Machines and Markets: Assessing the Impact of Algorithmic Trading on Financial Market Efficiency

Karan Garg
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Karan Garg: University of Warwick

Warwick-Monash Economics Student Papers from Warwick Monash Economics Student Papers

Abstract: The rise of machine learning has revolutionised finance. Institutions across the world have increasingly turned to data science and machine learning to create trading models without the need for human intervention. This has had various implications for the financial markets that they operate in, including market efficiency. This paper simulates a financial market with agent-based modelling and Monte-Carlo style simulations, to motivate a qualitative discussion about the implications of increased algorithmic trading on financial market efficiency. It finds that algorithmic traders (ATs) can seemingly increase market efficiency through better liquidity management and more complete extraction of information from prices. However, this also comes with increased instability and potential convergence to an unstable equilibrium. The Adaptive Market Hypothesis (Lo, 2004) is suggested as an alternative framework for analysing AT behaviour.

Keywords: Neural Networks; Agent-Based Modelling; Efficient Market Hypothesis; Stock Market Simulation; Financial Regulation JEL Classification: C45; C53; G14; G17; G18 (search for similar items in EconPapers)
Date: 2021
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fdg, nep-hme, nep-mst and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:wrk:wrkesp:11

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