Stock price clustering on option expiration dates

This paper presents striking evidence that option trading changes the prices of underlying stocks. In particular, we show that on expiration dates the closing prices of stocks with listed options cluster at option strike prices. On each expiration date, the returns of optionable stocks are altered by an average of at least 16.5 basis points, which translates into aggregate market capitalization shifts on the order of $9 billion. We provide evidence that hedge rebalancing by option market makers and stock price manipulation by firm proprietary traders contribute to the clustering.     

The Timing of Opening Trades and Pricing Errors

After demonstrating that a zero investment trading strategy that buys stocks with overnight returns below the market average and sells stocks with overnight returns above the market average earns more than 1% monthly profit, I demonstrate that this profit is greater for stocks that start trading more quickly than for other stocks. These results control for trading costs. The resulting pricing errors are a material portion of stock price volatility and suggest that a quick response to overnight information adds non‐information‐based stock volatility to stock prices.     

Can margin traders predict future stock returns in Japan?

A growing body of literature suggests that investor sentiment affects stock prices both at the firm level and at the market level. This study examines the relationship between investor behavior and stock returns focusing on Japanese margin transactions using weekly data from 1994 to 2003. Margin trading is dominated by individual investors in Japan. In analysis at the firm level, we find a significant cross-sectional relationship between margin buying and stock returns. Both market-level and firm-level analyses show that margin buying traders follow herding behavior. They seem to follow positive feedback trading behavior for small-firm stocks and negative feedback trading behavior for large firm stocks. Our results show that information about margin buying helps predict future stock returns, especially for small-firm stocks at short horizons. The predictive power does not diminish even after controlling for firm size and liquidity.     

A network perspective of the stock market

Complex networks are constructed to study correlations between the closing prices for all US stocks that were traded over two periods of time (from July 2005 to August 2007; and from June 2007 to May 2009). The nodes are the stocks, and the connections are determined by cross correlations of the variations of the stock prices, price returns and trading volumes within a chosen period of time. Specifically, a winner-take-all approach is used to determine if two nodes are connected by an edge. So far, no previous work has attempted to construct a full network of US stock prices that gives full information about their interdependence. We report that all networks based on connecting stocks of highly correlated stock prices, price returns and trading volumes, display a scalefree degree distribution. The results from this work clearly suggest that the variation of stock prices are strongly influenced by a relatively small number of stocks. We propose a new approach for selecting stocks for inclusion in a stock index and compare it with existing indexes. From the composition of the highly connected stocks, it can be concluded that the market is heavily dominated by stocks in the financial sector.     

Which past returns affect trading volume?

Anecdotal evidence suggests and recent theoretical models argue that past stock returns affect subsequent stock trading volume. We study 3,000 individual investors over a 51 month period to test this apparent link between past returns and volume using several different panel regression models (linear panel regressions, negative binomial panel regressions, Tobit panel regressions). We find that both past market returns as well as past portfolio returns affect trading activity of individual investors (as measured by stock portfolio turnover, the number of stock transactions, and the propensity to trade stocks in a given month). After high portfolio returns, investors buy high risk stocks and reduce the number of stocks in their portfolio. High past market returns do not lead to higher risk taking or underdiversification. We argue that the only explanations for our findings are overconfidence theories based on biased self-attribution and differences of opinion explanations for high levels of trading activity.     

Computerized stock screening rules for portfolio selection

Recent studies have uncovered several systematic patterns that increase the probability that individual investors can select stock portfolios with excess returns. This study tests the feasibility of using a commercially available computerized stock screening program for investors to take advantage of these patterns. The screening program searches the three major exchanges and selects stocks on both fundamental and technical indicators: low price-to-sales ratio, small firm size, accelerating stock prices above their 50 day moving average, high trading volume, and high earnings growth. Of the 18 models tested between 1994 and 1998, those that allow for selection between exchanges yield portfolio returns that significantly exceed the average market indices.     

The information content of stock split announcements: Do options matter?

