Information demand and stock market volatility

We study information demand and supply at the firm and market level using data for 30 of the largest stocks traded on NYSE and NASDAQ. Demand is approximated in a novel manner from weekly internet search volume time series drawn from the recently released Google Trends database. Our paper makes contributions in four main directions. First, although information demand and supply tend to be positively correlated, their dynamic interactions do not allow conclusive inferences about the information discovery process. Second, demand for information at the market level is significantly positively related to historical and implied measures of volatility and to trading volume, even after controlling for market return and information supply. Third, information demand increases significantly during periods of higher returns. Fourth, analysis of the expected variance risk premium confirms for the first time empirically the hypothesis that investors demand more information as their level of risk aversion increases.     

The Dynamics of Security Trades,Quote Revisions,and Market Depths for Actively Traded Stocks

We examine the dynamics of return volatility, trading volume, and depth—in an intraday setting for a sample of actively traded NYSE and NASDAQ stocks. We show that depth is a useful intervening variable and mitigates the impact of trading activity on price volatility. Furthermore, depth is affected by the perception of prevailing information asymmetry between informed and uninformed traders. We demonstrate empirically that the NYSE supplies greater depth under conditions of high, perceived information asymmetry as compared to NASDAQ. NASDAQ makes up for this deficiency by its capability of managing large volume shocks without a major decline in depth.JEL Classification: C32, D82, G10     

Competition For Order Flow, Market Quality, And Price Discovery In The Nasdaq 100 Index Tracking Stock

We investigate competition for order flow, market quality, and price discovery in the Nasdaq 100 Index Tracking Stock (QQQ). The QQQ, an AMEX‐listed, exchange‐traded fund, is the most actively traded security in the U.S. equities market. On July 31, 2001, the NYSE began trading the QQQ, marking the first time it traded securities of companies it does not list. The greatest volume of trading takes place on electronic communication networks (ECNs), following by trading on the AMEX and the NYSE. Most of the block trades are executed on the AMEX, where the bid‐ask spreads are narrower. We find that ECNs contribute the most to the price‐discovery process. The spreads on all trading platforms have decreased and market quality and price discovery have improved since QQQ shares have traded on the NYSE.     

Time and dynamic volume–volatility relation

This paper examines volume and volatility dynamics by accounting for market activity measured by the time duration between two consecutive transactions. A time-consistent vector autoregressive (VAR) model is employed to test the dynamic relationship between return volatility and trades using intraday irregularly spaced transaction data. The model is used to identify the informed and uninformed components of return volatility and to estimate the speed of price adjustment to new information. It is found that volatility and volume are persistent and highly correlated with past volatility and volume. The time duration between trades has a negative effect on the volatility response to trades and correlation between trades. Consistent with microstructure theory, shorter time duration between trades implies higher probability of news arrival and higher volatility. Furthermore, bid–ask spreads are serially dependent and strongly affected by the informed trading and inventory costs.     

Impacts of trades in an error-correction model of quote prices

In this paper we analyze and interpret the quote price dynamics of 100 NYSE stocks stratified by trade frequency. We specify an error-correction model for the log difference of the bid and the ask price with the spread acting as the error-correction term, and include as regressors the characteristics of the trades occurring between quote observations, if any. From this model we are also able to extract the implied model for the spread and the mid-quote. We find that short duration and medium volume trades have the largest impacts on quote prices for all one hundred stocks. Further, we find that buys have a greater impact on the ask price than on the bid price, while sells have a greater impact on the bid price than on the ask price. Both buys and sells increase spreads in the short run, but in the absence of further trades, the spreads mean revert. Trades have a greater impact on quotes for the infrequently traded stocks than for the more actively traded stocks.     

Stock market structure and volatility

The procedure for opening stocks on the NYSE appears to affectprice volatility. An analytical framework for assessing themagnitude of the structurally induced volatility is presented.The ratio of variance of open-to-open returns to close-to-closereturns is shown to be consistently greater than one for NYSEcommon stocks during the period 1982 through 1986. The greatervolatility at the open is not attributable to the way in whichpublic information is released since both the open-to-open returnand the close-to-close return span the same period of time.Instead, the greater volatility appears to be attributable toprivate information revealed in trading and to temporary pricedeviations induced by specialist and other traders. The impliedcost of immediacy at the open is significantly higher than atthe close. Other empirical evidence in this article documentsthe volume of trading at the open, the time delays between theexchange opening and the first transaction in a stock, the differencein daytime volatility versus overnight volatility, and the extendto which volatility is related to trading volume.     

Weather and intraday patterns in stock returns and trading activity

We examine the relation between weather in New York City and intraday returns and trading patterns of NYSE stocks. While stock returns are found to be generally lower on cloudier days, cloud cover has a significant influence on stock returns only at the market open. There are significantly more seller-initiated trades when there is more cloud cover at the market open, which is consistent with the return results. Cloudy skies are associated with higher volatility and less market depth over the entire trading day. Finally, cloud cover is not significantly correlated with spread measures and turnover ratios. The findings overall suggest that weather has a significant influence on investors’ intraday trading behavior.     

