Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity

• URL: http://arxiv.org/abs/2407.09557v1
• Date: Sat, 29 Jun 2024 20:56:58 GMT
• Title: Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity
• Authors: Alireza Mohammadshafie, Akram Mirzaeinia, Haseebullah Jumakhan, Amir Mirzaeinia,
• Abstract summary: This paper investigates the tendencies towards holding or trading financial assets as well as purchase diversity of deep reinforcement learning (DRL) algorithms. Our findings reveal that each DRL algorithm exhibits unique trading patterns and strategies, with A2C emerging as the top performer in terms of cumulative rewards.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Recent deep reinforcement learning (DRL) methods in finance show promising outcomes. However, there is limited research examining the behavior of these DRL algorithms. This paper aims to investigate their tendencies towards holding or trading financial assets as well as purchase diversity. By analyzing their trading behaviors, we provide insights into the decision-making processes of DRL models in finance applications. Our findings reveal that each DRL algorithm exhibits unique trading patterns and strategies, with A2C emerging as the top performer in terms of cumulative rewards. While PPO and SAC engage in significant trades with a limited number of stocks, DDPG and TD3 adopt a more balanced approach. Furthermore, SAC and PPO tend to hold positions for shorter durations, whereas DDPG, A2C, and TD3 display a propensity to remain stationary for extended periods.

Related papers

• Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent [44.99833362998488]
  We develop a novel Explainable Deep Reinforcement Learning (XDRL) approach for portfolio management. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy.
  arXiv  Detail & Related papers  (2024-07-19T17:40:39Z)
• E2EAI: End-to-End Deep Learning Framework for Active Investing [123.52358449455231]
  We propose an E2E that covers almost the entire process of factor investing through factor selection, factor combination, stock selection, and portfolio construction. Experiments on real stock market data demonstrate the effectiveness of our end-to-end deep leaning framework in active investing.
  arXiv  Detail & Related papers  (2023-05-25T10:27:07Z)
• Optimizing Trading Strategies in Quantitative Markets using Multi-Agent Reinforcement Learning [11.556829339947031]
  This paper explores the fusion of two established financial trading strategies, namely the constant proportion portfolio insurance ( CPPI) and the time-invariant portfolio protection (TIPP) We introduce two novel multi-agent RL (MARL) methods, CPPI-MADDPG and TIPP-MADDPG, tailored for probing strategic trading within quantitative markets. Our empirical findings reveal that the CPPI-MADDPG and TIPP-MADDPG strategies consistently outpace their traditional counterparts.
  arXiv  Detail & Related papers  (2023-03-15T11:47:57Z)
• Factor Investing with a Deep Multi-Factor Model [123.52358449455231]
  We develop a novel deep multi-factor model that adopts industry neutralization and market neutralization modules with clear financial insights. Tests on real-world stock market data demonstrate the effectiveness of our deep multi-factor model.
  arXiv  Detail & Related papers  (2022-10-22T14:47:11Z)
• Deep Reinforcement Learning Approach for Trading Automation in The Stock Market [0.0]
  This paper presents a model to generate profitable trades in the stock market using Deep Reinforcement Learning (DRL) algorithms. We formulate the trading problem as a Partially Observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market. 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.
  arXiv  Detail & Related papers  (2022-07-05T11:34:29Z)
• Safe-FinRL: A Low Bias and Variance Deep Reinforcement Learning Implementation for High-Freq Stock Trading [26.217805781416764]
  We propose Safe-FinRL, a novel DRL-based high-freq stock trading strategy enhanced by the near-stationary financial environment. Our main contributions are firstly, we separate the long financial time series into the near-stationary short environment. Secondly, we implement Trace-SAC in the near-stationary financial environment by incorporating the general retrace operator into the Soft Actor-Critic.
  arXiv  Detail & Related papers  (2022-06-13T05:40:03Z)
• Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
  This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective. We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
  arXiv  Detail & Related papers  (2021-12-08T14:55:21Z)
• Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review [0.0]
  Deep Reinforcement Learning agents proved to be to a force to be reckon with in many games like Chess and Go. This paper reviews the progress made so far with deep reinforcement learning in the subdomain of AI in finance. We conclude that DRL in stock trading has showed huge applicability potential rivalling professional traders under strong assumptions.
  arXiv  Detail & Related papers  (2021-05-31T22:26:43Z)
• FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance [20.43261517036651]
  We introduce a DRL library FinRL that facilitates beginners to expose themselves to quantitative finance. FinRL simulates trading environments across various stock markets, including NASDAQ-100, DJIA, S&P 500, HSI, SSE 50, and CSI 300. It incorporates important trading constraints such as transaction cost, market liquidity and the investor's degree of risk-aversion.
  arXiv  Detail & Related papers  (2020-11-19T01:35:05Z)
• Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations [88.94162416324505]
  A deep reinforcement learning (DRL) agent observes its states through observations, which may contain natural measurement errors or adversarial noises. Since the observations deviate from the true states, they can mislead the agent into making suboptimal actions. We show that naively applying existing techniques on improving robustness for classification tasks, like adversarial training, is ineffective for many RL tasks.
  arXiv  Detail & Related papers  (2020-03-19T17:59:59Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.