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.     

Trading and Returns under Periodic Market Closures

This paper studies how market closures affect investors' trading policies and the resulting return-generating process. It shows that closures generate rich patterns of time variation in trading and returns, including those consistent with empirical findings: (1) U-shaped patterns in the mean and volatility of returns over trading periods, (2) higher trading activity around the close and open, (3) more volatile open-to-open returns than close-to-close returns, (4) higher returns over trading periods than over nontrading periods, (5) more volatile returns over trading periods than over nontrading periods. It also shows that closures can make prices more informative about future payoffs.     

Information, Trading, and Volatility: Evidence from Weather-Sensitive Markets

We find that trading‐ versus nontrading‐period variance ratios in weather‐sensitive markets are lower than those in the equity market and higher than those in the currency market. The variance ratios are also substantially lower during periods of the year when prices are most sensitive to the weather. Moreover, the comovement of returns and volatilities for related commodities is stronger during the weather‐sensitive season, largely due to stronger comovement during nontrading periods. These results are consistent with a strong link between prices and public information flow and cannot be explained by pricing errors or changes in trading activity.     

TRADING TIME EFFECTS IN FINANCIAL AND COMMODITY FUTURES MARKETS

Through the examination of one commodity contract, soybeans, and one financial contract, U.S. Treasury bonds, the authors test to determine (1) whether mean rates of return during trading times differ from mean rates of return during nontrading times; (2) whether mean returns during trading times and nontrading times differ by day of the week; (3) whether trading time returns differ significantly from previous nontrading time returns; and (4) the extent to which trading and previous nontrading returns are correlated. In addition, the authors empirically examine a possible explanation for the results obtained.     

Stock weighting and nontrading bias in estimated portfolio returns

Liu and Strong (2008) note that researchers often employ a simple (but incorrect) averaging approach that induces significant error into estimated buy‐and‐hold portfolio returns. This study explores the additional challenges that arise when stocks are subject to nontrading. We develop a decomposition of the total bias in estimated return into the components attributable to the stock weighting approach and the treatment of nontrading. While the latter is shown to be negligible, the former can approach 150 basis points per month. Our empirical analysis of Australian equities shows that the simple averaging approach tends to overstate the size and book‐to‐market effects, and understate the momentum effect.     

A transactions data analysis of nonsynchronous trading

Weekly returns of stock portfolios exhibit substantial autocorrelation.Analytical studies suggest that nonsynchronous trading is capableof explaining from 5% to 65% of the autocorrelation. The varyingimportance of nonsynchronous trading in these studies arisesprimarily from differing assumptions regarding nontrading periodsof stocks. We simulate the effects of nonsynchronous tradingby sampling stock returns from a return generating process usingtransactions data to obtain the precise time of each stock'slast trade. We find that simulated weekly portfolio returnsexhibit autocorrelations that are roughly 25% that of theirobserved (CRSP) weekly returns.     

Kalman filter estimation for valuing nontrading securities,with applications to the MMI cash-future spread on October 19 and 20, 1987

The Kalman filter is proposed as a method for estimating the value of nontrading securities during periods when other securities are trading. The method also provides confidence intervals that indicate the degree of uncertainty regarding estimated value. The method is applied to the Major Market Index during the principal days of the 1987 stock market crash. Our results indicate that nonsynchronous trading explains a small but significant portion of the cash-futures spread that prevailed during these days.     

The weekend effect on the distribution of stock prices: Implications for option pricing

Evidence of weekend effects on the distribution of security returns suggests that returns are generated by a process operating closer to trading time rather than calendar time. In contrast, accumulation of interest over the weekend follows a calendar-time process. Since both the variance of returns and the interest rate are important parameters of the Black-Scholes option pricing model, this paper suggests that the model be stated to account for this by utilizing a trading-time variance and a calendar-time interest rate. Empirical evidence indicates that this allows the model to better explain market option prices.     

