Artificial Intelligence Approach to Momentum Risk-Taking

Ivan Cherednik
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Ivan Cherednik: Department of Mathematics, UNC at Chapel Hill, Phillips Hall, Chapel Hill, NC 27599, USA

IJFS, 2021, vol. 9, issue 4, 1-42

Abstract: We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management focused on short-term volatility. Its implementation, a fully automated momentum equity trading system, is systematically discussed in this paper. It proved to be successful in extensive historical and real-time experiments. Momentum risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning. We begin with a new mathematical approach to news impact on share prices, which models well their power-type growth, periodicity, and the market phenomena like price targets and profit-taking. This theory generally requires Bessel and hypergeometric functions. Its discretization results in some tables of bids, basically, expected returns for main investment horizons, the key in our trading system. A preimage of our approach is a new contract card game. There are relations to random processes and the fractional Brownian motion. The ODE we obtained, especially those of Bessel-type, appeared to give surprisingly accurate modeling of the spread of COVID-19.

Keywords: news impact; decision-making; risk management; momentum trading; power laws; econophysics; behavioral finance; cognitive theory; artificial intelligence; Bessel functions (search for similar items in EconPapers)
JEL-codes: F2 F3 F41 F42 G1 G2 G3 (search for similar items in EconPapers)
Date: 2021
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