A Robust Concurrent Multi-Agent Deep Reinforcement Learning ‎based Stock Recommender System

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.‎ 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.‎ 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.‎ 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.‎