When is inter-transaction time informative?

This paper estimates the role of inter-transaction time in price discovery for 100 NYSE-listed firms between 1993 and 2003. We find faster arriving trades move prices more than slower arriving trades across stocks and across time. We further document that the information content of inter-transaction time varies with trading activity, and is weakest for the most actively traded stocks. We then distinguish trades in the same direction as the previous trade from trades in the reverse direction. Our empirical findings document that inter-transaction time is informative for both types of trades, but in opposite directions. Faster arriving trades in the same direction are more informative, whereas faster arriving trades in opposite directions are less informative.     

An empirical analysis of the role of the trading intensity in information dissemination on the NYSE

In this paper, we use high-frequency data on five frequently traded stocks listed on the New York Stock Exchange (NYSE) in the year 1999 to examine the price impact of trades and its relation to the trading intensity. We show that the distribution of the absolute price change with fast trading first-order stochastically dominates the distribution of the absolute price change with slow trading. Moreover, we find significant causality from the trade characteristics to the trading intensity. Large trades significantly increase the speed of trading, while large returns tend to decrease the trading intensity. We show that this feedback has little impact on the distribution of the price impact of trades.     

FURTHER EXAMINATION OF PRICE DISCOVERY ON THE NYSE AND REGIONAL EXCHANGES

I re‐examine price discovery on the New York Stock Exchange (NYSE) and regional exchanges. I employ three common‐trend cointegration models to analyze the equilibrium dynamics between the NYSE and regional exchanges for the thirty Dow stocks. The overall results show that whether the regional exchanges free‐ride on the NYSE in obtaining equilibrium prices depends on whether trade prices or quotes are examined. The regional exchanges play a significant (though less important) role in the price‐discovery process for trade prices. However, the contributions of regional exchanges in price discovery of quotes are negligible. I explain the inconsistency between the results using quotes and those using trades. I also highlight the problems of using either quotes or trades in examining this free‐riding hypothesis and suggest future research on the different informativeness of trades on the NYSE and regional exchanges. JEL classification: G20, C32.     

The Informational Role of Stock and Option Volume

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

The Effect of Option Listing on Bid-Ask Spreads,Price Volatility,and Trading Activity of the Underlying OTC Stocks

This paper examines the changes in spreads, price volatility, and trading activity surrounding option listing for a sample of 144 OTC stocks. For this sample, both price volatility and volume increase, but the evidence on spreads is mixed. The increase in price volatility is attributed primarily to an increase in residual return variances. Furthermore, price volatility increases even after controlling for volume, insider trading, and spreads. Although these variables do not fully explain the causes for the increase in price volatility after option listing, the results suggest that liquidity trading or volume has a stronger effect on price volatility than insider trading. This study also finds that both the number of trades and institutional holdings show substantial increases, which are supportive of the notion that listing of options on OTC stocks attracts more attention.     

Dynamic Volume-Return Relation of Individual Stocks

We examine the dynamic relation between return and volume ofindividual stocks. Using a simple model in which investors tradeto share risk or speculate on private information, we show thatreturns generated by risk-sharing trades tend to reverse themselves,while returns generated by speculative trades tend to continuethemselves. We test this theoretical prediction by analyzingthe relation between daily volume and first-order return autocorrelationfor individual stocks listed on the NYSE and AMEX. We find thatthe cross-sectional variation in the relation between volumeand return autocorrelation is related to the extent of informedtrading in a manner consistent with the theoretical prediction.     

French and U.S. trading of cross-listed stocks around the period of U.S. decimalization: Volume, spreads, and depth effects

We analyze how U.S. decimalization affects stocks cross-listed in France (Euronext) and the U.S. (NYSE). The French stocks examined are much larger than the non-U.S. stocks examined in prior studies of decimalization, and their U.S. trading is likely to be dominated by institutions. So, we explore whether a reduction in depths in the U.S. due to decimalization makes the U.S. market less competitive for institutions trading these French stocks. We find evidence consistent with the above. First, the average NYSE trade size for these stocks relative to that on Euronext declines substantially after decimalization. Second, we categorize individual trades by the number of shares traded. We find that mainly driven by large trades, the NYSE proportion of trading of French firms declines markedly after decimalization. Third, using regression analysis, we find that the decline in the U.S. share of institutional trading volume is significantly positively related with the decline in NYSE depths relative to Euronext, and the decline is greater for French firms. Overall, we find consistent results indicating a migration of institutional order flow in French firms to France after NYSE decimalization. Also, intraday analysis indicates that the institutional volume in both France and the U.S. is greatest when both the markets are open.     

