The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
The use of data mining and neural networks for forecasting stock market returns
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Citations
Cites
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Fama, E. F. (1970) · The Journal of Finance · doi:10.2307/2325486 · Cited by 13,635
On Estimation of a Probability Density Function and Mode
Parzen, E. (1962) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177704472 · Cited by 10,585
A general regression neural network
Specht, D. F. (1991) · IEEE Transactions on Neural Networks · doi:10.1109/72.97934 · Cited by 4,492
Data Mining: Concepts and Techniques
Thamaraiselvi, G., & Kaliammal, A. (2014) · Journal of Information and Knowledge · Cited by 4,088
Business conditions and expected returns on stocks and bonds
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Stock Market Prices Do Not Follow Random Walks: Evidence from a Simple Specification Test
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Cited by
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
Artificial neural networks in business: Two decades of research
Tkáč, M., & Verner, R. (2016) · Applied Soft Computing · doi:10.1016/j.asoc.2015.09.040 · Cited by 340
Artificial Intelligence Applied to Stock Market Trading: A Review
Ferreira, F. G. D. C., Gandomi, A. H., & Cardoso, R. T. N. (2021) · IEEE Access · doi:10.1109/access.2021.3058133 · Cited by 187
Automated trading with performance weighted random forests and seasonality
Booth, A., Gerding, E., & McGroarty, F. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.12.009 · Cited by 154
Stock selection with random forest: An exploitation of excess return in the Chinese stock market
Tan, Z., Yan, Z., & Zhu, G. (2019) · Heliyon · doi:10.1016/j.heliyon.2019.e02310 · Cited by 107
“In contrast to the academic study where there is greater emphasis on the predictions of stock returns (Enke and Thawornwong, 2005; Khashei and Bijari, 2010; Hassan et al., 2007; Guresen et al., 2011; Jia et al., 2012; Ticknor, 2013; Babu and Reddy, 2014; Rather et al., 2015), investment managers…”
MethodResult
Metadata
- Authors
- Enke, D., & Thawornwong, S.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2005-11
- Volume
- 29
- Issue
- 4
- Pages
- 927-940
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2005346797
- Cited by
- 465
- References
- 45
- FWCI
- 14.65
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Enke, D., & Thawornwong, S. (2005). The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2005.06.024
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