Return autocorrelations around nontrading days

We document a pattern in the serial dependence of security returnsaround nontrading days. ne correlation of returns the secondday after a week-end or holiday with returns first day afteris unusually low, and in many return series is negative, implyinga reversal of price movements. We also document unusually largepositive return autocorrelations the last day before and thefirst day after weekends and holidays. The pattern has existedin equity returns for over 100 years, and also exists in severalfutures markets, implying that the pattern is robust to alternativemarket microstructures.     

Expected Option Returns

This paper examines expected option returns in the context of mainstream asset-pricing theory. Under mild assumptions, expected call returns exceed those of the underlying security and increase with the strike price. Likewise, expected put returns are below the risk-free rate and increase with the strike price. S&P index option returns consistently exhibit these characteristics. Under stronger assumptions, expected option returns vary linearly with option betas. However, zero-beta, at-the-money straddle positions produce average losses of approximately three percent per week. This suggests that some additional factor, such as systematic stochastic volatility, is priced in option returns.     

The Effect of US Stock Market Uncertainty on Emerging Market Returns

We investigate the effects of US stock market uncertainty (VIX) on the stock returns in Latin America and aggregate emerging markets before, during, and after the financial crisis. We find that increases in VIX lead to significant immediate and delayed declines in emerging market returns in all periods. However, changes in VIX explained a greater percentage of changes in emerging market returns during the financial crisis than in other periods. The higher US stock market uncertainty exerts a much stronger depressing effect on emerging market returns than their own-lagged and regional returns. Our risk transmission model suggests that a heightened US stock market uncertainty lowers emerging market returns by both reducing the mean returns and raising the variance of returns. The VIX fears raise the volatility of emerging market returns through generalized autoregressive conditional heteroskedasticity (GARCH)-type volatility transmission processes.     

Overnight information and stochastic volatility: A study of European and US stock exchanges

This paper provides a comprehensive evaluation of the predictive ability of information accumulated during nontrading hours for a set of European and US stock indexes. We introduce a stochastic volatility model, which conditions on lagged overnight information, distinguishes between the nontrading periods of weeknights, weekends, holidays and long weekends, and allows for an asymmetric leverage effect on the impact of overnight news. We implement Bayesian methods for estimation and ranking of the empirical models, and find two key results: (i) there is substantial predictive ability in financial information accumulated during nontrading hours; and (ii) the performance of stochastic volatility models improves considerably by separating the asymmetric impact of positive and negative news made available over weeknights, weekends, holidays and long weekends.     

Bidders’ strategic timing of acquisition announcements and the effects of payment method on target returns and competing bids

Using a sample of acquisition announcements released during trading versus nontrading hours, this study examines how the strategic timing of acquisition announcements determines the impact of the method of payment on target stock returns and competition among bidders. For overnight acquisition announcements, we find that cash payment offers positively and significantly affect acquisition premiums and target returns, yet these results do not hold for daytime announcements. Cash payment offers made during nontrading hours are more likely to deter potential bidders and complete proposed transactions. However, we find no such relationship for daytime announcements. These findings suggest that the timing of acquisition announcements by bidders is important for assessing the effects of payment method as a signal of target valuation and a preemption of competing bids.     

Intrinsic bubbles and Granger causality in the S&P 500: Evidence from long-term data

Results of research on whether changes in earnings can predict future stock returns are inconclusive. We add to this debate by using long-term data from 1871 to 2004 to examine the predictive power of changes in earnings in periods of intrinsic bubbles and in periods absent intrinsic bubbles. Our results show that accounting for bubbles is important in whether changes in earnings can predict future stock returns. In periods of no bubble, we find that changes in earnings Granger-cause future returns, whereas in periods of bubble, this Granger causality from changes in earnings to future returns cannot be found. We conclude that changes in earnings can predict future stock returns, but only in periods absent bubbles.     

Determinants of S&;P 500 index option returns

We analyze common factors that affect returns on S&P 500 index options and find that 93% of the variation in option returns can be explained by three factors, which respectively account for 87%, 4%, and 2% of the variation in option returns. Furthermore, we test diffusion option pricing models by using mean–variance spanning properties implied in the models. The spanning tests reject one-factor diffusion models, as well as the hypothesis that the underlying asset and an equally weighted option index span options. Our results fail to reject that the underlying asset and an at-the-money option can span out-of-the-money options, but does reject that they span in-the-money options.      

