MACROECONOMIC NEWS AND STOCK MARKET CALENDAR AND WEATHER ANOMALIES

An analysis of six stock market calendar and weather anomalies from 1980 to 2003 shows that (1) returns on trading days in which macroeconomic announcements were made generate the anomalies and (2) five of the six anomalies are not present at all on the trading days in which such announcements were not made (more than 60% of the sample). The results suggest that the market response to macroeconomic news, not psychological or institutional factors, is the main source of calendar and weather anomalies.     

EXPECTED VOLATILITY,UNEXPECTED VOLATILITY,AND THE CROSS‐SECTION OF STOCK RETURNS

The existing literature finds conflicting results on the cross‐sectional relation between expected returns and idiosyncratic volatility. We contend that at the firm level, the sample correlation between unexpected returns and expected idiosyncratic volatility can cloud the true relation between the expected return and expected idiosyncratic volatility. We show strong evidence that unexpected idiosyncratic volatility is positively related to unexpected returns. Using unexpected idiosyncratic volatility to control for unexpected returns, we find expected idiosyncratic volatility to be significantly and positively related to expected returns. This result holds after controlling for various firm characteristics, and it is robust across different sample periods.     

PRESIDENTIAL ELECTION UNCERTAINTY AND COMMON STOCK RETURNS IN THE UNITED STATES

There is substantial evidence on the influence of political outcomes on the business cycle and stock market. We further hypothesize that uncertainty about the outcome of a U.S. presidential election should be reflected in pre‐election common stock returns. Prior research pools returns based on the party of the winning candidate, assuming that the outcome of the election is known a priori. We use candidate preference (i.e., polling) data to construct a measure of election uncertainty. We find that if the election does not have a candidate with a dominant lead, stock market volatility (risk) and average returns rise.     

INTANGIBLE ASSETS,BOOK‐TO‐MARKET,AND COMMON STOCK RETURNS

I examine two anomalies where the Fama and French three‐factor model fails to adequately explain monthly industry and index returns. Both anomalies are consistent with a bad model problem where the book‐to‐market factor introduces a negative bias in the intercepts. I propose the intangibles model as an alternative where the three‐factor model is known to have difficulty. This alternative model, which replaces the book‐to‐market factor with zero investment portfolio returns based on prior investments in intangible assets, is well specified in random samples, has comparable power, and fully explains both anomalies.     

TRADING‐VOLUME SHOCKS AND STOCK RETURNS: AN EMPIRICAL ANALYSIS

We examine high‐volume premiums based on weekly risk‐adjusted returns. Significant average weekly abnormal high‐volume premiums up to 0.50% per week are documented for 1962–2005. Most premiums are generated in the first two weeks and monotonically decline as holding periods are extended. Evidence of reversal is found as the holding periods are extended. Premiums depend on realized turnover in the holding period. The last finding supports the theories of Miller and Merton. Finally, we test whether premiums are compensation for taking additional risk. Negative skewness, idiosyncratic risk, and liquidity risk do not explain the high‐volume premiums.     

ASYMMETRIC COVARIANCE,VOLATILITY, AND THE EFFECT OF NEWS

We propose that covariance (rather than beta) asymmetry provides a superior framework for examining issues related to changing risk premiums. Accordingly, we investigate whether the conditional covariance between stock and market returns is asymmetric in response to good and bad news. Our model of conditional covariance accommodates both the sign and magnitude of return innovations, and we find significant covariance asymmetry that can explain, at least in part, the volatility feedback of stock returns. Our findings are consistent across firm size, firm leverage, and temporal and cross‐sectional aggregations.     

MACROECONOMIC VARIABLES AND SEASONAL MEAN REVERSION IN STOCK RETURNS

In this paper I investigate whether seasonal mean reversion in stock portfolio returns is related to common macroeconomic risk factors. I decompose excess returns into explained and unexplained returns using a multifactor pricing model. The explained excess returns exhibit January mean reversion; the unexplained excess returns do not. The mean reversion can be attributed to the components of return related to unexpected inflation, bond default premium, and market risk. The results do not depend on the time-series properties of the portfolio betas. Bond default premia and excess market returns are mean reverting in January.     

