Special Issue on Algorithms in Computational Finance

Chinthalapati, V. L. Raju; Tsang, Edward

doi:10.3390/a12040069

Cited by 2 publications

(1 citation statement)

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“…In our approach, we only try to predict the ways investors react to news, but not the news itself! See here, e.g., (Chinthalapati and Tsang 2019) for various algorithms used in financial mathematics.…”

Section: Trading Spy (Longmentioning

confidence: 99%

Artificial Intelligence Approach to Momentum Risk-Taking

Cherednik¹

2021

IJFS

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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.

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Section: Trading Spy (Longmentioning

confidence: 99%