A Model to Coordinate Interests in Investment Management

G. I. Belyavsky, N. V. Danilova () and G. A. Ougolnitsky ()
Additional contact information
G. I. Belyavsky: Institute of Mathematics, Mechanics and Computer Sciences, Southern Federal University, Milchakova Street, 8a, Rostov-on-Don, Rostov Region, Russia
N. V. Danilova: Institute of Mathematics, Mechanics and Computer Sciences, Southern Federal University, Milchakova Street, 8a, Rostov-on-Don, Rostov Region, Russia
G. A. Ougolnitsky: Institute of Mathematics, Mechanics and Computer Sciences, Southern Federal University, Milchakova Street, 8a, Rostov-on-Don, Rostov Region, Russia

International Game Theory Review (IGTR), 2023, vol. 25, issue 01, 1-12

Abstract: The portfolio selection problem is treated as a two-player game: one player [Unit Investment Trust (UIT)] deals with investments, and the other (agent) allocates funds for it. The game is described in formal terms, and its solution is found. Note that the condition of sustainable development is derived directly from the game solution. UIT’s learning is analyzed. Three possible statements of the UIT’s learning problem are investigated, each corresponding to a particular machine learning model. An online learning model is considered to predict the information necessary for the agent’s optimal behavior in the game. A computational experiment is provided: the three learning models are applied, and the simulation results are discussed.

Keywords: Machine learning; interests coordination; investment management (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0219198923500020
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:igtrxx:v:25:y:2023:i:01:n:s0219198923500020

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0219198923500020

Access Statistics for this article

International Game Theory Review (IGTR) is currently edited by David W K Yeung

More articles in International Game Theory Review (IGTR) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().