Earnings Warnings and CEO Welfare

Some CEOs decide voluntarily to issue a warning when they expect a negative earnings surprise. Prior research suggests that warnings contain incremental information beyond actual earnings; warning firms tend to experience permanent earnings decreases. This paper investigates whether compensation committees take warnings into account in setting CEO compensation. We find that warnings are significantly negatively (positively) associated with CEO bonus (option grants), suggesting that compensation committees adjust CEO compensation towards a more high‐powered structure after warnings. However, the sensitivity of bonus or option grants to earnings and stock returns is not affected except for bonus sensitivity to stock returns. We also find weak evidence of an increase in forced CEO turnover after warnings, accompanied by a significant increase in its sensitivity to stock returns. This benefits CEOs with higher ability but imposes more risk on other CEOs. These findings provide a partial explanation of why not every CEO facing a negative surprise decides to issue a warning. Our results are robust to various specifications. In particular, the impact of warnings on compensation appears invariant to the timing or the number of warnings. Overall, these findings suggest that the signal from warnings is used in determining CEO compensation and retention.     

Returns and option activity over the option-expiration week for S&P 100 stocks

For S&P 100 stocks, we find that the weekly returns over option-expiration (OE) weeks (a month’s third-Friday week) tend to be high, relative to: (1) the third-Friday weekly returns of other stocks with less option activity, (2) the own stock’s other weekly returns, (3) the risk, based on asset-pricing alphas. For these same stocks, a month’s fourth-Friday weekly returns underperform modestly. We suggest the following two avenues are likely partial contributors towards understanding these return patterns: (1) delta-hedge rebalancing by option market makers, with a reduction in short-stock hedge positions over the OE week, and (2) declining risk perceptions over the OE week, as measured by option-derived implied volatilities. Our findings suggest option activity can induce reliable patterns in the weekly returns of option-active large-cap stocks.     

The Impact of Option Introduction on Stock Return Variances: The Role of Bid-Ask Spreads, Return Autocorrelations, and Intrinsic Variances

The purpose of this paper is to examine changes in stock return variances following option introduction. The sample consists of National Market System stocks and employs both transaction returns and returns based on bid and ask quotes. Variances are decomposed into portions attributable to bid-ask spreads, return autocorrelations, and intrinsic variances. Spreads play a negligible role in explaining variance changes. A generally positive component to short-term autocorrelations falls following option introduction, increasing variances over short holding periods. Intrinsic variances fall prior to the October 1987 crash, but do not change after the crash with option introduction.     

Option pricing under linear autoregressive dynamics,heteroskedasticity, and conditional leptokurtosis

Daily returns of financial assets are frequently found to exhibit positive autocorrelation at lag 1. When specifying a linear AR(1) conditional mean, one may ask how this predictability affects option prices. We investigate the dependence of option prices on autoregressive dynamics under stylized facts of stock returns, i.e. conditional heteroskedasticity, leverage effect, and conditional leptokurtosis. Our analysis covers both a continuous and discrete time framework. The results suggest that a non-zero autoregression coefficient tends to increase the deviation of option prices from Black and Scholes prices caused by stochastic volatility.     

中国股票市场上的“隔夜效应”和“午间效应”研究

我国目前关于非交易时期信息对股市影响的研究多集中在"周末效应"和"节日效应"上,而缺少对其他非交易时期的研究。针对这一现状,本文在定义了"午间效应"和"隔夜效应"之后,使用交叠样本法和ARMA—GARCH模型对深沪两市上的午间休市和晚间休市对股票收益率的影响进行了实证分析。研究发现:深沪两市均存在持续稳定的"隔夜效应";同时,在某些年份,两市存在显著的"午间效应",但这种效应不具有持续稳定的特性,而是随着样本期选择的不同而变化的特征。使用5分钟数据进行的抽样频率稳健性检验与上述结论基本一致,证明了本文结论具有较好的稳定性。