Artificial intelligence approach to momentum risk-taking

Artikel

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Journal: International Journal of Financial Studies ; ISSN: 2227-7072 ; Volume: 9 ; Year: 2021 ; Issue: 4 ; Pages: 1-42 ; Basel: MDPI

Classification and Topics

• Classification: Wirtschaft
• Subject: artificial intelligence ; behavioral finance ; Bessel functions ; cognitive theory ; decision-making ; econophysics ; momentum trading ; news impact ; power laws ; risk management

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Tscherednik, Iwan Wladimirowitsch
• Event: Veröffentlichung
• (who): MDPI
• (where): Basel
• (when): 2021

Further information

• DOI: doi:10.3390/ijfs9040058
• Handle: http://hdl.handle.net/10419/257803
• Last update: 10.03.2025, 11:45 AM CET