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Quantitative trading (QT), which refers to the usage of mathematical models and data-driven techniques in analyzing the financial market, has been a popular topic in both academia and financial industry since 1970s.
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Using support vector machine with a hybrid feature selection method to the stock trend prediction
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Corn: Correlation-driven nonparametric learning approach for portfolio selection
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Testing different reinforcement learning configurations for financial trading: Introduction and applications
Francesco Bertoluzzo and Marco Corazza. 2012 · 2012
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A hybrid stock selection model using genetic algorithms and support vector regression
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Stock price prediction using the ARIMA model. In Proceedings of the 6th International Conference on Computer Modelling and Simulation (ICCMS) . 106–112
Adebiyi A Ariyo, Adewumi O Adewumi, and Charles K Ayo. 2014 · 2014
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Buy Low, Sell High: A High Frequency Trading Perspective
Álvaro Cartea, Sebastian Jaimungal, and Jason Ricci. 2014 · 2014
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A reinforcement learning extension to the Almgren-Chriss framework for optimal trade execution. In Proceedings of the IEEE Conference on Computational Intelligence for Financial Engineering & Economics . 457–464
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Online portfolio selection: A survey
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Deterministic policy gradient algorithms. In Proceedings of the 31st International Conference on Machine Learning (ICML) . 387–395
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Nonnegative elastic net and application in index tracking
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Algorithmic and High-frequency Trading
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Temporal relational ranking for stock prediction
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Deep reinforcement learning for market making in corporate bonds: Beating the curse of dimensionality
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CNNpred: CNN-based stock market prediction using a diverse set of variables
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Improving financial trading decisions using deep Q-learning: Predicting the number of shares, action strategies, and transfer learning
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Enhancing time-series momentum strategies using deep neural networks
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