Applied Sciences · 2023 · 187 citations · 72 references
ComputingComputer ScienceFinanceFinancial EconomicsMachine LearningQuantitative FinanceAlgorithmic TradingDeep LearningFinancial ForecastingNeural NetworksReinforcement LearningTime Series PredictionDeep Learning For Time Series ForecastingDeep Learning-based ForecastingDeep Learning-based Price ForecastingDeep Reinforcement LearningMachine Learning Forecasting ModelsMachine Learning-based Price ForecastingPredictionReinforcement Learning For Trading
Forecasting stock market behavior is challenging, and researchers have explored linear, ML, DL, RL, and DRL models, with deep learning and deep reinforcement learning increasingly employed for prediction and algorithmic trading. The paper aims to help researchers stay current with ML, DL, RL, and DRL applications in quantitative finance and to outline future research directions. The review shows that DRL agents can generate automated trading strategies and highlights the broader utility of ML, DL, RL, and DRL in quantitative finance.
Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated linear models as well as models that are based on machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) in order to create an accurate predictive model. Machine learning algorithms can now extract high-level financial market data patterns. Investors are using deep learning models to anticipate and evaluate stock and foreign exchange markets due to the advantage of artificial intelligence. Recent years have seen a proliferation of the deep reinforcement learning algorithm’s application in algorithmic trading. DRL agents, which combine price prediction and trading signal production, have been used to construct several completely automated trading systems or strategies. Our objective is to enable interested researchers to stay current and easily imitate earlier findings. In this paper, we have worked to explain the utility of Machine Learning, Deep Learning, Reinforcement Learning, and Deep Reinforcement Learning in Quantitative Finance (QF) and the Stock Market. We also outline potential future study paths in this area based on the overview that was presented before.
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