Risk Management With S&P 500 Stock Price Regression Forecast
Modeling the Apple Stock with Stepwise and Multiple Regression
DOI:
https://doi.org/10.47611/jsrhs.v13i2.6465Keywords:
Price Forecasting, Regression Modeling, Stock Market, Multiple Regression, Stepwise Regression, Risk ManagementAbstract
For short-term stock market traders who trade with large amounts of capital within a short time frame, having the analytical tool to predict stock price is crucial for the risk management and planning of entry and exit. While retail traders rely on the visualizations of technical indicators to anticipate general price direction and trend lines to estimate areas of concentrated demand or supply, multiple regression modeling uses known quantifiable indices whose functions are based on historical prices of an equity to yield a concise price prediction. This study aims to build an accessible and accurate model for retail traders without advanced software. In order to do that, we must optimize the quality of forecast while minimizing the number of independent variables. Through multiple linear regression and forward stepwise regression, we isolated 3 key variables – the daily closing price, Nasdaq 10-day moving average, and the Dow intraday average – which, when combined again in multiple regression, would produce a model with a multiple-R of 0.9995 and a low standard error of 2.44556268.
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Yu, Minhui. "Linear regression model for stock price of Pfizer." Proceedings of the 5th International Conference on Economic Management and Green Development. Singapore: Springer Nature Singapore, 2022.
Rishi, Taran. "Stock Market Analysis Using Linear Regression." Proceedings of the Jepson Undergraduate Conference on International Economics. Vol. 4. No. 1. 2022.
Nunno, L., 2014. Stock market price prediction using linear and polynomial regression models. Computer Science Department, University of New Mexico: Albuquerque, NM, USA.
De Bondt, Werner FM, and Richard H. Thaler. "Financial decision-making in markets and firms: A behavioral perspective." Handbooks in operations research and management science 9 (1995): 385-410.
https://www.kaggle.com/code/nikhilkohli/stock-prediction-using-linear-regression-starter
Galton, Francis. "Regression towards mediocrity in hereditary stature." The Journal of the Anthropological Institute of Great Britain and Ireland 15 (1886): 246-263.
Acknowledgment:
Firstly, I would like to thank Dr. Mahshid Fardadi, professor at University of California, Los Angeles for the 12-week Data Analytics in Business Course and advice during analysis stage. Additionally, I would like to thank Mr. Rahul Deo Vishwakarma, pHD at California State University at Long Beach, for the guidance with StatTool, writing, and formatting the research paper.
The paper was copy edited by Academic Advisor at Cambridge Centre for International Research
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