Equity Mutual Funds' Performance Prediction Using Reinforcement Learning

Abstract

This dissertation investigates the utility of reinforcement learning (RL) algorithms in predicting the performance of equity mutual funds. The primary goal of this research is to discover how RL strategies can efficiently analyze ancient data and offer insights into the future performance of mutual funds. By leveraging RL algorithms, this takes a look at objectives to cope with the challenges related to traditional prediction techniques, consisting of statistical models and machine learning techniques, which regularly battle to seize the dynamic and nonlinear nature of monetary markets. The research methodology includes a combination of secondary studies, inclusive of a comprehensive literature assessment, and primary studies, which involve the introduction of
quantitative models and the use of one-of-a-kind RL algorithms. Historical information on equity mutual prices can be accumulated from professional monetary databases, preprocessed to ensure first-rate and consistency, and divided into schooling, validation, and trying out sets. Relevant functions influencing mutual fund overall performance might be decided on and dimensionality-reduced to construct an informative characteristic space. Various RL algorithms, which include Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Actor-Critic methods, might be taken into consideration for the
prediction challenge. The suitability of every algorithm could be assessed based totally on its ability to handle non-stop action spaces, convergence residences, and computational performance. Hyperparameters of decided-on RL algorithms might be tuned by using techniques including grid seek or Bayesian optimization to optimize model performance. The trained RL models will examine the highest quality policies for fund choice and portfolio management through coverage iteration, refining their strategies based totally on remarks from the surroundings. Model overall performance may be evaluated by the use of
widespread metrics together with Sharpe ratio, cumulative go back, most drawdown, and alpha.
The performance of RL models may also be in comparison against traditional prediction approaches to spotlight their relative strengths and weaknesses. The predicted final results of this study are to offer treasured insights into the application
of RL techniques for predicting the performance of equity mutual funds. By leveraging RL algorithms, traders and fund managers can probably improve their ability to forecast mutual fund performance and make extra knowledgeable investment choices in dynamic marketplace conditions. Additionally, this observes pursuits to make contributions to each academic study and realistic funding control by means of laying the basis for future improvements in the subject.