Revisiting Feature Scaling in Linear Regression: An Empirical Study on Microsoft Stock Price Prediction
DOI:
https://doi.org/10.35870/ijsecs.v6i1.6873Keywords:
Linear Regression, Feature Scaling, Stock Price PredictionAbstract
Stock price prediction occupies a central position in quantitative finance, bearing directly on risk management, portfolio construction, and investment decision-making. This study evaluated the effect of feature scaling on linear regression performance in predicting Microsoft (MSFT) stock prices. A quantitative experimental design was employed, drawing on historical MSFT stock data spanning 2014 to 2024. Preprocessing involved data cleaning, outlier treatment via the Interquartile Range (IQR) method, and feature standardization through Z-score normalization. Two experimental conditions were tested: linear regression without feature scaling and linear regression with feature scaling. Model performance was assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). Both conditions produced nearly identical results — R² approaching 0.99, with negligible divergence across all error metrics. The evidence suggests that feature scaling does not meaningfully alter the predictive behavior of linear regression. For simple linear models operating without regularization, scaling appears to be an unnecessary preprocessing step, a finding that warrants more deliberate evaluation of preprocessing decisions in machine learning pipelines.
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Aburto, L., Romero-Romero, R., Linfati, R., & Escobar, J. W. (2023). An approach for a multi-period portfolio selection problem by considering transaction costs and prediction on the stock market. Complexity, 2023, 1–15. https://doi.org/10.1155/2023/3056411
Ahmed, W. S., Mehmood, A., Sheikh, T., & Bachaya, A. (2022). Unveiling the linkages between emerging stock market indices and cryptocurrencies. Asian Academy of Management Journal, 27(2). https://doi.org/10.21315/aamj2022.27.2.9
Alka, T. A., Raman, R., & Suresh, M. (2025). Critical success factors for successful technology innovation development in sustainable energy enterprises. Scientific Reports, 15(1), Article 14138. https://doi.org/10.1038/s41598-025-98725-2
Azman, S., Pathmanathan, D., & Thavaneswaran, A. (2022). Forecasting the volatility of cryptocurrencies in the presence of COVID-19 with the state space model and Kalman filter. Mathematics, 10(17), Article 3190. https://doi.org/10.3390/math10173190
Bagheri, F., Recupero, D. R., & Sirnes, E. (2023). Leveraging return prediction approaches for improved value-at-risk estimation. Data, 8(8), Article 133. https://doi.org/10.3390/data8080133
Bang, C. G. (2024). Data-driven decision-making for business. Routledge. https://doi.org/10.4324/9781003457787
Bassam, A. M., Phillips, A. B., Turnock, S. R., & Wilson, P. A. (2022). Ship speed prediction based on machine learning for efficient shipping operation. Ocean Engineering, 245, Article 110449. https://doi.org/10.1016/j.oceaneng.2021.110449
Bian, L., Qin, X., Zhang, C., Guo, P., & Wu, H. (2023). Application, interpretability and prediction of machine learning method combined with LSTM and LightGBM: A case study for runoff simulation in an arid area. Journal of Hydrology, 625, Article 130091. https://doi.org/10.1016/j.jhydrol.2023.130091
Cândido, J., Haesen, J., Aniche, M., & van Deursen, A. (2021). An exploratory study of log placement recommendation in an enterprise system. Proceedings of the 18th International Conference on Mining Software Repositories (MSR), 143–154. https://doi.org/10.1109/msr52588.2021.00027
Chang, V., Xu, Q. A., Chidozie, A., & Wang, H. (2024). Predicting economic trends and stock market prices with deep learning and advanced machine learning techniques. Electronics, 13(17). https://doi.org/10.3390/electronics13173396
Cristescu, M. P., Mara, D. A., Nerișanu, R. A., Culda, L. C., & Maniu, I. (2023). Analyzing the impact of financial news sentiments on stock prices: A wavelet correlation. Mathematics, 11(23). https://doi.org/10.3390/math11234830
Dani, Y., Belouaggadia, N., & Jammoukh, M. (2025). Predicting CO2 emissions in Morocco: Exploring the use of ridge regression with data preprocessing and feature impact analysis. Environmental Science and Pollution Research, 32(45), 25618–25642. https://doi.org/10.1007/s11356-025-37156-y
DeMiguel, V., Gil-Bazo, J., Nogales, F. J., & Santos, A. A. P. (2021). Can machine learning help to select portfolios of mutual funds? SSRN. https://doi.org/10.2139/ssrn.3768753
Fang, F., Ventre, C., Basios, M., Kanthan, L., Martínez-Rego, D., Wu, F., & Li, L. (2022). Cryptocurrency trading: A comprehensive survey. Financial Innovation, 8(1). https://doi.org/10.1186/s40854-021-00321-6
Goel, H., & Som, B. K. (2023). Stock market prediction, COVID-19 pandemic and neural networks: An SCG algorithm application. Economia, 24(1), 134–146. https://doi.org/10.1108/econ-07-2022-0101
Gyaneshwar, A., Mishra, A., Chadha, U., Vincent, P. M. D. R., Rajinikanth, V., Ganapathy, G. P., & Srinivasan, K. (2023). A contemporary review on deep learning models for drought prediction. Sustainability, 15(7), Article 6160. https://doi.org/10.3390/su15076160
