mlsanityData to 14 Oct 2026

Deep Reinforcement Learning Approach for Trading Automation in The Stock Market

arXiv 2208.07165 ↗First posted 5 Jul 2022q-fin.TR cs.CE cs.LGcited by 9

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Abstract (arXiv)

Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with their environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially Observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm reporting a 2.68 Sharpe Ratio on unseen data set (test data). From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages of strategic decision-making.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 10 of its 43 references that are in the map. First posted on arXiv 5 Jul 2022.

Builds on

10 of 43 references.

References · 10

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  4. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  5. 2017Deep Reinforcement Learning that Matterscited by 754
  6. 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 385
  7. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 128
  8. 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
  9. 2019Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicatorscited by 4
  10. 2019Capturing Financial markets to apply Deep Reinforcement Learningcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 1; 2026: 2.

9 papers.

  1. 2024FinCon: A Synthesized LLM Multi-Agent System with Conceptual Verbal Reinforcement for Enhanced Financial Decision Makingcited by 113
  2. 2024INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agentcited by 64
  3. 2024SleeperNets: Universal Backdoor Poisoning Attacks Against Reinforcement Learning Agentscited by 19
  4. 2024Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Executioncited by 1
  5. 2025A Deep Reinforcement Learning Approach to Automated Stock Trading, using xLSTM Networkscited by 1
  6. 2023An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Tradingcited by 0
  7. 2024Provably Efficient Partially Observable Risk-Sensitive Reinforcement Learning with Hindsight Observationcited by 0
  8. 2026Learning Stock Trading Policies via Barycenter-Based Adversarial Inverse Reinforcement Learningcited by 0
  9. 2026Self-Supervised Auxiliary Task Discovery for Stable Reinforcement Learning in Stock Tradingcited by 0