Utilizing stochastic differential equations and random forest for precision forecasting in stock market dynamics

Abstract

The investigation for precision in Stock Market Forecasts (SMF) developments has led financial professionals to explore numerous modeling approaches. This paper investigates a new technique aimed at advancing the precision of Support Vector Machine (SMF) by combining the application of Random Forest (RF) methods with Stochastic Differential Equations (SDEs). Market deviations can be enhanced using the Geometric Brownian Motion (GBM) scheme, but this approach has problems because it denotes that market deviations will stay identical. To solve these challenges and develop how the GBM context more precisely corresponds to the unpredictable stock market factors, Random Forest (RF) is used for variable parameter prediction. The change in migration and deviation variables in GBM procedures has been defined using RF approaches in the present study using historical data from the stock market. It examines the performance of the RF-enhanced GBM method by comparing it to static variables in GBM, the Heston Model, and standard GBM while taking unpredictable chances into account. The quantitative metrics, such as the Sharpe Ratio (SR), Cumulative Returns (CR), and Maximum Drawdown (MD), were computed for a total of five stock markets in the research. When compared to the other models, the RF-enhanced GBM generally displayed a competitive edge in terms of risk-adjusted income and was highly accurate in terms of coming up with precise projections.