This is Part 4 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. Table of Links Abstract and Introduction Human-machine interactions Collective outcomes Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Discussion Implications for research Implications for design Implications for policy Conclusion, Acknowledgments, References, and Competing interests Box 1: Competition in High-frequency Trading Markets High-frequency trading (HFT) algorithms constitute automated scripts that rely on high-speed, large-volume transactions to exploit mispricings or market signals before they disappear or are incorporated into the price [166]. The phenomenon started in the mid-90s and has since spread to dominate foreign equities, foreign exchange, commodities, futures, and stock markets globally [117]. HFT algorithms process large amounts of trade history data and current news to make decisions and are thus considered the better “informed” traders [167]. Some of the algorithms appear to anticipate the market, and their trades consistently predict future order flow by human traders [168]. However, since most algorithms react similarly to the same public information, they exhibit less diverse trading strategies and more correlated actions among themselves compared to humans [169]. Thus, although their behavior generally improves market efficiency, it can also trigger behavioral cascades and instability [170]. HFT algorithms generally act as market makers, increasing trading opportunities, reducing transaction costs, connecting buyers and sellers across venues, and submitting significant volumes of price quotes [171,117]. They facilitate price efficiency by trading in the direction of permanent price changes but opposite temporary price errors [167]. This regularly acts as a stabilizing force, reducing short-term volatility [169,172,166]. On a longer time scale, however, HFT algorithms may decease market quality by increasing volatility [173] and uncertainty [37], and by reducing trading strategy diversity [174]. For instance, although the algorithms did not cause the 2010 flash crash, they exacerbated it by amplifying the volatility [175]. This has led to recent efforts to regulate the speed of trading in markets, for example, by processing trades in batches at slower intervals to diminish the advantage that HFT algorithms have [176]. Authors:
(1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom;
(2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland;
(3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA;
(4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This is Part 4 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. This is Part 4 of a 12-part series based on the research paper “Human-Machine Social Systems.” Use the table of links below to navigate to the next part. “ Human-Machine Social Systems .” Human-Machine Social Systems Table of Links Abstract and Introduction Abstract and Introduction Human-machine interactions Human-machine interactions Collective outcomes Collective outcomes Box 1: Competition in high-frequency trading markets Box 1: Competition in high-frequency trading markets Box 2: Contagion on Twitter Box 2: Contagion on Twitter Box 3: Cooperation and coordination on Wikipedia Box 3: Cooperation and coordination on Wikipedia Box 4: Cooperation and contagion on Reddit Box 4: Cooperation and contagion on Reddit Discussion Discussion Implications for research Implications for research Implications for design Implications for design Implications for policy Implications for policy Conclusion, Acknowledgments, References, and Competing interests Conclusion, Acknowledgments, References, and Competing interests Box 1: Competition in High-frequency Trading Markets High-frequency trading (HFT) algorithms constitute automated scripts that rely on high-speed, large-volume transactions to exploit mispricings or market signals before they disappear or are incorporated into the price [166]. The phenomenon started in the mid-90s and has since spread to dominate foreign equities, foreign exchange, commodities, futures, and stock markets globally [117]. HFT algorithms process large amounts of trade history data and current news to make decisions and are thus considered the better “informed” traders [167]. Some of the algorithms appear to anticipate the market, and their trades consistently predict future order flow by human traders [168]. However, since most algorithms react similarly to the same public information, they exhibit less diverse trading strategies and more correlated actions among themselves compared to humans [169]. Thus, although their behavior generally improves market efficiency, it can also trigger behavioral cascades and instability [170]. HFT algorithms generally act as market makers, increasing trading opportunities, reducing transaction costs, connecting buyers and sellers across venues, and submitting significant volumes of price quotes [171,117]. They facilitate price efficiency by trading in the direction of permanent price changes but opposite temporary price errors [167]. This regularly acts as a stabilizing force, reducing short-term volatility [169,172,166]. On a longer time scale, however, HFT algorithms may decease market quality by increasing volatility [173] and uncertainty [37], and by reducing trading strategy diversity [174]. For instance, although the algorithms did not cause the 2010 flash crash, they exacerbated it by amplifying the volatility [175]. This has led to recent efforts to regulate the speed of trading in markets, for example, by processing trades in batches at slower intervals to diminish the advantage that HFT algorithms have [176]. Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. Authors: Authors: (1) Milena Tsvetkova, Department of Methodology, London School of Economics and Political Science, London, United Kingdom; (2) Taha Yasseri, School of Sociology, University College Dublin, Dublin, Ireland and Geary Institute for Public Policy, University College Dublin, Dublin, Ireland; (3) Niccolo Pescetelli, Collective Intelligence Lab, New Jersey Institute of Technology, Newark, New Jersey, USA; (4) Tobias Werner, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

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High-Frequency Trading Algorithms Improve Efficiency but Risk Market Stability

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