We provide a new test of the informational efficiency of trading in stock options in the context of stock split announcements. These announcements tend to be associated with positive abnormal returns. Our traditional event study results show abnormal returns that are significantly lower for optioned than non-optioned stocks, whether traded on the NYSE, Amex, or Nasdaq. After controlling for market returns, capitalization, book-to-market ratio, and trading volume, we find that the abnormal returns are significantly lower for NYSE/Amex optioned than non-optioned stocks. Although the results for Nasdaq stocks are not as clear, the overall effects tend to be lower after optioning. These findings are consistent with the hypothesis that the prices of optioned stocks embody more information, diminishing the impact of the stock split announcement. They provide new evidence of the beneficial effects of options on their underlying stocks.     

Trading Behavior and the Unbiasedness of the Market Reaction to Dividend Announcements

This article examines the price formation process during dividend announcement day, using daily closing prices and transactions data. We find that the unconditional positive excess returns, first documented by Kalay and Loewenstein (1985) , are higher for small-firm and low-priced stocks. Price volatility and trading volume also increase during this period. Examination of trade prices relative to the bid-ask spread and volume of trades at bid and asked prices shows that the excess returns cannot be attributed to measurement errors or to spillover effects of tax-related ex-day trading. Rather, the price behavior is related to the absorption of dividend information.     

Earnings and Expected Returns

The aggregate dividend payout ratio forecasts excess returns on both stocks and corporate bonds in postwar U.S. data. High dividends forecast high returns. High earnings forecast low returns. The correlation of earnings with business conditions gives them predictive power for returns; they contain information about future returns that is not captured by other variables. Dividends and earnings contribute substantial explanatory power at short horizons. For forecasting long-horizon returns, however, only (scaled) stock prices matter. Forecasts of low long-horizon stock returns in the mid-1990s are caused not by earnings or dividends, but by high stock prices.     

Out-of-Sample Predictability in International Equity Markets: A Model Selection Approach

For 13 major international stock markets, there is much evidence of out-of-sample predictability for growth stocks especially when evaluated with economic criteria, and to a noticeably lesser extent for general stock markets and value stocks. Our results shed light on the recent debate about stock return predictability, using different assets (growth-style indexes), forecasting variables (past returns), forecasting models (nonlinear models), and alternative forecasting evaluation criteria (economic criteria). Our analysis suggests that (growth) stock returns might be predictable.     

Volume autocorrelation,information, and investor trading

This study investigates whether the widely documented daily correlated trading volume of stocks is driven by individual investor trading, institutional trading, or both. We find that at least 95% of NYSE and AMEX stocks exhibit statistically significant, positive serial correlation. Volume autocorrelation decreases with the level of institutional ownership of a stock. We also show that the rate of arrivals of new information to the market contributes to the clustering of trades. When there is high information flow to the market, institutional trading generates a more pronounced effect on volume autocorrelation than individual investor trading. Our results are broadly consistent with the predictions of trading volume patterns suggested by most theoretical models of stock trading and by empirical research on investor trading.     

Did institutions herd during the internet bubble?

We examine the trading behavior of institutional investors during the internet bubble and crash of 1998–2001, and its impact on stock prices. Similar to some recent findings concerning the trading behavior of hedge funds and NASDAQ 100 stocks, we find that during the bubble all types of institutions herded with great intensity into internet stocks for a comprehensive sample of institutional investors and internet stocks. In addition to this, we present three entirely new results. First, institutional herding was much greater than what can be explained by momentum trading. Second, institutions as a group continued to increase their holdings of internet stocks for two quarters past the market peak during the first quarter of 2000, and three quarters past the peak for individual stock prices, suggesting that institutions were unable to time the price peaks. Finally and most importantly, we find positive abnormal returns contemporaneous with institutional herding and negative abnormal returns (reversals) at the point that herding ceased. This finding suggests that institutions’ trading created temporary price pressures, and may have contributed to the bubble.     

Asset Price Dynamics and Infrequent Feedback Trades

This article combines the continuous arrival of information with the infrequency of trades, and investigates the effects on asset price dynamics of positive and negative-feedback trading. Specifically, we model an economy where stocks and bonds are traded by two types of agents: speculators who maximize expected utility, and feedback traders who mechanically respond to price changes and infrequently submit market orders. We show that positive-feedback strategies increase the volatility of stock returns, and the response of stock prices to dividend news. Conversely, the presence of negative-feedback traders makes stock returns less volatile, and prices less responsive to dividends.     

