Predicting the Return of an Equity Investment Fund Using the Random Forest Algorithm: A Case Study of a Fund Managed by Charisma Asset Management
Pages 1-14
https://doi.org/10.22034/ijftgrs.2026.252602
Hossein Eslami Mofid Abadi, Amirali Nematzadeh
Abstract PURPOSE: This study evaluates the ability of the Random Forest algorithm to predict the daily return of an equity investment fund managed by Charisma Asset Management, compares it with linear regression and a single decision tree as reference models, and identifies the most influential predictors of fund return.
DESIGN/METHODOLOGY/APPROACH: Daily data for the Karis (Sepehr Charisma) equity fund from Farvardin 1399 to Esfand 1403 (2020–2025) were cleaned by removing non-trading days and verified data-recording errors, resulting in 1,019 actual trading days. Fund-return, market-return, and asset-composition data were matched by date, and return-based features were constructed using only lagged information, leaving 989 observations for model estimation. Fund-specific and market variables were used to predict the total daily return. The models were trained using a chronological split, tuned with five-fold time-series cross-validation, and evaluated using RMSE, MAE, R², and directional accuracy.
FINDINGS: On the test set, linear regression achieved the highest predictive performance (R² = 0.721), whereas Random Forest produced comparable results (R² = 0.680) and outperformed the single decision tree. In five-fold time-series cross-validation, Random Forest achieved the highest mean R² and the lowest mean RMSE. Market return and short-term momentum variables were the dominant predictors. Excluding the contemporaneous market return reduced the R² of both models to approximately 0.28. Because no formal forecast-comparison tests were conducted, these comparisons are descriptive.
RESEARCH IMPLICATIONS: For asset managers and investors, daily fund returns are driven mainly by systematic market movements, so the information set matters more than algorithmic complexity. The single-fund design limits generalizability, the asset-allocation variables varied little over time, which may have reduced their predictive contribution, and macroeconomic predictors were not available at a daily frequency.
ORIGINALITY/VALUE: This study provides a case study of daily return prediction for a specific Iranian equity fund using Random Forest, with particular emphasis on performance variation across time-series validation folds and on the distinction between contemporaneous explanation and genuine forecasting.

