Evaluation of the Multiple Regression Analysis Algorithm on Stock Market Prediction

Article Sidebar

pdf (Bahasa Indonesia)
Published: Oct 14, 2024
DOI: https://doi.org/10.62885/improsci.v2i2.494
Keywords:
saham, algoritma, multiple, regresi, analisis

Main Article Content

Mohamat Setiawan
Politeknik Lembaga Pendidikan dan Pelatihan Profesi Indonesia
Ade Johar Maturidi
a:1:{s:5:"id_ID";s:61:"Politeknik Lembaga Pendidikan dan Pelatihan Profesi Indonesia";}
Dian Novianti
University of Muhammadiyah Cirebon

Abstract

Financial time series is one of the most challenging applications of modern time series forecasting. The financial time series is closely related to noise, non-stationary, and deterministic chaos. The characteristics suggest that no complete information can be obtained from the past behavior of financial markets to fully capture the dependency between future prices and that of the past. The data collection method was collected from the Stock Market Online Application "MetaTrader version 4" type "Daily" with a time range from "03/09/2001 to 25/07/2012", as many as 2052 data", with the attributes "Date, Open, High, Low, Close, Volume" with the main attribute "Close" using the Support vector machine algorithm, artificial neural network, and multiple linear regression. The conclusion of the value that is close to the series value is the value by testing on the support vector machine algorithm, with the parameter for the RMSE value that is close to the "0" value obtained from the measurement results on the SVM algorithm on the RBF kernel (radial base function) with a value of "gamma" γ = 100 with the value of RMSE = 0.000, and SE = 0.000. with prediction accuracy error = 0.976

Article Details

How to Cite
Setiawan, M., Maturidi, A. J., & Novianti, D. (2024). Evaluation of the Multiple Regression Analysis Algorithm on Stock Market Prediction. Jurnal Improsci, 2(2), 131–139. https://doi.org/10.62885/improsci.v2i2.494
Issue
Vol. 2 No. 2 (2024): Vol 2 No 2 October 2024
Section
Articles
Creative Commons License

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

References

Categories of forecasting methods. (n.d.).en.wikipedia.org/forecasting.

Chen, R. (2007). Using SVM with Financial Statement Analysis for Prediction of Stocks, 7(4), 63–72.

Collins, M., Avenue, P., Room, A., Park, F., & Schapire, R. E. (2000). Logistic Regression, AdaBoost and Bregman Distances. Proceedings of the Thirteenth Annual Conference on Computational Learning Theory, 13, 1–26.

Cuaresma, J. C. (2010). Modeling and Predicting the EUR/USD Exchange Rate : The Role of Nonlinear Adjustments to Purchasing Power Parity, 64–76.

Farrell, M. T., & Correa, A. (2007). Gaussian Process Regression Models for Predicting Stock Trends. Mit. Media Edu, 1–9.

Fitting a trend : least-squares. (n.d.).en.wikipedia.org/Trend Estimation.

Fox, J. (2010). Appendices to Applied Regression Analysis, Generalized Linear Models, and Related Methods, Second Edition.

From, F. (n.d.). Polynomial kernels, 2–4.

Hanias, M. P. (2008). Time Series Prediction of Dollar Euro Exchange Rate Index.

Hardiningsih, P. (2001). The Influence of Fundamental Factors and Economic Risk on the Return of Company Shares on the Jakarta Stock Exchange. Diponegoro University. http://europa.eu/legislation_summaries/economic_and_monetary_affairs/introducing_ euro_practical_aspects/l25007_en.htm. International Research Journal of Finance and Economics, 15(15), 232–239.

Linear prediction. (n.d.).en.wikipedia.org/linear prediction.

Multinomial, P., Multilevel, P., Semiparametric, N., Quantile, R., Principal, I., & Errorsvariables, L. S. (n.d.). Linear regression. en.wikipedia.org/Linear regression Regression.

Suparti, A. M. and A. R. (2007). Wavelet regression estimation thresholding with the bootstrap method. Vol, Journal of Mathematics, 10 (2), 43–50.