Fundamentals,Misvaluation, and Business Investment

Does stock market misvaluation affect business fixed investment? To answer this question, we provide evidence based on U.S. firm‐level panel data. We examine the orthogonality conditions for the investment Q and Euler equations, and our qualitative tests reject the null hypothesis that investment is unaffected by misvaluation (this result is not driven exclusively by the late 1990s). To measure the quantitative effects on investment, we introduce a measure of misvaluation into standard investment equations. Our estimates imply that a one‐standard‐deviation increase in misvaluation increases investment between 20% and 60% relative to the mean level of investment in the sample.     

Investor attention,aggregate limit-hits,and stock returns

We develop a simple measure of investor attention by aggregating the number of days that a stock hits the upper or lower limit on a monthly basis. This attention proxy describes investor trading behavior and contains information of future stock returns. Using data from the Chinese equity market from 2002 to 2017, we provide extensive evidence that the investor attention captured by our measure negatively predicts cross-sectional stock returns, and the long–short trading strategy based on this attention measure produces significant economic value. We argue that the attention-motivated trading is the main cause behind the return predictability of aggregate limit-hits.     

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.     

Uncertainty or Misvaluation? New Evidence on Determinants of Merger Activity from the Banking Industry

We use data from the past 30 years of takeover activity in the U.S. banking industry to test competing neoclassical and misvaluation merger theories. Test results are consistent with evidence in the literature that merger activity is significantly related to both structural industry change and stock price misvaluation. Our primary contribution is to show that changes in misvaluation reflect a rise in industry‐wide risk taking and that increases in risk originate from changes in industry structure due to deregulation. A measure of bank risk taking subsumes the power of stock price misvaluation to explain subsequent merger activity.     

Liquidity and asset pricing: Evidence from the Hong Kong stock market

This study investigates the role of liquidity in pricing stock returns in the Hong Kong stock market. Our results show that liquidity is an important factor for pricing returns in Hong Kong after taking well-documented asset pricing factors into consideration. The results are robust to adding portfolio residuals and higher moment factor in the factor models. The results are also robust to seasonality, and conditional-market tests. We also compare alternative factor models and find that the liquidity four-factor model (market excess return, size, book-to-market ratio, and liquidity) is the best model to explain stock returns in the Hong Kong stock market, while the momentum factor is not found to be priced.     

Value, valuation, and the long-run performance of merged firms

We propose an alternative measure of the long-term economic impact of mergers on firm value: post-acquisition changes in intrinsic value. Consistent with the literature on post-acquisition returns, the intrinsic value of merged firms decreases on average in the three years following deal completion, especially for firms with high initial intrinsic values. The loss of intrinsic value is driven primarily by decreases in expected earnings. Finally, using return decompositions, we find evidence that the poor post-acquisition stock returns documented in other studies can be attributed primarily to lost intrinsic value rather than changes in valuation levels.     

Is Financial Flexibility a Priced Factor in the Stock Market?

This paper develops a factor analysis–based measure for shifts in corporate financial flexibility (FFLEX) that can be observed from public accounting information. Companies that experience positive shifts in FFLEX are associated with higher future investment growth opportunities. We show that FFLEX is a robust determinant of future stock returns. Firms that have increased their financial flexibility are associated with lower stock returns in the subsequent period. A zero‐cost return portfolio produces a significant positive monthly premium of 0.69%, which is driven by covariance (risk). Risk inherent in the flexibility factor is not explained away by either prominent pricing characteristics or factors.     

Distress risk anomaly and misvaluation

This paper examines the effects of misvaluation on the well-documented negative relation between distress risk and stock returns (distress risk anomaly). Findings indicate that distress risk is negatively related to subsequent stock returns only in the subset of the most overvalued stocks, which is consistent with mispricing explanations provided by prior studies. The distress anomaly disappears after controlling for mispricing effects. Further analysis reveals earnings management to be one possible cause for the overvaluation of highly distressed firms. The results are robust to alternative specifications of distress risk and mispricing measures.     

Asset pricing: A tale of two days

We show that asset prices behave very differently on days when important macroeconomic news is scheduled for announcement. In addition to significantly higher average returns for risky assets on announcement days, return patterns are much easier to reconcile with standard asset pricing theories, both cross-sectionally and over time. On such days, stock market beta is strongly related to average returns. This positive relation holds for individual stocks, for various test portfolios, and even for bonds and currencies, suggesting that beta is after all an important measure of systematic risk. Furthermore, a robust risk–return trade-off exists on announcement days. Expected variance is positively related to future aggregated quarterly announcement day returns, but not to aggregated non-announcement day returns. We explore the implications of our findings in the context of various asset pricing models.     

Momentum strategies based on reward–risk stock selection criteria

In this paper, we analyze momentum strategies that are based on reward–risk stock selection criteria in contrast to ordinary momentum strategies based on a cumulative return criterion. Reward–risk stock selection criteria include the standard Sharpe ratio with variance as a risk measure, and alternative reward–risk ratios with the expected shortfall as a risk measure. We investigate momentum strategies using 517 stocks in the S&P 500 universe in the period 1996–2003. Although the cumulative return criterion provides the highest average monthly momentum profits of 1.3% compared to the monthly profit of 0.86% for the best alternative criterion, the alternative ratios provide better risk-adjusted returns measured on an independent risk-adjusted performance measure. We also provide evidence on unique distributional properties of extreme momentum portfolios analyzed within the framework of general non-normal stable Paretian distributions. Specifically, for every stock selection criterion, loser portfolios have the lowest tail index and tail index of winner portfolios is lower than that of middle deciles. The lower tail index is associated with a lower mean strategy. The lowest tail index is obtained for the cumulative return strategy. Given our data-set, these findings indicate that the cumulative return strategy obtains higher profits with the acceptance of higher tail risk, while strategies based on reward–risk criteria obtain better risk-adjusted performance with the acceptance of the lower tail risk.     

