Application of Business Analysis in Stock Market Forecasting -Machine Learning in Stock Market Practice

Xinyi Zhang

doi:10.54691/bcpbm.v32i.2855

Authors

Xinyi Zhang

DOI:

https://doi.org/10.54691/bcpbm.v32i.2855

Keywords:

Business analysis; Stock market forecast; data processing and application; machine learning; Data visualization.

Abstract

With the development of global economic integration, the stock market occupies an important position in the global economy. Accurately predict the stock market is of important social and economic value, the stock market has huge amounts of data sources, such data features to capture the hidden rule of the stock market, and associated accurately predict proposed the new challenge, with the vigorous development of the data mining technology and the data sample is unceasingly rich, The value of data is more fully recognized and more widely concerned, and business data analysis has been gradually applied to stock market forecasting, For example: Machine learning, data mining. This paper studies the business analysis in the era of big data, so that the stock market can get higher economic benefits. For the stock market, machine learning, statistical reasoning and other methods can be used as theoretical research, but the practical application needs to be prepared and improved according to the real market environment.

Downloads

Download data is not yet available.

References

Y. Fan and M. Stevenson, “A review of supply chain risk management: definition, theory, and research agenda,” International Journal of Physical Distribution & Logs Management, vol. 48, no. 3, pp. 205–230, 2018.

L. Gao and J. Xiao, “Big data credit report in credit risk management of consumer finance,” Wireless Communications and Mobile Computing, vol. 2021, no. 4, Article ID 4811086, 7 pages, 2021.

L. BREIMAN. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.

Eunsuk Chong and Chulwoo Han and Frank C. Park. Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies[J]. Expert Systems with Applications, 2017, 83: 187-205.

A. Titan The efficient market hypothesis: Review of specialized literature and empirical research Procedia Economics and Finance, 32 (2015), pp. 442-449

B. Malkiel the efficient market hypothesis and its critics. The Journal of Economic Perspectives, 17 (1) (2003), pp. 59-82

P. D. Trung, “Research risk factors and management competence of Vietnam commercial banks from 2006-2020,” Environmental Science and Engineering: B, vol. 5, no. 8, p. 5, 2016

T.B. Trafalis, H. Ince. Support vector machine for regression and application to financial Forecasting [C], Neural Networks, 2000. IJCMM 2000, Proceedings of the Ieeeinns-enns International Joint Conferenece on IEEE, 2000.

L.J, Cao, F, Tay. Support vector machine with adaptive parameters in financial time series forecasting [J]. IEEE Transactions on Neural Networks, 2003, 14(6): 1506-1518.

J. Friedman Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics. 2001. 29(5), pp. 1189-1232

He X, Pan W, Cheng H. Research on hit rate prediction model based on ensemble learning method. Computer engineering and Science. 2019. 41(12). pp. 2278‐2284.