Deep reinforcement learning approach for trading automation in the stock market

Name: Deep reinforcement learning approach for trading automation in the stock market
Author: Kabbani, Taylan, Duman, Ekrem

İsim	Deep reinforcement learning approach for trading automation in the stock market
Yazar	Kabbani, Taylan, Duman, Ekrem
Basım Tarihi:	2022
Basım Yeri	- IEEE
Konu	Autonomous agent, Deep reinforcement learning, MDP, Sentiment analysis, Stock market, Technical indicators, Twin delayed deep deterministic policy gradient
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2169-3536
Kayıt Numarası	b6722a2a-3815-4d39-bbdd-42e3b379d68d
Lokasyon	Industrial Engineering
Tarih	2022
Örnek Metin	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

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Deep reinforcement learning approach for trading automation in the stock market

Yazar Kabbani, Taylan, Duman, Ekrem

Basım Tarihi 2022

Basım Yeri - IEEE

Konu Autonomous agent, Deep reinforcement learning, MDP, Sentiment analysis, Stock market, Technical indicators, Twin delayed deep deterministic policy gradient

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2169-3536

Kayıt Numarası b6722a2a-3815-4d39-bbdd-42e3b379d68d

Lokasyon Industrial Engineering

Tarih 2022

Örnek Metin 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