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: The authors studied the relation between market returns and aggregate flow into U.S. equity funds, using daily flow data and showed that this concurrent relation reflects flow and institutional trading affecting returns.
443 citations
TL;DR: In this article, the authors explore how well these models actually perform by applying twelve value-at-risk approaches to 1,000 randomly chosen foreign exchange portfolios and find that the approaches generally capture the risk that they set out to assess and tend to produce risk estimates that are similar in average size.
Abstract: Recent studies have underscored the need for market participants to develop reliable methods of measuring risk. One increasingly popular technique is the use of "value-at-risk" models, which convey estimates of market risk for an entire portfolio in one number. The author explores how well these models actually perform by applying twelve value-at-risk approaches to 1,000 randomly chosen foreign exchange portfolios. Using nine criteria to evaluate model performance, he finds that the approaches generally capture the risk that they set out to assess and tend to produce risk estimates that are similar in average size. No approach, however, appears to be superior by every measure.
440 citations
TL;DR: In this paper, the authors present a theory of excess stock market volatility, in which market movements are due to trades by very large institutional investors in relatively illiquid markets, and derive the optimal trading behavior of thse investors, which allows them to provide a unified explanation for apparently disconnected empirical regularities in returns, trading volume and investor size.
Abstract: We present a theory of excess stock market volatility, in which market movements are due to trades by very large institutional investors in relatively illiquid markets. Such trades generate significant spikes in returns and volume, even in the absence of important news about fundamentals. We derive the optimal trading behavior of thse investors, which allows us to provide a unified explanation for apparently disconnected empirical regularities in returns, trading volume and investor size.
417 citations
TL;DR: This article studied the impact of algorithmic trading in the foreign exchange market using a long time series of high-frequency data that identify computer-generated trading activity and found that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity.
Abstract: We study the impact of algorithmic trading (AT) in the foreign exchange market using a long time series of high-frequency data that identify computer-generated trading activity. We find that AT causes an improvement in two measures of price efficiency: the frequency of triangular arbitrage opportunities and the autocorrelation of high-frequency returns. We show that the reduction in arbitrage opportunities is associated primarily with computers taking liquidity. This result is consistent with the view that AT improves informational efficiency by speeding up price discovery, but that it may also impose higher adverse selection costs on slower traders. In contrast, the reduction in the autocorrelation of returns owes more to the algorithmic provision of liquidity. We also find evidence consistent with the strategies of algorithmic traders being highly correlated. This correlation, however, does not appear to cause a degradation in market quality, at least not on average.
407 citations
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TL;DR: In this article, the empirical relation between corporate governance and stock market liquidity was investigated and it was shown that firms with better corporate governance have narrower spreads, higher market quality index, smaller price impact of trades, and lower probability of information-based trading.
Abstract: We investigate the empirical relation between corporate governance and stock market liquidity. We find that firms with better corporate governance have narrower spreads, higher market quality index, smaller price impact of trades, and lower probability of information-based trading. In addition, we show that changes in our liquidity measures are significantly related to changes in the governance index over time. These results suggest that firms may alleviate information-based trading and improve stock market liquidity by adopting corporate governance standards that mitigate informational asymmetries. Our results are remarkably robust to alternative model specifications, across exchanges, and different measures of liquidity.
404 citations