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A Survey of Forex and Stock Price Prediction Using Deep Learning

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  • Zexin Hu
  • Yiqi Zhao
  • Matloob Khushi

Abstract

The prediction of stock and foreign exchange (Forex) had always been a hot and profitable area of study. Deep learning application had proven to yields better accuracy and return in the field of financial prediction and forecasting. In this survey we selected papers from the DBLP database for comparison and analysis. We classified papers according to different deep learning methods, which included: Convolutional neural network (CNN), Long Short-Term Memory (LSTM), Deep neural network (DNN), Recurrent Neural Network (RNN), Reinforcement Learning, and other deep learning methods such as HAN, NLP, and Wavenet. Furthermore, this paper reviewed the dataset, variable, model, and results of each article. The survey presented the results through the most used performance metrics: RMSE, MAPE, MAE, MSE, accuracy, Sharpe ratio, and return rate. We identified that recent models that combined LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning method yielded great returns and performances. We conclude that in recent years the trend of using deep-learning based method for financial modeling is exponentially rising.

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  • Zexin Hu & Yiqi Zhao & Matloob Khushi, 2021. "A Survey of Forex and Stock Price Prediction Using Deep Learning," Papers 2103.09750, arXiv.org.
    • Handle: RePEc:arx:papers:2103.09750
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Iqbal Murtza & Ayesha Saadia & Rabia Basri & Azhar Imran & Abdullah Almuhaimeed & Abdulkareem Alzahrani, 2022. "Forex Investment Optimization Using Instantaneous Stochastic Gradient Ascent—Formulation of an Adaptive Machine Learning Approach," Sustainability, MDPI, vol. 14(22), pages 1-13, November.
    2. Branka Hadji Misheva & Joerg Osterrieder, 2023. "A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods," Papers 2311.07513, arXiv.org.
    3. Dorien Herremans & Kah Wee Low, 2022. "Forecasting Bitcoin volatility spikes from whale transactions and CryptoQuant data using Synthesizer Transformer models," Papers 2211.08281, arXiv.org.
    4. Jaideep Singh & Matloob Khushi, 2021. "Feature Learning for Stock Price Prediction Shows a Significant Role of Analyst Rating," Papers 2103.09106, arXiv.org.
    5. Xiaodong Zhang & Suhui Liu & Xin Zheng, 2021. "Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism," Mathematics, MDPI, vol. 9(8), pages 1-21, April.
    6. Rian Dolphin & Barry Smyth & Yang Xu & Ruihai Dong, 2021. "Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities," Papers 2107.03926, arXiv.org.
    7. Shima Nabiee & Nader Bagherzadeh, 2023. "Stock Trend Prediction: A Semantic Segmentation Approach," Papers 2303.09323, arXiv.org.
    8. Jian Guo & Saizhuo Wang & Lionel M. Ni & Heung-Yeung Shum, 2022. "Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence," Papers 2301.04020, arXiv.org.
    9. Srivinay & B. C. Manujakshi & Mohan Govindsa Kabadi & Nagaraj Naik, 2022. "A Hybrid Stock Price Prediction Model Based on PRE and Deep Neural Network," Data, MDPI, vol. 7(5), pages 1-11, April.
    10. Cheng Zhang & Nilam Nur Amir Sjarif & Roslina Ibrahim, 2023. "Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022," Papers 2305.04811, arXiv.org, revised Sep 2023.
    11. Jinan Zou & Qingying Zhao & Yang Jiao & Haiyao Cao & Yanxi Liu & Qingsen Yan & Ehsan Abbasnejad & Lingqiao Liu & Javen Qinfeng Shi, 2022. "Stock Market Prediction via Deep Learning Techniques: A Survey," Papers 2212.12717, arXiv.org, revised Feb 2023.
    12. Marc Wildi & Branka Hadji Misheva, 2022. "A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection," Papers 2212.02906, arXiv.org.
    13. Matteo Prata & Giuseppe Masi & Leonardo Berti & Viviana Arrigoni & Andrea Coletta & Irene Cannistraci & Svitlana Vyetrenko & Paola Velardi & Novella Bartolini, 2023. "LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study," Papers 2308.01915, arXiv.org, revised Sep 2023.
    14. Mimansa Rana & Nanxiang Mao & Ming Ao & Xiaohui Wu & Poning Liang & Matloob Khushi, 2021. "Clustering and attention model based for intelligent trading," Papers 2107.06782, arXiv.org, revised Aug 2021.
    15. Fateme Shahabi Nejad & Mohammad Mehdi Ebadzadeh, 2023. "Stock market forecasting using DRAGAN and feature matching," Papers 2301.05693, arXiv.org.
    16. Adrian Millea, 2021. "Deep Reinforcement Learning for Trading—A Critical Survey," Data, MDPI, vol. 6(11), pages 1-25, November.
    17. Yugo Fujimoto & Kei Nakagawa & Kentaro Imajo & Kentaro Minami, 2022. "Uncertainty Aware Trader-Company Method: Interpretable Stock Price Prediction Capturing Uncertainty," Papers 2210.17030, arXiv.org, revised Nov 2022.
    18. Yunze Li & Yanan Xie & Chen Yu & Fangxing Yu & Bo Jiang & Matloob Khushi, 2021. "Feature importance recap and stacking models for forex price prediction," Papers 2107.14092, arXiv.org.
    19. Yuze Lu & Hailong Zhang & Qiwen Guo, 2023. "Stock and market index prediction using Informer network," Papers 2305.14382, arXiv.org.
    20. Liping Wang & Jiawei Li & Lifan Zhao & Zhizhuo Kou & Xiaohan Wang & Xinyi Zhu & Hao Wang & Yanyan Shen & Lei Chen, 2023. "Methods for Acquiring and Incorporating Knowledge into Stock Price Prediction: A Survey," Papers 2308.04947, arXiv.org.
    21. Matej Steinbacher, 2023. "Predicting Stock Price Movement as an Image Classification Problem," Papers 2303.01111, arXiv.org.
    22. Abdellilah Nafia & Abdellah Yousfi & Abdellah Echaoui, 2023. "Equity-Market-Neutral Strategy Portfolio Construction Using LSTM-Based Stock Prediction and Selection: An Application to S&P500 Consumer Staples Stocks," IJFS, MDPI, vol. 11(2), pages 1-48, March.
    23. Reza Yarbakhsh & Mahdieh Soleymani Baghshah & Hamidreza Karimaghaie, 2023. "Predicting risk/reward ratio in financial markets for asset management using machine learning," Papers 2311.09148, arXiv.org.

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