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How Complexity and Uncertainty Grew with Algorithmic Trading

Communication, Computational Social Science, University of California, Davis, CA 95616, USA
Department of Mathematics, Monmouth University, West Long Branch, NJ 07764, USA
Author to whom correspondence should be addressed.
Entropy 2020, 22(5), 499;
Received: 1 February 2020 / Revised: 3 April 2020 / Accepted: 17 April 2020 / Published: 26 April 2020
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
The machine-learning paradigm promises traders to reduce uncertainty through better predictions done by ever more complex algorithms. We ask about detectable results of both uncertainty and complexity at the aggregated market level. We analyzed almost one billion trades of eight currency pairs (2007–2017) and show that increased algorithmic trading is associated with more complex subsequences and more predictable structures in bid-ask spreads. However, algorithmic involvement is also associated with more future uncertainty, which seems contradictory, at first sight. On the micro-level, traders employ algorithms to reduce their local uncertainty by creating more complex algorithmic patterns. This entails more predictable structure and more complexity. On the macro-level, the increased overall complexity implies more combinatorial possibilities, and therefore, more uncertainty about the future. The chain rule of entropy reveals that uncertainty has been reduced when trading on the level of the fourth digit behind the dollar, while new uncertainty started to arise at the fifth digit behind the dollar (aka ‘pip-trading’). In short, our information theoretic analysis helps us to clarify that the seeming contradiction between decreased uncertainty on the micro-level and increased uncertainty on the macro-level is the result of the inherent relationship between complexity and uncertainty. View Full-Text
Keywords: algorithmic trading; complexity; predictability; machine learning; dynamical systems theory algorithmic trading; complexity; predictability; machine learning; dynamical systems theory
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MDPI and ACS Style

Hilbert, M.; Darmon, D. How Complexity and Uncertainty Grew with Algorithmic Trading. Entropy 2020, 22, 499.

AMA Style

Hilbert M, Darmon D. How Complexity and Uncertainty Grew with Algorithmic Trading. Entropy. 2020; 22(5):499.

Chicago/Turabian Style

Hilbert, Martin, and David Darmon. 2020. "How Complexity and Uncertainty Grew with Algorithmic Trading" Entropy 22, no. 5: 499.

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