The intraday lead–lag relation between returns of the Major Market cash index and returns of the Major Market Index futures and S&P 500 futures is investigated. Empirical results show strong evidence that the futures leads the cash index and weak evidence that the cash index leads the futures. The asymmetric lead–lag relation holds between the futures and all component stocks, including those that trade in almost every five-minute interval. Evidence indicates that when more stocks move together (market-wide information) the futures leads the cash index to a greater degree. This suggests that the futures market is the main source of market-wide information.

[The intraday lead-lag relation between returns of the Major Market cash index and returns of the Major Market Index futures and S&P 500 futures is investigated. Empirical results show strong evidence that the futures leads the cash index and weak evidence that the cash index leads the futures. The asymmetric lead-lag relation holds between the futures and all component stocks, including those that trade in almost every five-minute interval. Evidence indicates that when more stocks move together (market-wide information) the futures leads the cash index to a greater degree. This suggests that the futures market is the main source of market-wide information.]

The author develops a model to explain why stock returns are positively cross-autocorrelated. When marketmakers observe noisy signals about the value of their stocks but cannot instantaneously condition prices on the signals of other stocks, which contain marketwide information, the pricing error of one stock is correlated with the other signals. As marketmakers adjust prices after observing true values or previous price changes of other stocks, stock returns become positively cross-autocorrelated. If the signal quality differs among stocks, the cross-autocorrelation pattern is asymmetric. The author shows that both own- and cross-autocorrelations are higher when market movements are larger.

This paper examines the roles of the number of trades, size of trades, and order imbalance (buyer- versus seller-initiated trades) in explaining the volatility–volume relation for a sample of NYSE and Nasdaq stocks. Our results reconfirm the significance of the size of trades, beyond that of the number of trades, in the volatility–volume relation on both markets. After controlling for the return impact of order imbalance, the volatility–volume relation becomes much weaker. For NYSE stocks, the order imbalance in large trade size categories affects the return more than in smaller size categories. For Nasdaq stocks, the largest return impact comes from the order imbalance in maximum-sized Small Order Execution System (SOES) trades.

ABSTRACT I develop a model to explain why stock returns are positively cross‐autocorrelated. When market makers observe noisy signals about the value of their stocks but cannot instantaneously condition prices on the signals of other stocks, which contain marketwide information, the pricing error of one stock is correlated with the other signals. As market makers adjust prices after observing true values or previous price changes of other stocks, stock returns become positively cross‐autocorrelated. If the signal quality differs among stocks, the cross‐autocorrelation pattern is asymmetric. I show that both own‐ and cross‐autocorrelations are higher when market movements are larger.

This paper examines the relation between the stock price synchronicity and analyst activity in emerging markets. Contrary to the conventional wisdom that security analysts specialize in the production of firm-specific information, we find that securities which are covered by more analysts incorporate greater (lesser) market-wide (firm-specific) information. Using the R2 statistics of the market model as a measure of synchronicity of stock price movement, we find that greater analyst coverage increases stock price synchronicity. Furthermore, after controlling for the influence of firm size on the lead–lag relation, we find that the returns of high analyst-following portfolio lead returns of low analyst-following portfolio more than vice versa. We also find that the aggregate change in the earnings forecasts in a high analyst-following portfolio affects the aggregate returns of the portfolio itself as well as those of the low analyst-following portfolio, whereas the aggregate change in the earnings forecasts of the low analyst-following portfolio have no predictive ability. Finally, when the forecast dispersion is high, the effect of analyst coverage on stock price synchronicity is reduced.

We document that instrumental variables known to possess forecast power in equity and bond markets (Treasury bill yields, equity dividend yields, and the ‘junk’ bond premium) also possess forecast power for prices in agricultural, metals, and currency futures markets. The pattern of forecastability in futures is consistent with economic equilibrium as embodied by a two-‘latent-variable’ model. We test whether the latent variables that explain these futures returns coincide with latent variables that explain returns on size-ranked equity portfolios. This hypothesis is rejected, suggesting that futures are subject to different sources of priced risk than are equities.

This article analyzes the intraday interdependence of order flows and price movements for actively traded NYSE stocks and their Chicago Board Options Exchange (CBOE)-traded options. Stock net trade volume (buyer-initiated volume minus seller-initiated volume) has strong predictive ability for stock and option quote revisions, but option net trade volume has no incremental predictive ability. This suggests that informed investors initiate trades in the stock market but not in the option market. On the other hand, both stock and option quote revisions have predictive ability for each other. Thus, while information in the stock market is contained in both quote revisions and trades, information in the option market is contained only in quote revisions.

[We examine the intraday relationship between returns and returns volatility in the stock index and stock index futures markets. Our results indicate a strong intermarket dependence in the volatility of the cash and futures returns. Price innovations that originate in either the stock or futures markets can predict the future volatility in the other market. We show that this relationship persists even during periods in which the dependence in the returns themselves appears to weaken. The findings are robust to controlling for potential market frictions such as asynchronous trading in the stock index. Our results have implications for understanding the pattern of information flows between the two markets.]