ESTIMATING EXPECTED EXCESS RETURNS USING HISTORICAL AND OPTION‐IMPLIED VOLATILITY

We test the relation between expected and realized excess returns for the S&P 500 index from January 1994 through December 2003 using the proportional reward‐to‐risk measure to estimate expected returns. When risk is measured by historical volatility, we find no relation between expected and realized excess returns. In contrast, when risk is measured by option‐implied volatility, we find a positive and significant relation between expected and realized excess returns in the 1994–1998 subperiod. In the 1999–2003 subperiod, the option‐implied volatility risk measure yields a positive, but statistically insignificant, risk‐return relation. We attribute this performance difference to the fact that, in the 1994–1998 subperiod, return volatility was lower and the average return was much higher than in the 1999–2003 subperiod, thereby increasing the signal‐to‐noise ratio in the latter subperiod.     

WHEN‐ISSUED SHARES,SMALL TRADES,AND THE VARIANCE OF RETURNS AROUND STOCK SPLITS

The increases in volatility after stock splits have long puzzled researchers. The usual suspects of discreteness and bid‐ask spread do not provide a complete explanation. We provide new clues to solve this mystery by examining the trading of when‐issued shares that are available before the split. When‐issued trading permits noise traders to compete with a more homogenous set of traders, decreasing the volatility of the stock before the split. Following the split, these noise traders reunite in one market and volatility increases. Thus, the higher volatility after the ex date of a stock split is a function of the introduction of when‐issued trading, the new lower price level after the split date, and the increased activity of small‐volume traders around a stock split.     

MEASURING EFFECTS ON STOCK RETURNS OF SENTIMENT INDEXES CREATED FROM STOCK MESSAGE BOARDS

Various techniques and sources of information exist to aid investors in predicting future stock returns. However, no effective proxy for retail investors, such as stock message board users, has been established. This study provides guidelines for creating an effective proxy. The heart of such proxies is sentiment indexes, and in the past the indexes have had low predictive power. Introducing four methodological improvements for applying text classifiers and two probability measurements, we contrast eight widely applied text classifiers to stock message board data. Based on the classifier results and incorporating our new methods, the new sentiment index proves to be a significant “same‐day positive but next‐day negative” directional indicator.     

STOCK MARKET REACTION TO GOOD AND BAD INFLATION NEWS

This article shows that differentiating between good and bad inflation news is important to understanding how inflation affects stock market returns. Summing positive and negative inflation shocks as in previous studies tends to wash out or mute the effects of inflation news on stock returns. More specifically, we find that, depending on the economic state, positive and negative inflation shocks can produce a variety of stock market reactions. We conclude that the effect of inflation on stock returns is conditional on whether investors perceive inflation shocks as good or bad news in different economic states.     

FORECASTING STOCK INDEX VOLATILITY: COMPARING IMPLIED VOLATILITY AND THE INTRADAY HIGH–LOW PRICE RANGE

The intraday high–low price range offers volatility forecasts similarly efficient to high‐quality implied volatility indexes published by the Chicago Board Options Exchange (CBOE) for four stock market indexes: S&P 500, S&P 100, NASDAQ 100, and Dow Jones Industrials. Examination of in‐sample and out‐of‐sample volatility forecasts reveals that neither implied volatility nor intraday high–low range volatility consistently outperforms the other.     

DAYLIGHT AND INVESTOR SENTIMENT: A SECOND LOOK AT TWO STOCK MARKET BEHAVIORAL ANOMALIES

Kamstra, Kramer, and Levi (2000, 2003) describe two stock market behavioral anomalies associated with changes in investor sentiment caused by daylight saving time (DST) changes and seasonal affective disorder (SAD). According to the hypothesized effects, DST changes and SAD affect asset prices by changing investors’ risk aversion. Although changes in the timing or amount of daylight are correlated with unusual stock market returns, I present evidence they do not cause those unusual returns. Instead, seasonal patterns in market‐related information during the sample period are the likely cause of the correlation between stock market returns and DST changes or SAD.     

