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 (‘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.

摘要

机器学习 (ML) 模型因其识别和预测模式的能力而在证券交易中越来越受欢迎。本研究探讨机器学习如何改变自动化交易。根据对市场参与者的 213 次采访（包括 94 次与在使用 ML 的公司工作的人员进行的采访）以及对一家专门从事基于 ML 的自动交易的贸易公司的人种学观察，我们认为基于 ML 的（“第二代”）自动化交易系统不同于以前的（“第一代”）自动交易系统。第一代系统基于人为定义的规则，第二代系统独立开发其交易规则。我们进一步论证，此类第二代系统的使用促使人们重新思考经济社会学中的既定概念。尤其，