STOCK RETURNS AND VOLATILITY ON CHINA'S STOCK MARKETS

We examine time‐series features of stock returns and volatility, as well as the relation between return and volatility in four of China's stock exchanges. Variance ratio tests reject the hypothesis that stock returns follow a random walk. We find evidence of long memory of returns. Application of GARCH and EGARCH models provides strong evidence of time‐varying volatility and shows volatility is highly persistent and predictable. The results of GARCH‐M do not show any relation between expected returns and expected risk. Daily trading volume used as a proxy for information arrival time has no significant explanatory power for the conditional volatility of daily returns. JEL classification: G15     

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.     

Trading imbalances,predictable reversals,and cross-stock price pressure

We test the implications of a multi-asset equilibrium model in which a finite number of risk-averse liquidity providers accommodate non-informational trading imbalances. These imbalances generate predictable reversals in stock returns. An imbalance in one stock also affects the prices of other stocks. The magnitude of the cross-stock price pressure depends on the correlations of the stocks’ underlying cash flows. The model implies that non-informational trading increases the volatility of stock returns. We confirm the model's implications using data from the Taiwan Stock Exchange.     

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.     

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.     

Stale Information,Shocks, and Volatility

We propose a new approach to measuring the effect of unobservable private information on volatility. Using intraday data, we estimate the effect of a well‐identified shock on the volatility of stock returns of European banks as a function of the quality of public information available about the banks. We hypothesize that as publicly available information becomes stale, volatility effects and its persistence increase, as private information of investors becomes more important. We find strong support for this idea in the data. We further show that stock volatility is higher just before important announcements if information is stale.     

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.     

ON THE RATIONALITY OF COMMON STOCK RETURN VOLATILITY

This paper derives the relationship between the population unconditional variance of common stock returns and the variance of expected returns conditional on a well-specified information set. As a consequence, a lower bound is obtained for the variance of common stock returns. The sample counterpart of this bound is then empirically tested against the sample variance of returns. The paper's main conclusion can be stated as follows: the observed volatility of real (inflation-adjusted) common stock returns is not “irrationally” large. The paper admits of this conclusion because the point estimate of the lower-bound variance derived in this model is actually larger than the point estimate of common stock return volatility. However, since these point estimates are found to have a statistically insignificant difference, equality of the two variances cannot be ruled out. Hence, “rationality” of common stock returns—as implied by a utility-based valuation conditional on a specified information set—cannot be rejected.     

DOES VOLATILITY DECREASE AFTER REVERSE STOCK SPLITS?

Previous research documents that volatility decreases after reverse stock splits. I show that measurement effects bias observed volatility, especially for lower priced stocks. Based on observed returns, volatility decreases 25% after reverse splits. Controlling for bid–ask bounce, volatility still decreases for stocks with prices above $5.00. However, for stocks below $2.00, volatility increases slightly. The portion of observed volatility attributable to measurement effects declines as the stock price increases and as the minimum tick size decreases. Finally, there is a significant and positive cross‐sectional relation between changes in the number of trades and changes in volatility after reverse splits.     

Lack of consumer confidence and stock returns

This paper investigates the link between the lack of consumer confidence and stock returns during market fluctuations. Using a Markov-switching framework, we first focus on whether the shock to consumer confidence has asymmetric effects on stock returns. We also examine whether the decreased confidence pushes the stock market into bear territory. Empirical evidence using monthly returns on Standard & Poor's S&P 500 price index suggests that market pessimism has larger impacts on stock returns during bear markets. Moreover, the lack of consumer confidence leads to a higher probability of switching to a bear market regime.     

INDUSTRY EFFECTS OF ANALYST STOCK REVISIONS

We examine the industry valuation effects of analyst stock revisions and identify the variables that influence these effects. Our results show that industry rivals experience significant abnormal returns in response to revision announcements. Although the mean stock price response suggests contagion effects, there is also evidence of significant competitive effects. The valuation effects are influenced by the magnitude of the rated firm's announcement return, along with analyst‐specific and industry‐specific characteristics. However, the sensitivity of the valuation effects to these characteristics is conditioned on whether the industry effects are contagious or competitive.     

The dynamic interaction between volatility and returns in the US stock market using leveraged bootstrap simulations

One of the most important stylized facts in finance is that stock index returns are inversely related to volatility. The theoretical rationale behind the proposition is still controversial. The causal relationship between returns and volatility is investigated in the US stock market over the period 2004-2009 using daily data. We apply a bootstrap test with leveraged adjustments that is robust to non-normality and ARCH. We find that the volatility causes returns negatively and returns cause volatility positively. The policy implications of our findings are discussed in the main text.     

