Author
Kabbani, Taylan, Duman, Ekrem
Publication Date
2022
Publication Place
-
IEEE
Subject
Autonomous agent, Deep reinforcement learning, MDP, Sentiment analysis, Stock market, Technical indicators, Twin delayed deep deterministic policy gradient
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
2169-3536
Record ID
b6722a2a-3815-4d39-bbdd-42e3b379d68d
Library Location
Industrial Engineering
Date
2022
Sample Text
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 in strategic decision-making.
DOI
10.1109/ACCESS.2022.3203697
Cilt
10
