Algorithmic trading

About: Algorithmic trading is a research topic. Over the lifetime, 6718 publications have been published within this topic receiving 162209 citations. The topic is also known as: algotrading & Algorithmic trading.

Institutional Trading and Stock Returns

Abstract: In this study, we explore the dynamics of the relation between institutional trading and stock returns. We find that stock returns Granger-cause institutional trading (especially purchases) on a quarterly basis. The robust and significant causality from equity returns to institutional trading can be largely explained by the time-series variation of market returns, that is, institutions buy more popular stocks after market rises. Stock returns appear to be negatively related to lagged institutional trading. An analysis of the behavior of trading and the returns of the traded stocks reveals evidence that stocks with heavy institutional buying (selling) experience positive (negative) excess returns over the previous 12 months.

Sequential Markets, Market Power, and Arbitrage

Abstract: We develop a framework to characterize strategic behavior in sequential markets under imperfect competition and restricted entry in arbitrage. Our theory predicts that these two elements can generate a systematic price premium. We test the model predictions using microdata from the Iberian electricity market. We show that the observed price differences and firm behavior are consistent with the model. Finally, we quantify the welfare effects of arbitrage using a structural model. In the presence of market power, we show that full arbitrage is not necessarily welfare-enhancing, reducing consumer costs but increasing deadweight loss. (JEL D42, D43, L12, L13, L94, Q41)

Market structure and trading volume

Abstract: Historically, trading volume reported for NASDAQ stocks has been overstated vis-a-vis New York Stock Exchange (NYSE) stocks, both because of the dealer's participation in trades as a market maker and because of interdealer trading. Beginning in 1997, the Securities and Exchange Commission changed order-handling rules and trade-reporting rules, which may have reduced or eliminated the overstatement of NASDAQ trading. We examine trading volumes of firms changing from NASDAQ to the NYSE since 1997 and document that reported trading volume for NASDAQ stocks continues to be overstated. Moreover, the degree of overstatement is much larger for firms with high trading volume.

Technical trading strategies and return predictability: NYSE

Abstract: This study consists of an empirical analysis on technical trading rules (the simple price moving average, the momentum, and trading volume) utilizing the NYSE value-weighted index over the period 1962–1996, as well as, three subperiods. The methodologies employed include the traditional t-test and residual bootstrap methodology utilizing random walk, GARCH-M and GARCH-M with some instrument variables. The results indicate that the technical trading rules add a value to capture profit opportunities over a buy-hold strategy. When the trading rules are applied to the different sub-samples, the results are weaker in the last sub-period, 1985–1996. This may imply that the market is getting efficient in information over the recent years because of technological improvements.

High Frequency Traders and Asset Prices

Abstract: Do high frequency traders affect transaction prices? In this paper we derive distributions of transaction prices in limit order markets populated by low frequency traders (humans) before and after the entrance of a high frequency trader (machine). We find that the presence of a machine is likely to change the average transaction price, even in the absence of new information. We also find that in a market with a high frequency trader, the distribution of transaction prices has more mass around the center and thinner far tails. With a machine, mean intertrade duration decreases in proportion to the increase in the ratio of the human order arrival rates with and without the presence of the machine; trading volume goes up by the same rate. We show that the machine makes positive expected profits by "sniping" out human orders somewhat away from the front of the book. This explains the shape of the transaction price density. In fact, we show that in a special case, the faster humans submit and vary their orders, the more profits the machine makes.