Machine learning and social action in markets: From first- to second-generation automated trading

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

Machine learning and social action in markets : From first- to second-generation automated trading. / Borch, Christian; Min, Bo Hee.

In: Economy and Society, 2022, p. 1-25.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Borch, C & Min, BH 2022, 'Machine learning and social action in markets: From first- to second-generation automated trading', Economy and Society, pp. 1-25. https://doi.org/10.1080/03085147.2022.2050088

APA

Borch, C., & Min, B. H. (2022). Machine learning and social action in markets: From first- to second-generation automated trading. Economy and Society, 1-25. https://doi.org/10.1080/03085147.2022.2050088

Vancouver

Borch C, Min BH. Machine learning and social action in markets: From first- to second-generation automated trading. Economy and Society. 2022;1-25. https://doi.org/10.1080/03085147.2022.2050088

Author

Borch, Christian ; Min, Bo Hee. / Machine learning and social action in markets : From first- to second-generation automated trading. In: Economy and Society. 2022 ; pp. 1-25.

Bibtex

@article{010691c162a24217b4009b6f85e64691,
title = "Machine learning and social action in markets: From first- to second-generation automated trading",
abstract = "Machine learning (ML) models are gaining traction in securities trading because of their ability to recognize and predict patterns. This study examines how ML is transforming automated trading. Drawing on 213 interviews with market participants (including 94 with people working at ML-employing firms) as well as ethnographic observations of a trading firm specializing in ML-based automated trading, we argue that ML-based ({\textquoteleft}second-generation{\textquoteright}) automated trading systems are different to previous ({\textquoteleft}first-generation{\textquoteright}) automated trading systems. Where first-generation systems are based on human-defined rules, second-generation systems develop their trading rules independently. We further argue that the use of such second-generation systems prompts a rethinking of established concepts in economic sociology. In particular, a Weberian notion of social action in markets is incompatible with such systems, but we also argue that second-generation automated trading calls for a reconsideration of the notion of the performativity of financial models.",
keywords = "Faculty of Social Sciences, automated trading, economic sociology, machine learning, performativity, social action",
author = "Christian Borch and Min, {Bo Hee}",
year = "2022",
doi = "10.1080/03085147.2022.2050088",
language = "English",
pages = "1--25",
journal = "Economy and Society",
issn = "0308-5147",
publisher = "Routledge",

}

RIS

TY - JOUR

T1 - Machine learning and social action in markets

T2 - From first- to second-generation automated trading

AU - Borch, Christian

AU - Min, Bo Hee

PY - 2022

Y1 - 2022

N2 - Machine learning (ML) models are gaining traction in securities trading because of their ability to recognize and predict patterns. This study examines how ML is transforming automated trading. Drawing on 213 interviews with market participants (including 94 with people working at ML-employing firms) as well as ethnographic observations of a trading firm specializing in ML-based automated trading, we argue that ML-based (‘second-generation’) automated trading systems are different to previous (‘first-generation’) automated trading systems. Where first-generation systems are based on human-defined rules, second-generation systems develop their trading rules independently. We further argue that the use of such second-generation systems prompts a rethinking of established concepts in economic sociology. In particular, a Weberian notion of social action in markets is incompatible with such systems, but we also argue that second-generation automated trading calls for a reconsideration of the notion of the performativity of financial models.

AB - Machine learning (ML) models are gaining traction in securities trading because of their ability to recognize and predict patterns. This study examines how ML is transforming automated trading. Drawing on 213 interviews with market participants (including 94 with people working at ML-employing firms) as well as ethnographic observations of a trading firm specializing in ML-based automated trading, we argue that ML-based (‘second-generation’) automated trading systems are different to previous (‘first-generation’) automated trading systems. Where first-generation systems are based on human-defined rules, second-generation systems develop their trading rules independently. We further argue that the use of such second-generation systems prompts a rethinking of established concepts in economic sociology. In particular, a Weberian notion of social action in markets is incompatible with such systems, but we also argue that second-generation automated trading calls for a reconsideration of the notion of the performativity of financial models.

KW - Faculty of Social Sciences

KW - automated trading

KW - economic sociology

KW - machine learning

KW - performativity

KW - social action

U2 - 10.1080/03085147.2022.2050088

DO - 10.1080/03085147.2022.2050088

M3 - Journal article

SP - 1

EP - 25

JO - Economy and Society

JF - Economy and Society

SN - 0308-5147

ER -

ID: 319888248