Journal ArticleDOI
A parallel multi-module deep reinforcement learning algorithm for stock trading
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TLDR
A novel model named Parallel Multi-Module Deep Reinforcement Learning (PMMRL) algorithm, which achieves a higher profit and a lower drawdown than several state-of-the-art algorithms on China stock market.About:
This article is published in Neurocomputing.The article was published on 2021-08-18. It has received 13 citations till now. The article focuses on the topics: Algorithmic trading & Reinforcement learning.read more
Citations
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Journal ArticleDOI
Deep Reinforcement Learning with the Random Neural Network
TL;DR: In this paper , a Deep Reinforcement Learning (DRL) algorithm was proposed for predicting stock market trends, which includes the previous learnings entirely from previous rewards, rather than only the actual one.
Journal ArticleDOI
Multi-type data fusion framework based on deep reinforcement learning for algorithmic trading
Journal ArticleDOI
An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges
TL;DR: In this article , the authors 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 and outline potential future study paths in this area based on the overview that was presented before.
Journal ArticleDOI
Stock Price Forecasting with Artificial Neural Networks Long Short-Term Memory: A Bibliometric Analysis and Systematic Literature Review
TL;DR: In this paper , the authors map the academic literature on stock price forecasting with Long-Term Memory Artificial Neural Networks (RNA LSTM) and find that 65% of the studies are the comparison between RNN LSTMs and other artificial neural networks, and 57% of studies include improvements to existing neural network models and 42% new projection models.
Journal ArticleDOI
Developing a smart stock trading system equipped with a novel risk control mechanism for investors with different risk appetites
TL;DR: In this article , the authors proposed a framework for smart stock trading that uses a new approach to risk management, which selects blue-chip stocks through a fundamental analysis and predicts trading signals with technical indicators and a deep neural network model for the selected stocks.
References
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TL;DR: In this article, the authors identify five common risk factors in the returns on stocks and bonds, including three stock-market factors: an overall market factor and factors related to firm size and book-to-market equity.
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Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TL;DR: Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.
Proceedings ArticleDOI
TensorFlow: a system for large-scale machine learning
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TL;DR: TensorFlow as mentioned in this paper is a machine learning system that operates at large scale and in heterogeneous environments, using dataflow graphs to represent computation, shared state, and the operations that mutate that state.
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