Deployment of Deep Reinforcement Learning and Market Sentiment Aware Strategies in Automated Stock Market Prediction
How to Cite?
Keerthi Sagiraju, Prof. Shashi Mogalla, "Deployment of Deep Reinforcement Learning and Market Sentiment Aware Strategies in Automated Stock Market Prediction," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 37-47, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I1P205
Tracking and responding to the dynamic stock market to maximize profit within a risk-controlled framework to meet the investment objective continues to be challenging. In addition to this, it is understood that market sentiment also plays a pivotal role in investment decisions. The deep reinforcement learning concept shows promising outcomes on stock market prices by training an intelligent agent. The stock market investments and returns can be predicted using Reinforcement learning with historical data and agent-based training in the given environment. In the proposed approach, a deep reinforcement learning agent is trained that uses historical stock and market sentiment consisting of Dow Jones and S&P 500 and get the resultant strategy in trading with Auto Encoder and LSTM along with four algorithms, namely Advantage Actor-Critic (A2C), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG) and Deep Q-Learning (DQN). These are compared using the financial metrics, Sharpe ratio, Sortino ratio, max drawdown, cumulative return, annual volatility, and annualized investment return.
Deep reinforcement learning (DRL), Stock Market Trading, Sentiment Analysis, Auto Encoder(AE), Long Short-Term Memory(LSTM).
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