MONETARY POLICY INDICATORS AS PREDICTORS OF STOCK RETURNS

We explore the linkage between stock return predictability and the monetary sector by examining alternative proxies for monetary policy. Using two complementary methods, we document that failure to condition on the Fed's broad policy stance causes a substantial understatement in the ability of monetary policy measures to predict returns. Industry analyses suggest that cross‐industry return differences are also linked to changes in monetary conditions, as monetary policy has the strongest (weakest) relation with returns for cyclical (defensive) industries. Overall, we find that monetary conditions have a prominent and systematic relation with future stock returns, even in the presence of business conditions.     

ANALYZING STOCK MARKET VOLATILITY USING EXTREME‐DAY MEASURES

We develop a simple measure of volatility based on extreme‐day returns and apply it to market returns from 1885 to 2002. Because returns are not normally distributed, the extreme‐day measure, which is distribution free, might provide a better measure of stock market risk than the traditional standard deviation. The extreme‐day measure more accurately explains investor behavior relative to standard deviation as shown by equity fund flows, and we find evidence that large negative changes appear to influence investor behavior more than large positive changes.     

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.     

ON ESTIMATING STOCK MARKET VOLATILITY: AN EXPLORATORY APPROACH

Traditional methods of estimating market volatility use daily return observations from a stock index to calculate monthly variance. We break with tradition and estimate stock market volatility using the daily, cross-sectional standard deviation of returns for all firms trading on the New York Stock Exchange and the American Stock Exchange. We find a significantly positive relation between risk and return. Market volatility is estimated to be about half the volatility level previously reported. The intraday, cross-sectional market volatility measure provides findings consistent with risk-return theory.     

ON STOCK RETURN SEASONALITY AND CONDITIONAL HETEROSKEDASTICITY

We model the seasonal volatility of stock returns using GARCH specifications and size-sorted portfolios. Estimation results indicate that there are volatility differences between months and that these seasonal volatility patterns are conditional on firm size. Additionally, we find that seasonal volatility does not explain seasonal returns when the reward for risk is held constant over the sample period. Specifically, our results indicate that much of the abnormal return in January for small firms cannot be entirely attributed to either higher systematic risk or a higher risk premium in January.     

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.     

A behavioural explanation to the asymmetric volatility phenomenon: Evidence from market volatility index

This study examines how the behavioural explanations, in particular loss aversion, can be used to explain the asymmetric volatility phenomenon by investigating the relationship between stock market returns and changes in investor perceptions of risk measured by the volatility index. We study the behaviour of India volatility index vis‐à‐vis Hong Kong, Australia and UK volatility index, and provide a comprehensive comparative analysis. Using Bai‐Perron test, we identify structural breaks and volatility regimes in the time series of volatility index, and investigate the volatility index‐return relation during high, medium and low volatility periods. Regardless of volatility regimes, we find that volatility index moves in opposite direction in response to stock index returns, and contemporaneous return is the most dominating across the four markets. The negative relation is strongest for UK followed by Australia, Hong Kong and India. Second, volatility index reacts significantly different to positive and negative returns; negative return has higher impact on changes in volatility index than positive return across the markets over full‐sample and sub‐sample periods. The asymmetric effect is stronger in low volatility regime than in high and medium volatility periods for all the markets except UK. The strength of asymmetric effect is strongest for Hong Kong and weakest for India. Finally, negative returns have exponentially increasing effect and positive returns have exponentially decreasing effect on the changes in volatility index.     

A High‐Frequency Investigation of the Interaction between Volatility and DAX Returns

One of the most noticeable stylised facts in finance is that stock index returns are negatively correlated with changes in volatility. The economic rationale for the effect is still controversial. The competing explanations have different implications for the origin of the relationship: Are volatility changes induced by index movements, or inversely, does volatility drive index returns? To differentiate between the alternative hypotheses, we analyse the lead‐lag relationship of option implied volatility and index return in Germany based on Granger causality tests and impulse‐response functions. Our dataset consists of all transactions in DAX options and futures over the time period from 1995 to 2005. Analyzing returns over 5‐minute intervals, we find that the relationship is return‐driven in the sense that index returns Granger cause volatility changes. This causal relationship is statistically and economically significant and can be clearly separated from the contemporaneous correlation. The largest part of the implied volatility response occurs immediately, but we also observe a smaller retarded reaction for up to one hour. A volatility feedback effect is not discernible. If it exists, the stock market appears to correctly anticipate its importance for index returns.