information, we show thatreturns generated by risk-sharing trades tend to reverse themselves,while returns generated by speculative trades tend to continuethemselves. We test this theoretical prediction by analyzingthe relation between daily volume and first-order return autocorrelationfor individual stocks listed on the NYSE and AMEX. We find thatthe cross-sectional variation in the relation between volumeand return autocorrelation is related to the extent of informedtrading in a manner consistent with the theoretical prediction.     

Time-varying risk The case of the American computer industry

A bivariate GARCH-in-mean model for individual stock returns and the market portfolio is designed to model volatility and to test the conditional Capital Asset Pricing Model versus the conditional Residual Risk Model. We find that a univariate model of volatility for individual stock returns is misspecified. A joint modelling of the market return and the individual stock return shows that a major force driving the conditional variances of individual stocks is the history contained in the market return variance. We find that a conditional residual risk model, where the variance of the individual stock return is used to explain expected returns, is preferred to a conditional CAPM. We propose a partial ordering of securities according to their market risk using first and second order dominance criteria.     

The effects of dividend yield and dividend policy on common stock prices and returns

This paper suggests that it is not possible to demonstrate, using the best available empirical methods, that the expected returns on high yield common stocks differ from the expected returns on low yield common stocks either before or after taxes. A taxable investor who concentrates his portfolio in low yield securities cannot tell from the data whether he is increasing or decreasing his expected after-tax return by so doing. A tax exempt investor who concentrates his portfolio in high yield securities cannot tell from the data whether he is increasing or decreasing his expected return. We argue that the best method for testing the effects of dividend policy on stock prices is to test the effects of dividend yield on stock returns. Thus the fact that we cannot tell, using the best available methods, what effects dividend yield has on stock returns implies that we cannot tell what effect, if any, a change in dividend policy will have on a corporation's stock price.     

Microstructure and reverse stock splits

In this paper we show that, similar to NYSE/AMEX stocks, NASDAQ stocks exhibit significant ex date returns for reverse stock splits. Although the 10-day cumulative return after the ex date is close to –10%, this does not violate market efficiency, because the average bid-ask spread for the reverse split stock is at least double this return. We also document that these large negative returns are mostly due to a drop in the ask price while bid prices barely change at all. Furthermore, the ex date returns are negatively related to trading volume.These results suggest that there is abnormal selling and a significant buildup of market makers' inventories near the ex date. To reduce the inventory buildup, market makers lower ask prices to induce buying by investors, resulting in the observed negative returns. Lowering bid prices, an alternative strategy for reducing inventories, is not attractive to market makers due to competitive factors and the reduction of commissions associated with a smaller number of transactions. Notably, selling investors have no incentives to sell their stocks early to avoid the observed negative ex date return, since this return is largely an ask price phenomenon and does not represent realized returns to sellers.     

Stock Returns in Thinly Traded Markets

We examine the share price behavior of thinly traded NASDAQ National Market System stocks during periods when financial markets are open but the individual stocks do not trade. The absence of trade allows us to isolate the effect of nontrading from that of market closure. We find that nontrading stocks have negative mean returns and lower variances regardless of whether markets are open or closed. Two-day returns that include one nontrading day have a mean daily return of -0.226% compared to +0.164% for two-day returns over consecutive trading days. Two-day returns that include one nontrading day have only 3.8% higher variance than one-day returns. We conclude that the relation between transaction arrival, mean returns, and volatility depends on whether a stock is trading and not simply on whether the market is open.     

Risk,Mispricing, and Value Investing

We evaluate the stock return performance of a modified version of the book-to-market strategy and its implications for market efficiency. If the previously documented superior stock return of the book-to-market strategy represents mispricing, its performance should be improved by excluding fairly valued firms with extreme book-to-market ratios. To attain this, we classify stocks as value or glamour on book-to-market ratios and accounting accruals jointly. This joint classification is likely to exclude stocks with extreme book-to-market ratios due to mismeasured accounting book values reflecting limitations underlying the accounting system. Using both 12-month buy-and-hold returns and earnings announcement returns, our results show that this joint classification generates substantially higher portfolio returns in the post-portfolio-formation year than the book-to-market classification alone with no evidence of increased risk. In addition, this superior stock return performance is more pronounced among firms held primarily by small (unsophisticated) investors and followed less closely by market participants (stock price <$10). Finally, and most importantly, financial analysts are overly optimistic (pessimistic) about earnings of glamour (value) stock, and for a subset of firms identified as overvalued by our strategy, the earnings announcement raw return, as well as abnormal return, is negative. These last results are particularly important because it is hard to envision a model consistent with rational investors holding risky stocks with predictable negative raw returns for a long period of time rather than holding fT-bills and with financial analysts systematically overestimating the earnings of these stocks while underestimating earnings of stocks that outperform the stock market.     

Analyst Forecasts and the Cross Section of European Stock Returns

We examine revisions to earnings forecasts by equity analysts and their role in predicting stock returns. We provide evidence that European stocks with net upward revised forecasts earn higher future returns than otherwise similar stocks. This effect is not concentrated in small stocks, stocks with low analyst coverage, or stocks with low book‐to‐market ratios. We find differences in the return continuation patterns of stocks with upward versus downward revisions, namely, bad news travels quickly, but good news travels slowly. This result is consistent with investors' attaching greater significance to poor earnings forecasts, but adopting a wait‐and‐see approach to good news.     

Cross-sectional dispersion and expected returns

This study investigates whether the cross-sectional dispersion of stock returns, which reflects the aggregate level of idiosyncratic risk in the market, represents a priced state variable. We find that stocks with high sensitivities to dispersion offer low expected returns. Furthermore, a zero-cost spread portfolio that is long (short) in stocks with low (high) dispersion betas produces a statistically and economically significant return. Dispersion is associated with a significantly negative risk premium in the cross section (–1.32% per annum) which is distinct from premia commanded by alternative systematic factors. These results are robust to stock characteristics and market conditions.