a robust concurrent multi-agent deep reinforcement learning ‎based stock recommender system

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.‎