Machine Learning
S. Khonsha; M. A. Sarram; R. Sheikhpour
Abstract
Background and Objectives: Stock recommender system (SRS) based on deep reinforcement learning (DRL) has garnered significant attention within the financial research community. A robust DRL agent aims to consistently allocate some amount of cash to the combination of high-risk and low-risk ...
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Background and Objectives: Stock recommender system (SRS) based on deep reinforcement learning (DRL) has garnered significant attention within the financial research community. A robust DRL agent aims to consistently allocate some amount of cash to the combination of high-risk and low-risk stocks with the ultimate objective of maximizing returns and balancing risk. However, existing DRL-based SRSs focus on one or, at most, two sequential trading agents that operate within the same or shared environment, and often make mistakes in volatile or variable market conditions. In this paper, a robust Concurrent Multiagent Deep Reinforcement Learning-based Stock Recommender System (CMSRS) is proposed.Methods: The proposed system introduces a multi-layered architecture that includes feature extraction at the data layer to construct multiple trading environments, so that different feed DRL agents would robustly recommend assets for trading layer. The proposed CMSRS uses a variety of data sources, including Google stock trends, fundamental data and technical indicators along with historical price data, for the selection and recommendation suitable stocks to buy or sell concurrently by multiple agents. To optimize hyperparameters during the validation phase, we employ Sharpe ratio as a risk adjusted return measure. Additionally, we address liquidity requirements by defining a precise reward function that dynamically manages cash reserves. We also penalize the model for failing to maintain a reserve of cash.Results: The empirical results on the real U.S. stock market data show the superiority of our CMSRS, especially in volatile markets and out-of-sample data.Conclusion: The proposed CMSRS demonstrates significant advancements in stock recommendation by effectively leveraging multiple trading agents and diverse data sources. The empirical results underscore its robustness and superior performance, particularly in volatile market conditions. This multi-layered approach not only optimizes returns but also efficiently manages risks and liquidity, offering a compelling solution for dynamic and uncertain financial environments. Future work could further refine the model's adaptability to other market conditions and explore its applicability across different asset classes.