Bear beta

We test whether bear market risk, time variation in the probability of future bear market states, is priced. We construct an Arrow–Debreu security that pays off in bear market states (AD Bear) from traded Standard & Poor’s (S&P) 500 index options and use its returns to measure bear market risk. We find that bear beta (exposure to bear market risk) has a strong relation with expected stock returns that is robust, persistent, and remains strong among liquid and large stocks. Historical bear beta also predicts future bear market risk exposure. We conclude that bear market risk is priced in the cross section of stock returns.     

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.     

The empirical risk–return relation: A factor analysis approach

Existing empirical literature on the risk–return relation uses relatively small amount of conditioning information to model the conditional mean and conditional volatility of excess stock market returns. We use dynamic factor analysis for large data sets, to summarize a large amount of economic information by few estimated factors, and find that three new factors—termed “volatility,” “risk premium,” and “real” factors—contain important information about one-quarter-ahead excess returns and volatility not contained in commonly used predictor variables. Our specifications predict 16–20% of the one-quarter-ahead variation in excess stock market returns, and exhibit stable and statistically significant out-of-sample forecasting power. We also find a positive conditional risk–return correlation.     

The Impact of Inflation Measures on the Real Returns and Risk of U.S. Stocks

Using different inflation measures produces economically significant differences in both the inflation record and inflation‐adjusted stock returns. We introduce a more consistent measure of the monthly Consumer Price Index (CPI) inflation rate to better measure real returns over 1913–2004, for which the official CPI exists. We also extend the series backward to 1871 on a monthly basis, an important addition to the data series. We analyze the impact of inflation on the real standard deviation of stock returns and find that, in contrast to the results for geometric mean returns, inflation adjustments have little impact on estimates of return variability.     

On the relationship between concentration of prospect theory/mental accounting investors,cointegration, and momentum

We examine the role of cointegration between stock prices and their estimated fundamental values in return momentum. We find that the positive relationship between capital gains overhang and future stock returns in Grinblatt and Han (2005) is significantly stronger among the “non-cointegrated” group of stocks as compared with the “cointegrated” group of stocks. Further, for the cointegrated stocks, the slower the speed of adjustment to the cointegrating equilibrium, the greater (smaller) is the future return of stocks with unrealized capital gains (losses). These findings are robust to various firm characteristics including firm size, book-to-market ratio, past returns, idiosyncratic volatility, dispersion in analysts’ earnings forecasts, turnover, individual investor ownership, and industry returns.     

Asset Pricing with a Bank Risk Factor

This paper studies how the state of the banking sector influences stock returns of nonfinancial firms. We consider a two‐factor pricing model, where the first factor is the traditional market excess return and the second factor is the change in the average distance to default of commercial banks. We find that this bank factor is priced in the cross section of U.S. nonfinancial firms. Controlling for market beta, the expected excess return for a stock in the top quintile of bank risk exposure is on average 2.83% higher than for a stock in the bottom quintile.     

Corporate financing decisions, managerial market timing, and real investment

Both market timing and investment-based theories of corporate financing predict under-performance after firms raise capital, but only market timing predicts that the composition of financing (equity compared with debt) should also forecast returns. In cross-sectional tests, we find that the amount of net financing is more important than its composition in explaining future stock returns. In the time series, investment-based factor models explain abnormal stock performance following a variety of corporate financing events that previous studies link to market timing. At the aggregate level, the amount of new financing is also more important for future market returns than its composition. Overall, our joint tests reveal that measures of real investment are correlated with future returns and measures of managerial market timing are not.     

MACROECONOMIC NEWS,STOCK TURNOVER,AND VOLATILITY CLUSTERING IN DAILY STOCK RETURNS

We study volatility clustering in daily stock returns at both the index and firm levels from 1985 to 2000. We find that the relation between today's index return shock and the next period's volatility decreases when important macroeconomic news is released today and increases with the shock in today's stock market turnover. Collectively, our results suggest that volatility clustering tends to be stronger when there is more uncertainty and disperse beliefs about the market's information signal. Our findings also contribute to a better understanding of the joint dynamics of stock returns and trading volume.     

Market sentiment: a key factor of investors’ imitative behaviour

The aim of this paper is to explore herding behaviour among investors to determine its rational and emotional component factors and identify relationships among them. We apply causality tests to evaluate the impact of return and market sentiment on herding intensity. The herding intensity is quantified using the measure developed by Patterson and Sharma (2006) . The research was conducted during the period 1997–2003 in the Spanish stock market, where the presence of herding has been confirmed. The results reveal that the herding intensity depends on past returns and sentiment or subjective assessments and confirm the presence of both a rational and an emotional factor.     

Implied volatility and future portfolio returns

Prior studies find that the CBOE volatility index (VIX) predicts returns on stock market indices, suggesting implied volatilities measured by VIX are a risk factor affecting security returns or an indicator of market inefficiency. We extend prior work in three important ways. First, we investigate the relationship between future returns and current implied volatility levels and innovations. Second, we examine portfolios sorted on book-to-market equity, size, and beta. Third, we control for the four Fama and French [Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56.] and Carhart [Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance, 52, 57–82.] factors. We find that VIX-related variables have strong predictive ability.