Deep Direct Reinforcement Learning for Financial Signal Representation and Trading

By Yue Deng, Feng Bao, Youyong Kong, Zhiquan Ren, Qionghai Dai. 2017

792 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Finance, Management Science and Operations Research, Reinforcement Learning

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Citations in the corpus

Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[141] 300 stocks from SZSE, Commodity 2014-2015 Price data FDDR, DNN + RL Profit, return, SR, profit-loss curves Keras”
Deep Learning for Financial Applications : A Survey (2020, methodology): “In [40], the authors used Fuzzy Deep Direct Reinforcement Learning (FDDR) for stock price prediction and trading signal generation.”
Stock Price Prediction by Deep Neural Generative Model of News Articles (2018, background): “A market simulation considering commissions and time lags inherent in trading is also future work [30].”
A Study on Overfitting in Deep Reinforcement Learning (2018, methodology): “…concerning our everyday life such as healthcare (Chakraborty & Murphy, 2014; Kosorok & Moodie, 2015) and finance (Li et al., 2009; Yu et al., 2009; Deng et al., 2017), and fundamental infrastructures such as power grids (Wen et al., 2015; Glavic et al., 2017) and traffic control (Mannion et al.,…”
AI in Finance: Challenges, Techniques, and Opportunities (2021, methodology): “, prices) and trends of securities, foreign currencies, and commodities; modeling the supply-demand relations and stakeholder relationships in property markets, energy markets and capital markets; optimizing order routing in foreign exchange and features markets; and optimizing trading agent actions”
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning (2019, background): “Namely, RL agents often overfit to training environments, thus the lack of generalization ability makes them unreliable in several applications, such as health care (Chakraborty & Murphy, 2014) and finance (Deng et al., 2016).”
Agent Inspired Trading Using Recurrent Reinforcement Learning and LSTM Neural Networks (2017, methodology): “…learning with recurrent reinforcement learning, Lu[8] had shown using Linear Matrix Inequilities can beat the risk free rate, and Deng et al.[9] had shown max return can be used as objective in recurrent reinforcement learning as well as using deep learning transformations to initialize the…”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “Reinforcement learning is also used for building algorithmic trading systems (Deng et al., 2016).”
A Stock Market Trading System Using Deep Neural Network (2017, neutral): “Experiment has been conducted to test its profitability and performance as a trading system in the stock and commodity market [6].”
Recent advances in reinforcement learning in finance (2021, methodology): “…functions include cash inflow or outflow (depending on whether we sell or buy) (Nevmyvaka et al., 2006; Shen et al., 2014), implementation shortfall (Hendricks &Wilcox, 2014), profit (Deng et al., 2017), Sharpe ratio (Deng et al., 2017), return (Jeong . . . Kim, 2019), and PnL (Wei et al., 2019).”
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search (2020, background): “…AlphaGo and beating human video game players, have triggered tremendously increased enthu-siasm to develop and deploy DRL-powered intelligence into numerous real-world inference and control applications, including robotics [42, 22, 53], autonomous vehicles [83], ﬁ-nance [15] and smart grids [19].”
A Review of Machine Learning and Deep Learning Applications (2018, methodology): “A prediction domain topic such as Financial Signal Representation and Trading can be very well be implemented usingDirect Deep Reinforcement Learning technique in this paper [19].”
An Introduction to Deep Reinforcement Learning (2018, positive): “They also have applications in finance (Deng et al., 2017), smart grids (François-Lavet, 2017), etc.”
Machine behaviour (2019, methodology): “More sophisticated agents may compute strategies based on adaptive heuristics or explicit maximization of expected utility 86 .”
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (2017, background): “These include Moody and Saﬀell (2001), Dempster and Leemans (2006), Cumming (2015), and the recent deep RL utilization by Deng et al. (2017).”
A preliminary study on deep learning for predicting social insurance payment behavior (2017, background): “While sequence-to-sequence prediction model learning have been developed for dealing with many kinds of sequential data, such as Financial Data Analysis [3], Speech Signal Processing [6] [8] [17] [24], and Natural Language Processing [23], the problem has not been well explored under the context of ”