The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Deep learning for financial applications : A survey
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Approximation by superpositions of a sigmoidal function
Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883
Machine Learning: An Applied Econometric Approach
Mullainathan, S., & Spiess, J. (2017) · Journal of Economic Perspectives · doi:10.1257/jep.31.2.87 · Cited by 1,989
Deep Direct Reinforcement Learning for Financial Signal Representation and Trading
Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2522401 · Cited by 822
Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500
Krauss, C., Do, X. A., & Huck, N. (2017) · European Journal of Operational Research · doi:10.1016/j.ejor.2016.10.031 · Cited by 750 · cited 1×
“[50] Stocks in the S&P500 1990-2015 Price data DNN, GBT, RF Mean return, MDD, Calmar ratio H2O”
MethodBig Data: Deep Learning for financial sentiment analysis
Sohangir, S., Wang, D., Pomeranets, A., & Khoshgoftaar, T. M. (2018) · Journal of Big Data · doi:10.1186/s40537-017-0111-6 · Cited by 390
Cryptocurrency Portfolio Management with Deep Reinforcement Learning
Jiang, Z., & Liang, J. (2016) · arXiv · doi:10.48550/arxiv.1612.01277 · Cited by 298
Cited by
A novel graph convolutional feature based convolutional neural network for stock trend prediction
Chen, W., Jiang, M., Zhang, W.-G., & Chen, Z. (2021) · Information Sciences · doi:10.1016/j.ins.2020.12.068 · Cited by 281
Data science and AI in FinTech: an overview
Cao, L., Yang, Q., & Yu, P. S. (2021) · International Journal of Data Science and Analytics · doi:10.1007/s41060-021-00278-w · Cited by 238
A Missing Piece in the Puzzle: Considering the Role of Task Complexity in Human-AI Decision Making
Salimzadeh, S., He, G., & Gadiraju, U. (2023) · Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization · doi:10.1145/3565472.3592959 · Cited by 42
A comprehensive survey and taxonomy on privacy-preserving deep learning
Tran, A.-T., Luong, T.-D., & Huynh, V.-N. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127345 · Cited by 21
Metadata
- Authors
- Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B.
- Venue
- Applied Soft Computing
- Publisher
- Elsevier BV
- Published
- 2020-08
- Volume
- 93
- Pages
- 106384
- ISSN
- 1568-4946 (ISSN-L)
- OpenAlex
- W3005892455
- Cited by
- 545
- References
- 9
- FWCI
- 39.20
- Type
- journal article
- Language
- en
- Open access
- green
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- Ozbayoglu, A. M., Gudelek, M. U., & Sezer, O. B. (2020). Deep learning for financial applications : A survey. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2020.106384
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
