One Day in the Life of a Very Common Stock (original) (raw)

What a Difference a Day Makes: On the Common Market Microstructure of Trading Days

SSRN Electronic Journal, 2000

This paper analyzes the issue of interday stability of the price process using transaction data. While the vast majority of empirical studies on the micostructure of nancial markets based on high frequency data rests on the tacit assumption that observed prices are generated by a unique price process, implying the existence of a common set of parameters driving this process, we question this assumption by means of a minimum distance estimation framework. Starting from estimates speci c for each d a y's price process, the proposed procedure enables us to work out a common structure accross trading days and allows us to disentangle the pecularities of trading days which are marked by certain news events.

The information content of the trading process

Journal of Empirical Finance, 1997

The trade process is a stochastic process of transactions interspersed with periods of inactivity. The realizations of this process are a source of information to market participants. They cause prices to move as they affect the market maker's beliefs about the value of the stock. We fit a model of the trade process that allows us to ask whether trade size is important, in that large and small trades may have different information content (they do, but this varies across stocks); whether uninformed trade is i.i.d. (it is not); and, whether large buys and large sells are equally informative (they differ only marginally). The model is fitted by maximum likelihood using transactions data on six stocks over 60 days.

Informed Trading During the Intraday: The Case of Short Sellers

SSRN Electronic Journal, 2013

This project applies the methods of functional data analysis (FDA) to intra-daily returns of US corporations. It focuses on an extension of the Capital Asset Pricing Model (CAPM) to such returns. The CAPM is essentially a linear regression with the slope coefficient β. Returns of an asset are regressed on index return. We compare the estimates of β obtained for the daily and intra-daily returns. The variability of these estimates is assessed by two bootstrap methods. All computations are performed using statistical software R. Customized functions are developed to process the raw data, estimate the parameters and assess their variability. The results turn out to be: First, the estimates of β obtained for the intradaily returns have bigger absolute values than those for the daily returns; secondly, to assess the variability of the estimates of β obtained for the intra-daily returns, residual bootstrap method is more reliable than pairwise bootstrap method; thirdly, the estimates of β obtained for the intra-daily returns are much higher in absolute values in 2004 than those in any other years.

The Cross-Section of Expected Trading Activity

Review of Financial Studies, 2006

This paper studies cross-sectional variations in trading activity for a comprehensive sample of NYSE/AMEX and Nasdaq stocks over a period of about 40 years. We test whether trading activity depends upon the degree of liquidity trading, the mass of informed traders, and the extent of uncertainty and dispersion of opinion about fundamental values. We hypothesize that liquidity (or noise) trading depends both on a stock's visibility and on portfolio rebalancing needs triggered by past price performance. We use firm size, age, price and the book-to-market ratio as proxies for a firm's visibility. The mass of informed agents is proxied by the number of analysts, while forecast dispersion and firm leverage proxy for differences of opinion. Earnings volatility and absolute earnings surprises proxy for uncertainty about fundamental values. Overall, the results provide support for theories of trading based on stock visibility, portfolio rebalancing needs, dif-

Short Term Predictability, Volume and Microstructure Effects in Stock Prices

This paper documents strong evidence for short term predictability of individual stocks in the London Stock Exchange. We find empirical evidence for price reversals after large price changes and price continuation after small price changes. Our results indicate that large companies seem to react more efficiently to previous price changes and that the effect has been robust over time. The results are robust to nonnormality of returns and are not explained by market microstructure effects like nonsynchronous trading or bid-ask bounce. We further investigate whether the effect can be explained by rational asset-pricing theory or behavioral theories. In these investigations we employ volume as an additional explanatory variable. Finally when considering whether the predictability has any implications for market efficiency we show that the size of bid-ask spreads are highly related to previous price movements.

Information-based trading, price impact of trades, and trade autocorrelation

Journal of Banking & Finance, 2005

In this study we show that both the price impact of trades and serial correlation in trade direction are positively and significantly related to the probability of information-based trading (PIN). The positive relation remains significant even after controlling for the effects of stock attributes. Higher trading activity (i.e., shorter intervals between trades) induces both larger price impact and stronger positive serial correlation in trade direction. The effect of time interval between trades on quote revision is stronger for stocks with higher PIN values. These results provide direct empirical support for the information models of trade and quote revision.

The Non-Information Cost of Trading and Its Relative Importance in Asset Pricing

Using intraday order-flow data for a broad and long sample of NYSE/AMEX stocks, we show that the non-information component of trading costs is priced in the cross-section of stock returns. The pricing effect of the non-information component is stronger in January than in other months of the year. More importantly, we show that the non-information component is much larger and more strongly related to stock returns than the adverse-selection component, indicating that the non-information component plays a more important role in asset pricing than the adverse-section component. We conduct a variety of robustness tests and show that our main results hold for different estimation methods, measures of the adverse-selection cost, sub-sample periods, and control variables. We offer plausible explanations for these results.

Prices, Liquidity, and the Information Content of Trades

Review of Financial Studies, 2000

We investigate the effect of asymmetric information on prices and liquidity by analyzing trades, quotes, spreads and depths. Information content should increase with trade size and the degree of information asymmetry of the trading period. Results show that price and liquidity effects are significantly associated with information content as measured by both trade size and the timing of the trade relative to information events. Results are stronger for purchases than sales. Quoted prices are better measures of information effects than transaction prices, because they control for bid-ask bounce. Finally, trades that are known a priori not to contain information have no impact on prices and liquidity, even when they are very large in size.

Information, trading, and volatility

Journal of Financial Economics, 1994

We examine the effects of trading and information flows on the short-run behavior of stock prices by comparing the behavior of stock return volatility during trading and nontrading periods. We define nontrading periods as periods when exchanges and businesses are open but traders endogenously choose not to trade. After correcting for the bid/ask bounce and stickiness in quotes, we find that a large proportion of daily stock return volatility occurs without trades, especially for large firms. Furthermore, we provide new evidence that public (versus private) information is the major source of short-term return volatility.