Algorithmic trading

About: Algorithmic trading is a(n) research topic. Over the lifetime, 6718 publication(s) have been published within this topic receiving 162209 citation(s). The topic is also known as: algotrading & Algorithmic trading.

A Theory of Intraday Patterns: Volume and Price Variability

Abstract: 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

The impact of institutional trading on stock prices

Abstract: This paper uses new data on the holdings of 769 tax-exempt (predominantly pension) funds, to evaluate the potential effect of their trading on stock prices. We address two aspects of trading by these money managers: herding, which refers to buying (selling) simultaneously the same stocks as other managers buy (sell), and positive-feedback trading, which refers to buying past winners and selling past losers. These two aspects of trading are commonly a part of the argument that institutions destabilize stock prices. The evidence suggests that pension managers do not strongly pursue these potentially destabilizing practices.

The price variability-volume relationship on speculative markets

Abstract: This paper concerns the relationship between the variability of the daily price change and the daily volume of trading on the speculative markets. Our work extends the theory of speculative markets in two ways. First, we derive from economic theory the joint probability distribution of the price change and the trading volume over any interval of time within the trading day. And second, we determine how this joint distribution changes as more traders enter (or exit from) the market. The model's parameters are estimated by FIML using daily data from the 90-day T-bills futures market. The results of the estimation can reconcile a conflict between the price variability-volume relationship for this market and the relationship obtained by previous investigators for other speculative markets. THIS PAPER CONCERNS the relationship between the variability of the daily price change and the volume of trading on speculative markets. Previous empirical studies [2, 3, 6, 12, 14, 16] of both futures and equity markets always find a positive association between price variability (as measured by the squared price change Ap2) and the trading volume.2 There are two explanations for the relationship. Clark's [2] explanation, which is secondary to his effort to explain why the probability distribution of the daily price change is leptokurtic, emphasizes randomness in the number of within-day transactions. In Clark's model the daily price change is the sum of a random number of within-day price changes. The variance of the daily price change is thus a random variable with a mean proportional to the mean number of daily transactions. Clark argues that the trading volume is related positively to the number of within-day transactions, and so the trading volume is related positively to the variability of the price change. The second explanation is due to Epps and Epps [6]. Their model examines the mechanics of within-day trading. The change in the market price on each within-day transaction or market clearing is the average of the changes in all of the traders' reservation prices. Epps and Epps assume there is a positive relationship between the extent to which traders disagree when they revise their reservation prices and the absolute value of the change in the market price. That is, an increase in the extent to which traders disagree is associated with a larger absolute price change. The price variability-volume relationship arises, then, because the volume of trading is positively related to the extent to which traders disagree when they revise their reservation prices.

Liquidity, Information, and Infrequently Traded Stocks

Abstract: This article investigates whether differences in information-based trading can explain observed differences in spreads for active and infrequently traded stocks. Using a new empirical technique, we estimate the risk of information-based trading for a sample of New York Stock Exchange (NYSE) listed stocks. We use the information in trade data to determine how frequently new information occurs, the composition of trading when it does, and the depth of the market for different volume-decile stocks. Our most important empirical result is that the probability of information-based trading is lower for high volume stocks. Using regressions, we provide evidence of the economic importance of information-based trading on spreads.

Special Information and Insider Trading

Abstract: Trading by corporate officers, directors, and large stockholders, who are commonly called insiders, commands widespread attention in the financial community. Academicians are interested in the amount of special information insiders possess, as well as in the profit they earn from such knowledge. The average investor seeks out useful information in the Official Summary of Insider Trading,' the monthly report listing the transactions of corporate officials. Previous research on corporate insiders has focused on the profitability of their trading. Some researchers, examining months of intensive insider activity, have concluded that insiders can predict stock price movement up to 6 months subsequent to trading. Rogoff for example, examines 45 companies in which, within a single month, three or more insiders buy their company's stock and no insiders sell the stock.2 He finds that the returns to the insiders of these companies in the following 6 months are on average 91/2 percent greater than the return to the stock market as a whole. Glass examines 14 different calendar months and selects the eight securities with the greatest excess of buyers to sellers among insiders within a month.3 He finds that the average return on these securities is 10 percent above the return on the stock market as a whole in the 7 months following the individual months of intensive buying. Lorie and Niederhoffer investigate stock performance following months in which there are at least two more buyers than sellers or at least two more sellers than buyers among the insiders of a company.4 They find that a security experiencing an intensive buying month is more likely to advance than to decline relative to the market in the 6 months subsequent to the event. Conversely, a security experiencing an intensive selling month is more likely to decline than to advance relative to the market in the 6 months subsequent to the event. Driscoll examines the trading by insiders prior to dividend changes