Machine Learning in Economics and Finance
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Alexakis, C., Dowling, M., Eleftheriou, K., et al. (2020). Textual machine learning: An application to computational economics research. Computational Economics. https://doi.org/10.1007/s10614-020-10077-3.
Babaei, G., & Bambad, S. (2020). A new hybrid instance-based learning model for decision-making in the P2P lending market. Computational Economics. https://doi.org/10.1007/s10614-020-10085-3.
Bouri, E., Gkillas, K., Gupta, R., et al. (2020). Forecasting realized volatility of bitcoin: The role of the trade war. Computational Economics. https://doi.org/10.1007/s10614-020-10022-4.
Bussmann, N., Giudici, P., Marinelli, D., et al. (2020). Explainable machine learning in credit risk management. Computational Economics. https://doi.org/10.1007/s10614-020-10042-0.
Chakraborty, T., Chakraborty, A. K., Biswas, M., et al. (2020). Unemployment rate forecasting: A hybrid approach. Computational Economics. https://doi.org/10.1007/s10614-020-10040-2.
Chen, T. H., Chen, M. Y., & Du, G. T. (2020). The determinants of bitcoin’s price: Utilization of GARCH and machine learning approaches. Computational Economics. https://doi.org/10.1007/s10614-020-10057-7.
Duarte, J. J., Montenegro González, S., & Cruz, J. C. (2020). Predicting stock price falls using news data: Evidence from the Brazilian market. Computational Economics. https://doi.org/10.1007/s10614-020-10060-y.
Kline, R. R. (2011). Cybernetics, automata studies and the dartmouth conference on artificial intelligence. In IEEE Annals of the History of Computing, October–December. IEEE Computer Society.
Lee, S. C., & Lee, E. (1974). Fuzzy sets and neural networks. Journal of Cybernetics, 4(2), 83–103. https://doi.org/10.1080/01969727408546068.
Lima, L. R., Godeiro, L. L., & Mohsin, M. (2020). Time-varying dictionary and the predictive power of FED minutes. Computational Economics. https://doi.org/10.1007/s10614-020-10039-9.
Lussange, J., Lazarevich, I., Bourgeois-Gironde, S., et al. (2020). Modelling stock markets by multi-agent reinforcement learning. Computational Economics. https://doi.org/10.1007/s10614-020-10038-w.
Mitchell, T. (1997). Machine Learning (p. 2). New York: McGraw Hill. ISBN 978-0-07-042807-2
Pesantez-Narvaez, J., Guillen, M., & Alcañiz, M. (2020). A synthetic penalized logitboost to model mortgage lending with imbalanced data. Computational Economics. https://doi.org/10.1007/s10614-020-10059-5.
Plakandaras, V., Gogas, P., & Papadimitriou, T. (2020). Gold against the machine. Computational Economics. https://doi.org/10.1007/s10614-020-10019-z.
Soybilgen, B., & Yazgan, E. (2020). Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors. Computational Economics. https://doi.org/10.1007/s10614-020-10083-5.
Syriopoulos, T., Tsatsaronis, M., & Karamanos, I. (2020). Support vector machine algorithms: An application to ship price forecasting. Computational Economics. https://doi.org/10.1007/s10614-020-10032-2.
Triepels, R., Daniels, H., & Berndsen, R. (2020). Monitoring liquidity management of banks with recurrent neural networks. Computational Economics. https://doi.org/10.1007/s10614-020-10067-5.
Tsagris, M. (2020). A new scalable Bayesian network learning algorithm with applications to economics. Computational Economics. https://doi.org/10.1007/s10614-020-10065-7.
Wang, H., Li, C., Gu, B., & Min, W. (1984). “Does AI-based credit scoring improve financial inclusion? Evidence from online payday lending”. In 40th international conference on information systems. ICIS 2019.
White, H. (1988). Economic prediction using neural networks: The case of IBM daily stock returns. In IEEE 1988 international conference on neural networks, San Diego, CA, USA (vol. 2, pp. 451–458). https://doi.org/10.1109/ICNN.1988.23959.
Yilmaz, F. M., & Arabaci, O. (2020). Should deep learning models be in high demand, or should they simply be a very hot topic? A comprehensive study for exchange rate forecasting. Computational Economics. https://doi.org/10.1007/s10614-020-10047-9.