The Effectiveness of Price Limits and Stock Characteristics: Evidence from the Shanghai and Shenzhen Stock Exchanges

We examine the effectiveness of price limits on Chinese A shares and investigate the characteristics of those stocks that hit their price limits more frequently. We find that the effect of price limits is asymmetric for the A shares in upward and downward price movements and different for bullish and bearish sample periods. During a bullish period price limits effectively reduce stock volatility for downward price movements, but not for upward price movements; while during a bearish period price limits effectively reduce stock volatility for upward price movements, but not for downward price movements. Second, price limits delay efficient price discovery for upward price movements, but not for downward price movements. However, we do not find evidence to suggest that price limits harmfully interfere with the stock trading processes in the Chinese A share markets. Finally, we find that actively traded stocks hit their price limits more often and tend to hit the lower limit more frequently when overall market conditions are bearish. Stocks with high book-to-market values of equity hit their upper price limits more frequently, while stocks with a high ratio of tradable shares tend to hit their price limits less frequently.JEL Classification: G10, G14, G15     

Trading frequency and the efficiency of price discovery in a non-dealer market

The increasing popularity of non-dealer security markets that offer automated, computer-based, continuous trading reflects a presumption that institutionally-set trading sessions are economically obsolete. This theoretical paper investigates the effect of the trading frequency, a key feature of the trading mechanism, on the efficiency of price discovery in a non-dealer market. By tracing the market pricing error to the correlation structures of arriving information and pricing errors of individual traders, the effect of diverging expectations on error-based and overall return volatility is isolated. The analysis reveals that, due to a portfolio effect, an increase in the trading time interval has contradictory effects on the portion of return volatility stemming from pricing errors. The greater accumulation of information increases error-based return volatility, but the greater volume and number of traders per session have the opposite effect. The net effect on overall return volatility can go either way. It is found that the return volatility of heavily traded securities is likely to be minimized under continuous trading, but that of thinly traded securities may be minimized under discrete trading at moderate time intervals. The latter is more likely to occur the greater is the divergence of expectations among traders. These findings challenge the presumption that automated continuous trading in a non-dealer market is more efficient than discrete trading for all securities, regardless of trading volume. The findings are applicable to all economies, but have special importance for developing countries where typically a single market is dominated by small issues and a low volume of trade. As a by-product of the analysis, it is shown how to correct the biased estimate of inter-session price volatility when observations are less frequent than the trading sessions themselves.     

MARKET STRUCTURE AND REPORTED TRADING VOLUME: NASDAQ VERSUS THE NYSE

Trading volume for common stocks is of interest to financial economists, investors, and securities lawyers. NASDAQ is a dealer market where trades with dealers are included in reported trading volume. This procedure does not accurately measure the trading volume by public buyers and sellers. Trading volume reported on the NYSE, which is primarily an auction market, provides a much closer measure of trades by public investors. We examine a sample of firms whose stock traded on the NASDAQ/NMS and subsequently on the NYSE. When trading switches to the NYSE, the firms' trading volume drops to about 50 percent of the volume previously reported on NASDAQ. A control group of firms that switched from the AMEX to the NYSE shows a small, but statistically insignificant, increase in trading volume.     

Competition among Trading Venues: Information and Trading on Electronic Communications Networks

This paper explores the competition between two trading venues, Electronic Communication Networks (ECNs) and Nasdaq market makers. ECNs offer the advantages of anonymity and speed of execution, which attract informed traders. Thus, trades are more likely to occur on ECNs when information asymmetry is greater and when trading volume and stock‐return volatility are high. ECN trades have greater permanent price impacts and more private information is revealed through ECN trades than though market‐maker trades. However, ECN trades have higher ex ante trading costs because market makers can preference or internalize the less informed trades and offer them better executions.     

Stock price clustering and discreteness

Stock prices cluster on round fractions. Clustering increaseswith price level and volatility. and decreases with capitalizationand transaction frequency. Clustering is pervasive. Price clusteringwill occur if traders use discrete price sets to simplify theirnegotiations. Exchange regulations require that most stocksbe traded on eighths. Clustering on larger fractions will occurif traders choose to use discrete price sets based on quarters,halves, or whole numbers. An econometric model of clusteringis derived and estimated. Projections from the result suggestthat traders would frequently use odd sixteenths when tradinglow-price stocks, if exchange regulations permitted tradingon sixteenths.     

Intraday volume and volatility relations with and without public news

This paper reexamines the dynamic relation between intraday trading volume and return volatility of large and small NYSE stocks in two partitioned samples, with and without identifiable public news. We argue that the sequential information arrival hypothesis (SIAH) can be tested only in periods containing public news. After partitioning the sample into periods with and without public news, we find bi-directional Granger-causality between volume and volatility in the presence of public information as hypothesized by the SIAH. Our analysis further suggests that return volatility is higher in the periods with public news, while trading volume is significantly higher in the no-news period; perhaps owing to the importance of private information for trading stocks. Using the sample without public news, we find evidence that volume Granger-causes volatility without feedback. These results are broadly consistent with behavioral models like the overconfidence and biased self-attribution model of [Daniel, K., Hirshleifer, D., Subrahmanyam, A., 1998. Investor psychology and security market under- and over-reactions. Journal of Finance 53, 1839–1885]. It appears that overconfident investors overrate the precision of their private news signals and therefore trade too aggressively in the absence of public news; when public news arrives, investors’ biased self-attribution triggers excessive return volatility.