New papers on machine learning and financial markets
Kristian and Christian have published three new AlgoFinance papers (all available open access) which discuss the uptake of machine learning in the financial markets in various ways:
Kristian Bondo Hansen and Christian Borch: “Alternative data and sentiment analysis: Prospecting non-standard data in machine-learning-driven finance”, Big Data & Society, 2022
Abstract: Social media commentary, satellite imagery and GPS data are a part of ‘alternative data’, that is, data that originate outside of the standard repertoire of market data but are considered useful for predicting stock prices, detecting different risk exposures and discovering new price movement indicators. With the availability of sophisticated machine-learning analytics tools, alternative data are gaining traction within the investment management and algorithmic trading industries. Drawing on interviews with people working in investment management and algorithmic trading firms utilizing alternative data, as well as firms providing and sourcing such data, we emphasize social media-based sentiment analytics as one manifestation of how alternative data are deployed for stock price prediction purposes. This demonstrates both how sentiment analytics are developed and subsequently utilized by investment management firms. We argue that ‘alternative data’ are an open-ended placeholder for every data source potentially relevant for investment management purposes and harnessing these disparate data sources requires certain standardization efforts by different market participants. Besides showing how market participants understand and use alternative data, we demonstrate that alternative data often undergo processes of (a) prospecting (i.e. rendering such data amenable to processing with the aid of analytics tools) and (b) assetization (i.e. the transformation of data into tradable assets). We further contend that the widespread embracement of alternative data in investment management and trading encourages a financialization process at the data level which raises new governance issues.
Christian Borch: “Machine learning, knowledge risk, and principal-agent problems in automated trading”, Technology in Society, 2022
Abstract: Present-day securities trading is dominated by fully automated algorithms. These algorithmic systems are characterized by particular forms of knowledge risk (adverse effects relating to the use or absence of certain forms of knowledge) and principal-agent problems (goal conflicts and information asymmetries arising from the delegation of decision-making authority). Where automated trading systems used to be based on human-defined rules, increasingly, machine-learning (ML) techniques are being adopted to produce machine-generated strategies. Drawing on 213 interviews with market participants involved in automated trading, this study compares the forms of knowledge risk and principal-agent relations characterizing both human-defined and ML-based automated trading systems. It demonstrates that certain forms of ML-based automated trading lead to a change in knowledge risks, particularly concerning dramatically changing market settings, and that they are characterized by a lack of insight into how and why trading rules are being produced by the ML systems. This not only intensifies but also reconfigures principal-agent problems in financial markets.
Christian Borch: “Machine learning and social theory: Collective machine behaviour in algorithmic trading”, European Journal of Social Theory, 2021
Abstract: This article examines what the rise in machine learning (ML) systems might mean for social theory. Focusing on financial markets, in which algorithmic securities trading founded on ML-based decision-making is gaining traction, I discuss the extent to which established sociological notions remain relevant or demand a reconsideration when applied to an ML context. I argue that ML systems have some capacity for agency and for engaging in forms of collective machine behaviour, in which ML systems interact with other machines. However, ML-based collective machine behaviour is irreducible to human decision-making and thereby challenges established sociological notions of financial markets (including that of embeddedness). I argue that such behaviour can nonetheless be analysed through an adaptation of sociological theories of interaction and collective behaviour.