Predictive Modeling for Financial Market Trends Using Statistical Methods

Authors

  • Sushil Bhattarai, Krishna Bahadur Thapa1b, Kripa Sindhu Prasad, Arun Kumar Chaudhary, Puspa Raj Ojha, Suresh Kumar Sahani, Garima Sharma

Keywords:

Predictive Modeling, Financial Markets, Statistical Methods, Time Series Analysis, Regression Models, ARIMA, Machine Learning.

Abstract

A key instrument for comprehending and predicting the developments of the financial markets is predictive modelling. Time series analysis, regression models, and machine learning techniques are the main topics of this study, which looks at how statistical methods are applied to forecast financial market movements. The application of these techniques in practice is illustrated using a case study
that makes use of historical stock market data. Furthermore, a statistical modelling examination of consumer buying trends highlights how important it is to comprehend consumer behavior in financial markets

References

• Box, G. E., & Jenkins, G. M. (1976). Time series analysis: Forecasting and control. Holden Day.

• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. https://doi.org/10.1023/A:1010933404324

• Chen, K., Zhou, Y., & Dai, F. (2012). A deep learning framework for financial time series using stacked autoencoders and long-short term memory. PLoS ONE, 12(7), e0180944. https://doi.org/10.1371/journal.pone.0180944

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Published

2025-04-10

How to Cite

Sushil Bhattarai, Krishna Bahadur Thapa1b, Kripa Sindhu Prasad, Arun Kumar Chaudhary, Puspa Raj Ojha, Suresh Kumar Sahani, Garima Sharma. (2025). Predictive Modeling for Financial Market Trends Using Statistical Methods . Journal of Computational Analysis and Applications (JoCAAA), 34(3), 117–127. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2233

Issue

Vol. 34 No. 3 (2025): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.