STOCK TRADING OF JII ISLAMIC STOCKS USING DEEP REINFORCEMENT LEARNING - Nugraha, Teguh
From the perspective of devout Muslims, they are restricted to investing only in assets that are not contrary to Islamic principles. However, it is challenging to gain optimal return with fewer alternatives in Islamic stocks. To address this challenge, the potential of Deep Reinforcement Learning (DRL) is explored to optimize the stock trading. Stock trading task is modeled as a Markov Decision Process (MDP) problem, due to its stochastic and interactive nature. Then the trading objective is a problem of maximization. The DRL agents used are actor-critic algorithms, namely A2C, DDPG, and PPO. The selected portfolio consists of 30 most liquid Islamic stocks in Indonesia that constitutes JII index. Finally, the performance is compared between the algorithms, and against LQ45 index as the benchmark that consists of the 45 most liquid conventional stocks in the Indonesian Stock Exchange (IDX). The result shows that trading on Islamic stocks from Jan 2019 to Dec 2020 using the DRL agents can outperform the benchmark index of conventional stocks.