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.

Migration, Spillovers, and Trade Diversion: The Impact of Internationalization on Domestic Stock Market Activity

Abstract: What is the impact of firms that cross-list, issue depositary receipts, or raise capital in international stock markets on the trading activity and liquidity of remaining firms in domestic markets? Using a panel of 3,000 firms from 55 countries during 1989-2000, we find that internationalization reduces the trading activity and liquidity of domestic firms through two channels. First, the trading of international firms migrates from domestic to international markets and this migration along with the reduction in domestic trading of international firms has negative spillover effects on domestic firm trading activity and liquidity. Second, there is trade diversion within domestic markets as trading activity shifts out of domestic firms and into international firms.

Anomalies Across the Globe: Once Public, No Longer Existent?

Abstract: Motivated by McLean and Pontiff (2016), we study the pre- and post-publication return predictability of 138 anomalies in 39 stock markets. Based on more than a million anomaly country-months, we find that the United States is the only country with a statistically significant and economically meaningful post-publication decline in long/short returns. The surprisingly large differences between the U.S. and international markets cannot be fully explained with general time effects or differences in limits to arbitrage, in-sample anomaly profitability, data availability, or local risk factor exposure. Our results have implications for the recent literature on arbitrage trading, data mining, and market segmentation.

Fallout from the Mutual Fund Trading Scandal

Abstract: In September 2003, several prominent mutual fund companies came under investigation for illegal trading practices Allegations suggested these funds allowed certain investors to profit from short-term trading schemes at the expense of other investors Surprisingly, regulatory authorities have known for more than two decades of the potential for such abuses, yet have taken limited steps to correct the problem We explore investor reaction to the scandal by measuring assets under management, stock returns, and performance Mutual funds managed by investigated firms show a substantial decline in post-announcement assets under management These firms also experienced significantly negative announcement-period returns Finally, we discuss several policy suggestions to prevent future trading abuses and provide direction for future research

A Simple Agent-based Financial Market Model: Direct Interactions and Comparisons of Trading Profits

Abstract: In the recent past, a number of interesting agent-based financial market models have been proposed. These models successfully explain some important stylized facts of financial markets, such as bubbles and crashes, fat tails for the distribution of returns and volatility clustering. These models, reviewed, for instance, in Chen, Chang, and Du (in press); Hommes (2006); LeBaron (2006); Lux (in press); Westerhoff (2009), are based on the observation that financial market participants use different heuristic trading rules to determine their speculative investment positions. Note that survey studies by Frankel and Froot (1986);Menkhoff (1997);Menkhoff and Taylor (2007); Taylor and Allen (1992) in fact reveal that market participants use technical and fundamental analysis to assess financial markets. Agent-based financial market models obviously have a strong empirical microfoundation.