Yanjun Long
ABSTRACT
For this paper, we utilize the famous LSTM model and modify it to a model that consists of two layers of Bi-LSTM. Our trading strategy with the model is to trade the stocks with the highest growth rates predicted by the model and the strategy repeat once a day. To test the efficiency of our model, we change parameters of the experiment: time-interval and both number and frequency of buying stocks. In this way, we can conclude that the model is stable. Whether reducing the frequency of buying increases profit depending on the degree of this parameter. And the number of buying stocks sometimes prevent greater loss during the strategy.
- Ignatovich, Denis Andrey. "Quantitative Trading System." (2006)Google Scholar
- Kumiega, Andrew, “Trading System Capability.” Quantitative Finance, vol. 14, no. 3, 2014, pp. 383–392., https://doi.org/10.1080/14697688.2013.787492.Google ScholarCross Ref
- F. Q. Lauzon, "An introduction to deep learning," 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), 2012, pp. 1438-1439, doi: 10.1109/ISSPA.2012.6310529.Google ScholarCross Ref
- Heaton, J.B.. “Deep Learning in Finance.”(2016)Google Scholar
- Heaton, J.B. “Deep Learning for finance: deep portfolios.”(2016)Google Scholar
- Culkin, Robert, and Sanjiv R. Das. "Machine learning in finance: the case of deep learning for option pricing." Journal of Investment Management 15.4 (2017): 92-100.Google Scholar
- Troiano, Luigi, “Replicating a Trading Strategy by Means of LSTM for Financial Industry Applications.” IEEE Transactions on Industrial Informatics, vol. 14, no. 7, 2018, pp. 3226–3234., https://doi.org/10.1109/tii.2018.2811377.Google ScholarCross Ref
- Cao, Jian, “Financial Time Series Forecasting Model Based on CEEMDAN and LSTM.” Physica A: Statistical Mechanics and Its Applications, vol. 519, 2019, pp. 127–139., https://doi.org/10.1016/j.physa.2018.11.061.Google ScholarCross Ref
- Sherstinsky, Alex. “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network.” Physica D: Nonlinear Phenomena, vol. 404, 2020, p. 132306., https://doi.org/10.1016/j.physd.2019.132306.Google ScholarCross Ref
- Moghar, Adil, and Mhamed Hamiche. “Stock Market Prediction Using LSTM Recurrent Neural Network.” Procedia Computer Science, vol. 170, 2020, pp. 1168–1173., https://doi.org/10.1016/j.procs.2020.03.049.Google ScholarCross Ref
- hen, Kai, “A LSTM-Based Method for Stock Returns Prediction: A Case Study of China Stock Market.” 2015 IEEE International Conference on Big Data (Big Data), 2015, https://doi.org/10.1109/bigdata.2015.7364089.Google ScholarDigital Library
- Fang, Wen-Xiang, “Combine Facebook Prophet and LSTM with BPNN Forecasting Financial Markets: The Morgan Taiwan Index.” 2019 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 2019, https://doi.org/10.1109/ispacs48206.2019.8986377.Google ScholarCross Ref
- Fischer, Thomas, and Christopher Krauss. “Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions.” European Journal of Operational Research, vol. 270, no. 2, 2018, pp. 654–669., https://doi.org/10.1016/j.ejor.2017.11.054.Google ScholarCross Ref
- Althelaya, Khaled A., “Evaluation of Bidirectional LSTM for Short-and Long-Term Stock Market Prediction.” 2018 9th International Conference on Information and Communication Systems (ICICS), 2018, https://doi.org/10.1109/iacs.2018.8355458.Google ScholarCross Ref
- Pai, Nikhitha, and V. Ilango. “LSTM Neural Network Model with Feature Selection forFinancial Time Series Prediction.” 2020 Fourth International Conference on I-SMAC(IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2020,https://doi.org/10.1109/i-smac49090.2020.9243376.Google ScholarCross Ref
Index Terms
- Design Stock Market Trading Strategy with Deep Learning: A Bi-LSTM Based Approach
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