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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.


Papers
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Journal ArticleDOI
TL;DR: In this paper, the authors investigated how technical trading systems exploit the momentum and reversal effects in the S&P 500 spot and futures market and analyzed the performance of 2580 widely used models.
Abstract: This paper investigates how technical trading systems exploit the momentum and reversal effects in the S&P 500 spot and futures market. The former is exploited by trend-following models, while the latter by contrarian models. In total, the performance of 2580 widely used models is analyzed. When based on daily data, the profitability of technical stock trading has steadily declined since 1960 and has become unprofitable over the 1990s. However, when based on 30-minutes-data the same models produce an average gross return of 8.8% per year between 1983 and 2000. These results do not change substantially when trading is simulated over six subperiods. Those 25 models which performed best over the most recent subperiod produce a significantly higher gross return over the subsequent subperiod than all models. Over the out-of-sample-period 2001-2006 the 2580 models perform much worse than between 1983 and 2000. This result could be due to stock markets becoming more efficient or to stock price trends shifting from 30-minutes-prices to prices of higher frequencies.

116 citations

BookDOI
TL;DR: In this article, the authors discuss the link between Fragmentation and systemic risk, and the Epps Effect, as well as optimal trade scheduling and order routing for high frequency traders.
Abstract: Market Fragmentation: Monitoring and History Smart Order Routing Tick Size Information Seeking and Price Discovery Dark Pools and Broker Crossing Networks Liquidity: The Viewpoint of Trading Venues The Agenda of High Frequency Traders The Link between Fragmentation and Systemic Risk The Flash Crash The Signature Plot The Epps Effect Optimal organization for Optimal Trading Market Impact at Different Time Scales Optimal Trading Quantitative Approaches: Optimal Trade Scheduling and Optimal Order Routing.

115 citations

Journal ArticleDOI
TL;DR: The authors examined the relationship between online search intensity and stock-trading behavior in the Japanese market and found correlations with search intensity that are strongly positive for trading volume and weakly positive for stock returns.
Abstract: This paper examines the relationship between online search intensity and stock-trading behavior in the Japanese market. The search intensity is measured by the search volume of company names on Google, which is expected to be related to the aggregate stock purchasing behavior of individual investors. Our sample consists of 189 stocks included in the Nikkei 225 and searched between 2008 and 2011. We find correlations with search intensity that are strongly positive for trading volume and weakly positive for stock returns. Our results are consistent with the notion that the increase of search activity is associated with increases of trading activity, but the probability that this increase of trading raises stock prices is not high, probably because of the fact that our sample period includes major negative economic shocks such as the 2008 world financial crisis and the 2011 Great East Japan Earthquake; also, the presence of individual investors, whose online search activity is expected to be well-associated with stock trading, is smaller in Japan than in the U.S.

115 citations

Posted Content
TL;DR: In this paper, a theoretical interpretation of the "flash-crash" of may 2010 is presented, and the equilibrium level of investment in algorithmic trading is analyzed, showing that for a given level of trading, multiple equilibria can arise, some of which generate market exclusion for slow traders and sharp increases in the price impact of trades.
Abstract: Algorithms enable investors to locate trading opportunities, which raises gains from trade. Algorithmic traders can also process information on stock values before slow traders, which generates adverse selection. We model trading in this context and show that, for a given level of algorithmic trading, multiple equilibria can arise, some of which generate market exclusion for slow traders and sharp increases in the price impact of trades. We offer a theoretical interpretation for the "flash-crash" of may 2010. Next, we analyze the equilibrium level of investment in algorithmic trading. Because when others become fast it increases adverse selection costs for slow investors, algo-trading generates negative externalities. Therefore the equilibrium level of algo-trading exceeds its utilitarian welfare maximizing counterpart. Furthermore, since it involves fixed costs, investment in algorithmic trading is more pro table for large institutions than for small ones. This generates equilibrium informational asymmetries between large fast traders and small slow traders.

115 citations

Posted Content
TL;DR: The authors examined the lead-lag relationship between futures trading activity (volume and open interest) and cash price volatility for major agricultural commodities and found that an unexpected increase in futures trading volume unidirectionally causes an increase in Cash Price volatility for most commodities.
Abstract: This paper examines the lead-lag relationship between futures trading activity (volume and open interest) and cash price volatility for major agricultural commodities. Granger causality tests and generalized forecast error variance decompositions show that an unexpected increase in futures trading volume unidirectionally causes an increase in cash price volatility for most commodities. Likewise, there is a weak causal feedback between open interest and cash price volatility. These findings are generally consistent with the destabilizing effect of futures trading on agricultural commodity markets.

115 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202397
2022190
2021144
2020167
2019126
2018160