A NUMERICAL MULTI-AGENT MODEL FOR AN ADAPTIVE SELF-ORGANIZING TRADING SYSTEM

U. Abdullaev

Abstract

The purpose of the study is to develop an adaptive mathematical model for forecasting financial markets based on the Financial instruments exhibit the greatest sensitivity to trading volumes (β=0.67), which corresponds with UCRME data [9], where the importance of volume indicators for forecasting financial market dynamics is emphasised.principles of self-organisation. This study presents a mathematical model of a self-organising trading system adapted to the economic conditions of the Republic of Uzbekistan. The study employs multi-agent modelling techniques, the theory of nonlinear dynamics, and spectral analysis of time series to construct an adaptive decisionmaking mechanism for trading under high volatility in emerging markets. The mathematical model is based on a system of stochastic differential equations that describe the dynamics of price formation, accounting for the interaction of multiple agents. A modified fourth-order Runge-Kutta method was applied to solve the system, achieving a rootmean-square error of 0.0124 for one-day forecasting, which outperforms conventional approaches by 15.6%. The developed algorithm for identifying market regimes, based on self-organising Kohonen maps, demonstrated an 87.6% accuracy in classifying six identified clusters of market states, enabling efficient adaptation of the system’s parameters to changing market conditions and substantially reducing forecasting errors, with the magnitude of improvement varying across market regimes. Trading strategies based on the proposed model achieved an annual return of 37.2% with a Sharpe ratio of 1.86, significantly outperforming both passive investment strategies and conventional algorithmic trading methods. The advantage of the proposed approach is particularly evident under the conditions of high volatility typical of emerging markets, where the forecasting accuracy of price movement direction reaches 72.8% for a one-day horizon. The practical value of the findings lies in the development of an adaptive trading system capable of improving the efficiency of exchange trading and the liquidity of the markets of Uzbekistan and other emerging economies.

Source: semanticscholar · PDF

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