The best choice for predicting Intel Corporation's peak stock price: Regression Tree or Multiple Linear Regression?

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Published: Oct 17, 2023
DOI: https://doi.org/10.57166/riqchary/v5.n1.2023.117
Keywords:
Multiple Linear Regression, Regression Trees

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Edgar Eloy Carpio-Vargas
Department of Statistics and Informatics-UNA-Puno
Alicia Roxana Mayda-Huanca
School of Statistical Engineering and Informatics-UNA-Puno
German Rafael Espinoza-Rivas
Department of Engineering-UNAMBA-Peru
Ecler Mamani Vilca
Department of Computer and Systems Engineering-UNAMBA-Peru
Betsabe Milagros Ccolqque Ruiz
Department of Computer and Systems Engineering- UNAMBA

Abstract

The objective of the study was to compare the performance of the Regression Tree versus the Multiple Linear Regression Model in relation to the opening price and daily sales volume of Intel Corporation shares. A descriptive correlational non-experimental correlational research with cross-sectional design was conducted using a convenience sample. The sample consisted of 410 records collected from May 2018 to October 2019, obtained through documentary review. The results obtained showed that the Regression Tree established that the most significant variable to explain the maximum stock price was the opening price, discarding the volume variable. The Mean Squared Error obtained was $1.4480. On the other hand, the multiple linear regression model, using the outlier elimination technique, presented a Residual Standard Error of 0.2257 dollars. In conclusion, it was determined that the most adequate model to predict the maximum price of Intel Corporation shares is the Multiple Linear Regression Model with the elimination of outlier points.

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How to Cite
The best choice for predicting Intel Corporation’s peak stock price: Regression Tree or Multiple Linear Regression?. (2023). C&T Riqchary Science and Technology Research Magazine, 5(1), 49-56. https://doi.org/10.57166/riqchary/v5.n1.2023.117
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Author Biographies

Edgar Eloy Carpio-Vargas, Department of Statistics and Informatics-UNA-Puno

Edgar Eloy Carpio Vargas, Dr. in statistics and computer science, specialist in data science, professor of the Faculty of Statistical Engineering and Computer Science, RENACYT professor.

Alicia Roxana Mayda-Huanca, School of Statistical Engineering and Informatics-UNA-Puno

Alicia Roxana Mayda Huanca, Statistical and Informatics Engineer, Universidad Nacional del Altiplano.

German Rafael Espinoza-Rivas, Department of Engineering-UNAMBA-Peru

Espinoza Rivas German Rafael, specialist in geology, geotechnics and environment, with studies in Geological Engineering, Civil Engineering, and Topography in Peruvian universities, Master in Civil Engineering and Environment at the University of Utah, USA and second Master in Environmental Engineering at the Universidad Nacional del Altiplano. D. in Science, Technology and Environment.

Ecler Mamani Vilca, Department of Computer and Systems Engineering-UNAMBA-Peru

Ecler Mamani Vilca, Universidad Nacional Micaela Bastidas de Apurímac - Peru, Dr. in Computer Science, developer of multimedia applications and Intercultural Educational Software.

Betsabe Milagros Ccolqque Ruiz, Department of Computer and Systems Engineering- UNAMBA

Professor at the Universidad Nacional Micaela Bastidas de Apurímac, Computer and Systems Engineer with a Master's degree in information security and technology and PhD student.

How to Cite

The best choice for predicting Intel Corporation’s peak stock price: Regression Tree or Multiple Linear Regression?. (2023). C&T Riqchary Science and Technology Research Magazine, 5(1), 49-56. https://doi.org/10.57166/riqchary/v5.n1.2023.117
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