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How Complexity and Uncertainty Grew with Algorithmic Trading.

Martin Hilbert¹, David Darmon²

¹Communication, Computational Social Science, University of California, Davis, CA 95616, USA.

Entropy (Basel, Switzerland)

|December 8, 2020

Summary

This summary is machine-generated.

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Algorithmic trading increases market complexity and predictability on a micro-level, but also generates more future uncertainty on a macro-level. This information-theoretic analysis clarifies the relationship between complexity and uncertainty in financial markets.

Area of Science:

Quantitative Finance
Computational Economics
Market Microstructure

Background:

Machine learning algorithms are increasingly used in trading to reduce uncertainty.
The impact of algorithmic trading on market-level uncertainty and complexity requires investigation.

Purpose of the Study:

To analyze the detectable results of uncertainty and complexity at the aggregated market level due to algorithmic trading.
To investigate the relationship between algorithmic trading, market complexity, and future uncertainty.

Main Methods:

Analysis of nearly one billion trades across eight currency pairs from 2007-2017.
Information-theoretic analysis, including the chain rule of entropy.

Main Results:

Keywords:

algorithmic trading complexity dynamical systems theory machine learning predictability

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Increased algorithmic trading correlates with more complex subsequences and predictable structures in bid-ask spreads.
Algorithmic involvement is linked to greater future market uncertainty, despite micro-level uncertainty reduction.
Uncertainty decreased at the fourth decimal place of currency values but increased at the fifth ('pip-trading').

Conclusions:

The apparent contradiction of decreased micro-level and increased macro-level uncertainty is explained by the inherent link between complexity and uncertainty.
Algorithmic trading amplifies complexity, leading to more combinatorial possibilities and thus greater future uncertainty.