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 Flow and Pricing Errors: A Unified Approach to Estimation and Testing

This study examines whether rates of information flow differbetween trading and nontrading periods, and whether the variancesof pricing errors differ at the open and close of trading. Theapproach improves on existing methods by allowing for correlationbetween pricing errors and information flow, and by conductinginferences at the individual security level. The daytime rateof information flow is about seven times the overnight rate,and the variances of pricing errors at the open are not differentfrom those at the close of trading. This evidence differs fromexisting results based on return variance ratios.     

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.     

High Stock Returns before Holidays: Existence and Evidence on Possible Causes

On the trading day prior to holidays, stocks advance with disproportionate frequency and show high mean returns averaging nine to fourteen times the mean return for the remaining days of the year. Over one third of the total return accruing to the market portfolio over the 1963–1982 period was earned on the eight trading days which each year fall before holiday market closings. Examination of hourly pre-holiday stock returns reveals high returns throughout the day. Pre-holiday stock returns in the post-test 1983–1986 period are also examined.     

Stock returns and the weekend effect

This paper examines two alternative models of the process generating stock returns. Under the calendar time hypothesis, the process operates continuously and the expected return for Monday is three times the expected return for other days of the week. Under the trading time hypothesis, returns are generated only during active trading and the expected return is the same for each day of the week. During most of the period studied, from 1953 through 1977, the daily returns to the Standard and Poor's composite portfolio are inconsistent with both models. Although the average return for the other four days of the week was positive, the average for Monday was significantly negative during each of five-year subperiods.     

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.     

Revisiting the empirical linkages between stock returns and trading volume

This paper investigates whether the empirical linkages between stock returns and trading volume differ over the fluctuations of stock markets, i.e., whether the return–volume relation is asymmetric in bull and bear stock markets. Using monthly data for the S&P 500 price index and trading volume from 1973M2 to 2008M10, strong evidence of asymmetry in contemporaneous correlation is found. As for a dynamic (causal) relation, it is found that the stock return is capable of predicting trading volume in both bear and bull markets. However, the evidence for trade volume predicting returns is weaker.     

Option Mispricing around Nontrading Periods

We find that option returns are significantly lower over nontrading periods, the vast majority of which are weekends. Our evidence suggests that nontrading returns cannot be explained by risk, but rather are the result of widespread and highly persistent option mispricing driven by the incorrect treatment of stock return variance during periods of market closure. The size of the effect implies that the broad spectrum of finance research involving option prices should account for nontrading effects. Our study further suggests how alternative industry practices could improve the efficiency of option markets in a meaningful way.     

From dusk till dawn (and vice versa): Overnight-versus-daytime reversals and feedback trading

Although overnight-versus-daytime return reversals have often been ascribed to the heterogeneous clienteles of the overnight and daytime sessions, there exists no evidence to date on how those clienteles' trading behaviour motivates these reversals. We empirically investigate this issue for the first time by assessing whether these reversals are the result of feedback trading during overnight/daytime hours. Drawing on the S&P 500 ETF for the 1993–2021 period, we find that overnight (daytime) feedback trading largely motivates the expected positive (negative) overnight (daytime) returns; in line with this, days entailing the expected negative overnight-versus-daytime return reversals accommodate stronger feedback trading at the daily (i.e., close-to-close) frequency. Daytime feedback trading is present when the immediately preceding overnight session's returns are positive, while overnight feedback trading reveals a strong Monday-effect. We also show that overnight-versus-daytime variations of feedback trading hold across other large US ETFs.     

The Determinants of Conditional Autocorrelation in Stock Returns

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day‐of‐the‐week are potential determinants of conditional autocorrelation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M‐GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time‐varying patterns of return autocorrelation.     

JUNK BOND BEHAVIOR WITH DAILY RETURNS AND BUSINESS CYCLES

Using the Merrill Lynch daily junk bond index, we re-examine and extend previous junk bond return results. We include the effect of business cycles on junk and investment-grade bonds, the sensitivity of junk bond returns to economic activity and/or interest rates, and sample periods that allow us to investigate periods of reduced junk bond liquidity. Daily data confirm the same curious return behavior identified in studies using monthly returns. However, we reveal for the first time that junk bond returns track the movement of interest rates during economically good times but track stock returns during bad times.     

