Forecasting the Stock Price of Listed Innovative SMEs Using Machine Learning Methods Based on Bayesian optimization: Evidence from China-Reference-Cited by-同舟云学术

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Forecasting the Stock Price of Listed Innovative SMEs Using Machine Learning Methods Based on Bayesian optimization: Evidence from China

Published:2023-04-29 Issue: Volume: Page:
ISSN:0927-7099
Container-title:Computational Economics
language:en
Short-container-title:Comput Econ

Author:

Liu Wei^ORCID,Suzuki Yoshihisa,Du Shuyi

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Economics, Econometrics and Finance (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s10614-023-10393-4.pdf

Reference121 articles.

1. Abdalla, I. S., & Murinde, V. (1997). Exchange rate and stock price interactions in emerging financial markets: Evidence on India, Korea, Pakistan and the Philippines. Applied Financial Economics, 7(1), 25–35.

2. Ampomah, E. K., Qin, Z., & Nyame, G. (2020). Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement. Information, 11(6), 332.

3. Ariyo, A. A., Adewumi, A. O., & Ayo, C. K. (2014, March). Stock price prediction using the ARIMA model. In 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation (pp. 106–112). IEEE.

4. Babu, C. N., & Reddy, B. E. (2014). A moving-average filter based hybrid ARIMA–ANN model for forecasting time series data. Applied Soft Computing, 23, 27–38.

5. Basak, S., Kar, S., Saha, S., Khaidem, L., & Dey, S. R. (2019). Predicting the direction of stock market prices using tree-based classifiers. The North American Journal of Economics and Finance, 47, 552–567.

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