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Artificial Intelligence Approach to Momentum Risk-Taking

Author

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  • Ivan Cherednik

    (Department of Mathematics, UNC at Chapel Hill, Phillips Hall, Chapel Hill, NC 27599, USA)

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.

Suggested Citation

  • Ivan Cherednik, 2021. "Artificial Intelligence Approach to Momentum Risk-Taking," IJFS, MDPI, vol. 9(4), pages 1-42, October.
  • Handle: RePEc:gam:jijfss:v:9:y:2021:i:4:p:58-:d:661712
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