MODELS OF STOCK MARKET PREDICTABILITY

I briefly review the success of past studies purporting to explain equity valuations and predict future equity returns. The Campbell‐Shiller mean reversion models are contrasted with an expanded version of the so‐called Federal Reserve model. At least from 1970 to 2003, Federal Reserve–type models did somewhat better at predicting long‐horizon returns than did a mean reversion model based on dividend yields and price‐earnings multiples. However, timing investment strategies based on any of these prediction models do no better than a buy‐and‐hold strategy. Although some predictability of returns exists, there is no evidence of any systematic inefficiency that would enable investors to earn excess returns.     

INDUSTRY PROSPECTS AND ACQUIRER RETURNS IN DIVERSIFYING TAKEOVERS

We use a sample of 816 diversifying takeovers from 1978 to 2003 to examine whether takeover announcements release negative information about the future prospects of the acquirer's main industry. We find that rivals that are most similar to the acquirer (homogeneous rivals) experience significant negative cumulative abnormal returns (CAR) around takeover announcements. Takeovers that result in negative wealth effects to acquirers are associated with negative abnormal revisions in analysts' forecasts of homogeneous rivals' earnings per share. We also find a decline in the posttakeover operating performance of rival firms. The decline is especially pronounced for homogeneous rivals and for takeovers with negative wealth effects to acquirers. Our findings imply that CAR-based estimates of acquirer wealth gains from takeovers that do not account for industrywide information releases are significantly biased downward.     

THE ECONOMIC VALUE OF USING REALIZED VOLATILITY IN FORECASTING FUTURE IMPLIED VOLATILITY

We examine the economic benefits of using realized volatility to forecast future implied volatility for pricing, trading, and hedging in the S&P 500 index options market. We propose an encompassing regression approach to forecast future implied volatility, and hence future option prices, by combining historical realized volatility and current implied volatility. Although the use of realized volatility results in superior performance in the encompassing regressions and out-of-sample option pricing tests, we do not find any significant economic gains in option trading and hedging strategies in the presence of transaction costs.     

ABNORMAL PERFORMANCE IN SMALL PORTFOLIOS WITH EVENT‐INDUCED VOLATILITY: THE CASE OF STOCK SPLITS

In this article I present a test for detecting abnormal returns when the event analyzed induces volatility and the portfolio is small. The results show that the test is well specified and leads to significant gains in power. I subsequently analyze the abnormal returns around the stock split ex date according to the split factor and find significant abnormal returns only when the factor is greater than 2. The varying motives behind the splits may explain this finding.     

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.     

IDENTIFYING REGIME CHANGES IN MARKET VOLATILITY

A casual inspection of a graph of volatility indexes over time indicates that volatility has undergone infrequent, but significant, shifts in its average level. The purpose of this article is to test for multiple structural breaks in the mean level of market volatility measured by the VIX and VXO, and to identify statistically the dates of these mean shifts. We find evidence of three distinct periods: pre‐1992, 1992–1997, and post‐1997. We find that the mean volatility, as well as its standard deviation, was lowest during 1992–1997. Our findings provide statistical evidence consistent with popular beliefs that market volatility changes over time.     

HEDGE FUND SURVIVAL: NON-NORMAL RETURNS, CAPITAL OUTFLOWS, AND LIQUIDITY

We analyze the factors that influence the survival probability of hedge funds reported in the Lipper TASS database. Particular emphasis is placed on (1) non-normality of returns and assets under management (AUM), (2) short-term capital outflows, and (3) liquidity constraints associated with a hedge fund's cancellation policy. Estimation results using the Cox proportional hazards model and the panel logit model show that (1) funds with lower skewness in returns and AUM, (2) funds experiencing instantaneous rapid capital outflows, and (3) funds with a shorter redemption notice period and a higher redemption frequency have significantly higher liquidation probabilities, among others.