The author systematically examines one of the important issues of information — establishing the market price. He introduces the concept of «search» — where a buyer wanting to get a better price, is forced to question sellers. The article deals with various aspects of finding the necessary information.

Economics of Information

This article develops a theory in which concentrated-trading patterns arise endogenously as a result of the strategic behavior of liquidity traders and informed traders. Our results provide a partial explanation for some of the recent empitical findings concerning the patterns of volume and price variability in intraday transaction data. In the last few years, intraday trading data for a number of securities have become available. Several empirical studies have used these data to identify various patterns in trading volume and in the daily behavior of security prices. This article focuses on two of these patterns; trading volume and the variability of returns. Consider, for example, the data in Table 1 concerning shares of Exxon traded during 1981.1 The U-shaped pattern of the average volume of shares traded-namely, the heavy trading in the beginning and the end of the trading day and the relatively light trading in the middle of the day-is very typical and has been documented in a number of studies. [For example,Jain andJoh (1986) examine hourly data for the aggregate volume on the NYSE, which is reported in the Wall StreetJournal, and find the same pattern.] Both the variance of price changes

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A Theory of Intraday Patterns: Volume and Price Variability

This paper reviews previous and current research on the relation between price changes and trading volume in financial markets, and makes four contributions. First, two empirical relations are established: volume is positively related to the magnitude of the price change and, in equity markets, to the price change per se. Second, previous theoretical research on the price-volume relation is summarized and critiqued, and major insights are emphasized. Third, a simple model of the price-volume relation is proposed that is consistent with several seemingly unrelated or contradictory observations. And fourth, several directions for future research are identified.

https://www.jstor.org/stable/pdfplus/2330874.pdf

The Relation between Price Changes and Trading Volume: A Survey

This paper proposes a new statistical model for the analysis of data which arrive at irregular intervals. The model treats the time between events as a stochastic process and proposes a new class of point processes with dependent arrival rates. The conditional intensity is developed and compared with other self-exciting processes. Because the model focuses on the expected duration between events, it is called the autoregressive conditional duration (ACD) model. Asymptotic properties of the quasi maximum likelihood estimator are developed as a corollary to ARCH model results. Strong evidence is provided for duration clustering for the financial transaction data analyzed; both deterministic time-of-day effects and stochastic effects are important. The model is applied to the arrival times of trades and therefore is a model of transaction volume, and also to the arrival of other events such as price changes. Models for the volatility of prices are estimated with price-based durations, and examined from a market microstructure point of view.

Autoregressive conditional duration: a new model for irregularly spaced transaction data

This paper suggests that the interactions of security trades and quote revisions be modeled as a vector autoregressive system. Within this framework, a trade's information effect may be meaningfully measured as the ultimate price impact of the trade innovation. Estimates for a sample of NYSE issues suggest: a trade's full price impact arrives only with a protracted lag; the impact is a positive and concave function of the trade size; large trades cause the spread to widen; trades occurring in the face of wide spreads have larger price impacts; and, information asymmetries are more significant for smaller firms. CENTRAL TO THE ANALYSIS of market microstructure is the notion that in a market with asymmetrically informed agents, trades convey information and therefore cause a persistent impact on the security price. The magnitude of the price effect for a given trade size is generally held to be a positive function of the proportion of potentially informed traders in the population, the probability that such a trader is in fact informed (i.e., the probability that a private information signal has in fact been observed), and the precision of the private information. The close dependence of the price impact on these factors, which may be referred to as the extent of the information asymmetry, provides a strong motivation for the empirical determination of this impact. This paper strives to achieve such a determination in a framework that is robust to deviations from the assumptions of the formal models. In the process, the framework establishes a rich characterization of the dynamics by which trades and quotes interact. The market considered here is a specialist market in which a designated market-maker exposes bid and ask quotes to the trading public. An extensive theory has evolved that analyzes the market-maker's exposure to traders with superior information.' Concerning the extent of the information asymmetry, this body of theory yields two important empirical predictions: first,