The Nature and Persistence of Initial Public Offering Aftermarket Returns Predictability

This paper examines the predictability of monthly aftermarket returns of initial public offerings during the first six years of trading. Predictability is tested under the null hypothesis of random walk using a Markov chain analysis. The evidence shows that excess returns of IPOs (adjusted for the return on the equally weighted NASDAQ index) demonstrate non-random walk behavior through the first five years of trading and random walk behavior in the sixth year. This is accompanied by predictability of monthly excess returns conditioned on the two previous months' excess returns. A trading strategy is offered to capitalize on the predictability patterns. Implementing the trading strategy is not possible due to institutional barriers, providing additional explanation for why IPOs do not reach their intrinsic values for extended periods of time.     

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.     

Asymmetric return dynamics and technical trading strategies

We investigate the profitability of technical trading strategies based on an asymmetric reverting property of stock returns. We identify an asymmetry in return dynamics for daily returns on the S&P 500 index. Return dynamics evolve along a positive (negative) unconditional mean after a prior positive (negative) return. The trading strategies based on this asymmetry generate a positive return for buy signals, a negative return for sell signals, and a positive return for the spread between buy and sell signals. Our results imply that the observed asymmetry in return dynamics is the main source of profitability for the implied strategies, thereby corroborating arguments for the usefulness of technical trading strategies.     

Technical trading rules for nonlinear dynamics of stock returns: evidence from the G-7 stock markets

This paper explores a possible link between an asymmetric dynamic process of stock returns and profitable technical trading rules. Using the G-7 stock market indexes, we show that the dynamic process of daily index returns is better characterized by nonlinearity arising from an asymmetric reverting property. The asymmetric reverting property of stock returns is exploitable in generating profitable buy and sells signals for technical trading strategies. The bootstrap analysis shows that not all nonlinearities generate profitable buy and sell signals, but rather only the nonlinearities generating a consistent asymmetrical pattern of return dynamics can be exploitable for the profitability of the trading rules. The significant positive (negative) returns from buy (sell) signals are a consequence of trading rules that exploit the asymmetric nonlinear dynamics of the stock returns that revolve around positive (negative) unconditional mean returns under prior positive (negative) return patterns. Our results corroborate the arguments for the usefulness of technical trading strategies in stock market investments.     

Institutional Ownership and Return Reversals Following Short‐Term Return Consistency

Securities with consistently strong positive (negative) returns during the previous two weeks have future returns that are higher (lower) than those that do not. The results hold for various robustness checks, including those involving firm size, share turnover, past return levels, and bid‐ask bounce. The returns to short horizon consistency trading strategies are reliable through time and are both economically and statistically significant. There is also some evidence that longer periods of consistency lead to greater risk‐adjusted profits. Most surprising is that this effect holds only for those firms with high institutional ownership.     

International day-of-the-week effects: An empirical examination of iShares

Ample evidence suggests that day-of-the-week patterns exist in US and foreign equity returns. We extend the evidence on the day-of-the-week effect in equity returns by examining the return patterns of iShares for 17 countries and Standard and Poor's Depository Receipts (SPDRs) to establish whether previously observed predictabilities in equity returns are reflected in iShares' returns. We utilize a split sample to examine return patterns and develop trading rules using the initial subsample. We then test those trading rules out of sample. Empirical results reveal that iShares exhibit day-of-the-week return patterns that can be exploited by informed traders.     

Explaining Monday Returns

The Monday effect is reexamined using two stock indexes and a sample of 452 individual stocks that trade on the London Stock Exchange. The results based on conventional test methods reveal a negative average return on Monday. Extending the analysis to examine the effects of various possible influences simultaneously, the average Monday return becomes positive and does not differ significantly from the average returns of most other days of the week. Fortnight, ex‐dividend day, account period, (bad) news flow, trading activity, and bid‐ask spread effects are all controlled for. The results broadly support the trading time hypothesis.     

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.