Opinion Divergence Among Professional Investment Managers

Abstract:  We find that opinion divergence among professional investment managers is commonplace, using a large sample of transaction-level institutional trading data. When managers trade together, future returns are similar regardless if they are all buying or selling, inconsistent with the notion that professional investment managers possess stock picking ability or private information that is of investment value. However, when managers trade against each other, subsequent returns are low, especially for stocks that are difficult to short. This U-shaped disagreement-return relationship is consistent with Miller's (1977) hypothesis that, in the presence of short-sale constraints, opinion divergence can cause an upward bias in prices.     

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.     

The settlement day effect in the French Bourse

This article investigates the market-microstructure implications on stock returns and volatility of the settlement procedure of stock transactions. The question is, to what extent can a practical trading rule be implemented around the settlement day, to yield above normal risk-adjusted returns, when settling stock transactions is delayed by several weeks, e.g., a month, on the French stock market? Based on French data for the period 1987–1989 we observed, on average, a positive jump of stock prices between the close of the settlement day and the opening of the next trading day (0.97%) which was significantly greater than the one-month interest rate. The average of daily rates of return for non-settlement days was slightly positive (0.01%). These results are found to be very robust. We thus reach the conclusion that buying stocks on the close of the settlement day and selling them back the next day should have been, on average, a profitable strategy in the French Bourse during the period 1987–1989. However, transaction costs and risk premiums may reduce or even eliminate the recorded profit potential.     

STAR and ANN models: forecasting performance on the Spanish “Ibex-35” stock index

This paper studies whether it is possible to exploit the nonlinear behaviour of daily returns on the Spanish Ibex-35 stock index returns to improve forecasts over short and long horizons. In this sense, we examine the out-of-sample forecast performance of smooth transition autoregression (STAR) models and artificial neural networks (ANNs). We use one-step (obtained by using recursive and nonrecursive regressions) and multi-step-ahead forecasting methods. The forecasts are evaluated with statistical and economic criteria. In terms of statistical criteria, we compared the out-of-sample forecasts using goodness of forecast measures and various testing approaches. The results indicate that ANNs consistently surpass the random walk model and, although the evidence for this is weaker, provide better forecasts than the linear AR model and the STAR models for some forecast horizons and forecasting methods. In terms of the economic criteria, we assess the relative forecast performance in a simple trading strategy including the impact of transaction costs on trading strategy profits. The results indicate a better fit for ANN models, in terms of the mean net return and Sharpe risk-adjusted ratio, by using one-step-ahead forecasts. These results show there is a good chance of obtaining a more accurate fit and forecast of the daily stock index returns by using one-step-ahead predictors and nonlinear models, but that these are inherently complex and present a difficult economic interpretation.     

The 1994–1995 Mexican Currency Crisis and U.S. Bank Stock Returns

We investigate the impact of the 1994–1995 Mexican currency crisis on U.S. bank stock returns. We use a jump-diffusion model rather than a pure diffusion model to describe daily stock returns, because the public release of unexpected information generally is associated with discrete jumps in prices. Traditional event study models pool both announcement effects and trading effects and may lead to inefficient estimators. The jump-diffusion model can separate the impact of informed trading from unanticipated public announcements. Our results indicate that the variance of the jumps is large and increases with bank size and portion of loans to Mexico.     

Option trading after the opening bell and intraday stock return predictability

Prior literature finds that information is reflected in option markets before stock markets, but no study has explored whether option volume soon after market open has predictive power for intraday stock returns. Using novel intraday signed option-to-stock volume data, we find that a composite option trading score (OTS) in the first 30 min of market open predicts stock returns during the rest of the trading day. Such return predictability is greater for smaller stocks, stocks with higher idiosyncratic volatility, and stocks with higher bid–ask spreads relative to their options’ bid–ask spreads. Moreover, OTS is a significantly stronger predictor of intraday stock returns after overnight earnings announcements. The evidence suggests that option trading in the 30 min after the opening bell has predictive power for intraday stock returns.