Topic
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.
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TL;DR: In this article, the authors investigated whether the moving average and trading range breakout rules can forecast stock price movements and outperform a simple buy-and-hold strategy after adjusting for transaction costs over the period from January 1991 to December 2008.
85 citations
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TL;DR: In this paper, a multi-period rational expectations model of stock trading is developed in which investors have differential information concerning the underlying value of the stock. But the model assumes that the information revealed by the market-clearing prices, as well as other public news, is known.
Abstract: This paper develops a multi-period rational expectations model of stock trading in which investors have differential information concerning the underlying value of the stock. Investors trade competitively in the stock market based on their private information and the information revealed by the market-clearing prices, as well as other public news. We examine how trading volume is related to the information flow in the market and how investors' trading reveals their private information.
84 citations
84 citations
TL;DR: In this paper, the authors highlight fundamental yet unanswered questions on the nature of private information, the impact on market liquidity, and the changing process of price discovery, and outline potential microstructure explanations for long-standing exchange rate puzzles.
84 citations
TL;DR: In this paper, a delay of 300 ms or more significantly reduces returns of news-based trading strategies, and the effect of algorithmic trading on market quality around macroeconomic news is assessed.
Abstract: This paper documents that speed is crucially important for high-frequency trading strategies based on U.S. macroeconomic news releases. Using order-level data on the highly liquid S&P 500 ETF traded on NASDAQ from January 6, 2009 to December 12, 2011, we find that a delay of 300 ms or more significantly reduces returns of news-based trading strategies. This reduction is greater for high impact news and on days with high volatility. In addition, we assess the effect of algorithmic trading on market quality around macroeconomic news. In the minute following a macroeconomic news arrival, algorithmic activity increases trading volume and depth at the best quotes, but also increases volatility and leads to a drop in overall depth. Quoted half-spreads decrease (increase) when we measure algorithmic trading over the full (top of the) order book.
84 citations