/pdf/measuring-the-information-content-of-stock-trades-2bfwkwmama.pdf

Measuring the Information Content of Stock Trades

The behavior of time-weighted bid-ask spreads over the trading day are examined. The plot of minute-by-minute spreads versus time of day has a crude reverse J-shaped pattern. Schwartz identifies four determinants of spreads: activity, risk, information, and competition. Using a linear regression model, a significant relationship between these same factors and intraday spreads is demonstrated, but dummy variables for time of day have a reverse J-shape. For given values of the activity, risk, information and competition measures, spreads are higher at the beginning and end of the day relative to the interior period. THIS STUDY HAS A dual focus. First, it extends prior work by examining whether variables previously found to be determinants of spreads using data for intervals of a day or longer also explain spreads during the trading day. Second, the paper furthers previous research concerning intraday patterns in returns, variability, and volume by examining intraday patterns of spreads. Schwartz (1988, pp. 419-420) identifies four classes of variables as determinants of bid-ask spreads: activity, risk, information, and competition. Each of these determinants is considered briefly. Greater trading activity can lead to lower spreads due to economies of scale in trading costs. Using trading cost arguments, previous researchers show that a number of activity variables are significant determinants of bid-ask spreads including: (1) the average number of shares traded (Tinic, 1972), (2) the volume (Tinic and West (1972), Branch and Freed (1977), Stoll (1978)), and (3) the number of transactions (Benston and Hagerman, 1974). Copeland and Galai (1983) model the bid-ask spread as a free straddle option provided by the market maker and show that the bid-ask spread is inversely related to the frequency of trading. Copeland and Galai (1983) note that since less frequent trading usually means lower trading volume, the bid-ask spread is likely to be inversely related to measures of market activity. Inventory control models (Garman (1976), and Ho and Stoll (1980, 1981, 1983)) show that uncertainty in the arrival of buy and sell orders forces dealers away from their optimal inventory position. In the model of Amihud and Mendelson (1980), as the marker-maker approaches the desired inventory position the bid-ask spread is reduced. Hence, if greater

An Analysis of Intraday Patterns in Bid/Ask Spreads for NYSE Stocks

Using transactions data, the behavior of returns and characteristics of trades at the micro level is examined. A minute-by-minute market return series is formed and tested for normality and autocorrelation. Evidence of differences in return distributions is found among overnight trades, trades during the first 30 minutes following the market opening, trades at the close, and trades during the remainder of the day. The latter distribution is found to be normal. Unusually high returns and standard deviations of returns are found at the beginning and the end of the trading day. When the beginningand end-of-the-day effects are omitted, autocorrelation in the market return series is reduced substantially. A number of patterns in trading are reported. THIS PAPER EMPIRICALLY INVESTIGATES the nature of the return-generating process and the characteristics of trade size, price changes, trading frequency, and trading interval at the level of individual trades for a large sample of NYSE stocks. Previous studies of market indices have used daily, weekly, or monthly

An Investigation of Transactions Data for NYSE Stocks

Using synchronous transactions data for IBM from the New York, Pacific, and Midwest Stock Exchanges, we estimate an error correction model to investigate whether each of the exchanges is contributing to price discovery. Johansen's test yields two cointegrating vectors, which together verify the expected long-run equilibrium of equal prices across the three exchanges. Two error correction terms specified as the differences from IBM prices on the NYSE indicate that adjustments maintaining the long-run cointegration equilibrium take place on all three exchanges. That is, IBM prices on the NYSE adjust toward IBM prices on the Midwest and Pacific Exchanges, just as Midwest and Pacific prices adjust to the NYSE.

Cointegration, Error Correction, and Price Discovery on Informationally Linked Security Markets

Security price adjustment across exchanges: an investigation of common factor components for Dow stocks☆

A question invariably recurs in discussions about corporate reputation: are they cause,
consequence, or epiphenomenon? That is: do they have an independent causal effect on
corporate performance; are they a consequence of good financial performance? Or are they
an incidental by-product? The second day of the conference began with a review of avail-
able evidence of the possible financial impact of corporate reputations. The following
panelists discussed their prepared papers: The Value of Corporate Reputation: Evidence from the Equity Markets - 
Rajendra K. Srivastava, University of Texas at Austin;
Thomas H. McInish, Memphis State University;
Robert A. Wood, Memphis State University;
Anthony J. Capraro, University of Texas at Austin. The Effect of Financial and Media Reputations on Performance - 
David L. Deephouse, Louisiana State University. The Value of a Firm's Corporate Reputation: How Reputation Helps Attain and Sustain
Superior Profitability - 
Peter W. Roberts, University of New South Wales;
Grahame R. Dowling, University of New South Wales. Stock Market Valuation of Reputation for Corporate Social Performance - 
Brad Brown, University of Virginia. Sustainable Competitive Advantage and Firm Performance: The Role of Intangible Resources - 
G. Steven McMillan, The American College;
Maheshkumar P. Joshi, St. Joseph's University. Has the Influence of Financial Performance on Reputation Measures Been Overstated? - 
A. J. Capraro, University of Texas at Austin;
Rajendra K. Srivastava, University of Texas at Austin.

https://link.springer.com/content/pdf/10.1057%2Fpalgrave.crr.1540018.pdf

Thomas H. McInish

Papers

An Analysis of Intraday Patterns in Bid/Ask Spreads for NYSE Stocks

An Investigation of Transactions Data for NYSE Stocks

Cointegration, Error Correction, and Price Discovery on Informationally Linked Security Markets

Security price adjustment across exchanges: an investigation of common factor components for Dow stocks☆

Part IV: How Do Reputations Affect Corporate Performance?: The Value of Corporate Reputation: Evidence from the Equity Markets