The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Deep learning with long short-term memory networks for financial market predictions
Open accessAbstract
Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the constituent stocks of the S&P 500 from 1992 until 2015. With daily returns of 0.46 percent and a Sharpe ratio of 5.8 prior to transaction costs, we find LSTM networks to outperform memory-free classification methods, i.e., a random forest (RAF), a deep neural net (DNN), and a logistic regression classifier (LOG). The outperformance relative to the general market is very clear from 1992 to 2009, but as of 2010, excess returns seem to have been arbitraged away with LSTM profitability fluctuating around zero after transaction costs. We further unveil sources of profitability, thereby shedding light into the black box of artificial neural networks. Specifically, we find one common pattern among the stocks selected for trading – they exhibit high volatility and a short-term reversal return profile. Leveraging these findings, we are able to formalize a rules-based short-term reversal strategy that yields 0.23 percent prior to transaction costs. Further regression analysis unveils low exposure of the LSTM returns to common sources of systematic risk – also compared to the three benchmark models.
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A Comparison of ARIMA and LSTM in Forecasting Time Series
Siami-Namini, S., Tavakoli, N., & Siami Namin, A. (2018) · 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) · doi:10.1109/icmla.2018.00227 · Cited by 1,387
A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends
Hatcher, W. G., & Yu, W. (2018) · IEEE Access · doi:10.1109/access.2018.2830661 · Cited by 654
Kraus, M., Feuerriegel, S., & Oztekin, A. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.09.018 · Cited by 392
Short-term stock market price trend prediction using a comprehensive deep learning system
Shen, J., & Shafiq, M. O. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00333-6 · Cited by 380
Deep Reinforcement Learning for Trading
Zhang, Z., Zohren, S., & Roberts, S. (2020) · The Journal of Financial Data Science · doi:10.3905/jfds.2020.1.030 · Cited by 227
Metadata
- Authors
- Fischer, T., & Krauss, C.
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 270
- Issue
- 2
- Pages
- 654-669
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W2624385633
- Cited by
- 2,785
- References
- 10
- FWCI
- 111.72
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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