Habib, M., & Okayli, M. (2024). Evaluating the sensitivity of machine learning models to data preprocessing technique in concrete compressive strength estimation. Arabian Journal for Science and Engineering, 49(10), 13709–13727. https://doi.org/10.1007/s13369-024-08776-2
Hassan, R., & Baghban, A. (2025). Pioneering machine learning techniques to estimate thermal conductivity of carbon-based phase change materials: A comprehensive modeling framework. Case Studies in Thermal Engineering, 73, Article 106648. https://doi.org/10.1016/j.csite.2025.106648
Hollmann, N., Müller, S., Purucker, L., Krishnakumar, A., Körfer, M., Hoo, S. B., Schirrmeister, R. T., & Hutter, F. (2025). Accurate predictions on small data with a tabular foundation model. Nature, 637(8045), 319–326. https://doi.org/10.1038/s41586-024-08328-6
Jiang, H., Dong, Y., & Wang, J. (2024). Electricity price forecasting using quantile regression averaging with nonconvex regularization. Journal of Forecasting, 43(6), 1859–1879. https://doi.org/10.1002/for.3103
Khatoon, U. T., & Velidandi, A. (2025). An overview on the role of government initiatives in nanotechnology innovation for sustainable economic development and research progress. Sustainability, 17(3). https://doi.org/10.3390/su17031250
Khosravi, B., Weston, A. D., Nugen, F., Mickley, J. P., Maradit Kremers, H., Wyles, C. C., Carter, R. E., & Taunton, M. J. (2023). Demystifying statistics and machine learning in analysis of structured tabular data. The Journal of Arthroplasty, 38(10), 1943–1947. https://doi.org/10.1016/j.arth.2023.08.045
Ko, K., Al Doulat, A., & Ku, S. (2025). Ridge, LASSO, and elastic net regression: Fathers' psychological well-being in familial contexts. Journal of Social and Personal Relationships. Advance online publication. https://doi.org/10.1177/02654075251412474
Li, Q. (2021). The use of artificial intelligence combined with cloud computing in the design of education information management platform. International Journal of Emerging Technologies in Learning (iJET), 16(5), 32. https://doi.org/10.3991/ijet.v16i05.20309
Moro-Visconti, R., Rambaud, S. C., & Pascual, J. L. (2020). Sustainability in FinTechs: An explanation through business model scalability and market valuation. Sustainability, 12(24). https://doi.org/10.3390/su122410316
Munkhdalai, L., Munkhdalai, T., Pham, V.-H., Hong, J., Ryu, K. H., & Theera-Umpon, N. (2022). Neural network-augmented locally adaptive linear regression model for tabular data. Sustainability, 14(22), Article 15273. https://doi.org/10.3390/su142215273
Nagaraju, T. V., Mantena, S., Azab, M., Alisha, S. S., El Hachem, C., Adamu, M., & Rama Murthy, P. S. (2023). Prediction of high strength ternary blended concrete containing different silica proportions using machine learning approaches. Results in Engineering, 17, Article 100973. https://doi.org/10.1016/j.rineng.2023.100973
Panovski, D., & Zaharia, T. (2020). Long and short-term bus arrival time prediction with traffic density matrix. IEEE Access, 8, 226267–226284. https://doi.org/10.1109/access.2020.3044173
Sakib, M., Mustajab, S., & Alam, M. (2024). Ensemble deep learning techniques for time series analysis: A comprehensive review, applications, open issues, challenges, and future directions. Cluster Computing, 28(1), 73. https://doi.org/10.1007/s10586-024-04684-0
Shah, K. N. (2025). Data-driven automation and AI/ML: Revolutionizing financial decision-making. In AI and automation in financial services (pp. 249–271). Springer. https://doi.org/10.1007/978-3-031-92916-8_13
Sheng, Y., & Ma, D. (2022). Stock index spot–futures arbitrage prediction using machine learning models. Entropy, 24(10), Article 1462. https://doi.org/10.3390/e24101462
Shi, X., Jiang, D., Qian, W., & Liang, Y. (2022). Application of the Gaussian process regression method based on a combined kernel function in engine performance prediction. ACS Omega, 7(45), 41732–41743. https://doi.org/10.1021/acsomega.2c05952
Shome, A., Mukherjee, G., Chatterjee, A., & Tudu, B. (2024). Study of different regression methods, models and application in deep learning paradigm. In Deep learning concepts in operations research (pp. 130–152). CRC Press. https://doi.org/10.1201/9781003433309-13
Sperber, C., Gallucci, L., Mirman, D., Arnold, M., & Umarova, R. M. (2023). Stroke lesion size: Still a useful biomarker for stroke severity and outcome in times of high-dimensional models. NeuroImage: Clinical, 40, Article 103511. https://doi.org/10.1016/j.nicl.2023.103511
Tian, Y., Lu, Z., Adriaens, P., Minchin, R. E., Caithness, A., & Woo, J. (2020). Finance infrastructure through blockchain-based tokenization. Frontiers of Engineering Management, 7(4), 485–499. https://doi.org/10.1007/s42524-020-0140-2
Tiwari, A. K., Abdullah, M., Sarker, P. K., & Abakah, E. J. A. (2025). Real-world asset tokens and commodities: Static and dynamic linkages. China Accounting and Finance Review, 27(5), 759–788. https://doi.org/10.1108/CAFR-05-2024-0054
Tjøstheim, D. (2025). Selected topics in time series forecasting: Statistical models vs. machine learning. Entropy, 27(3), Article 279. https://doi.org/10.3390/e27030279
Yang, N., & Zhou, W. (2024). Feature selection for explaining yellowfin tuna catch per unit effort using least absolute shrinkage and selection operator regression. Fishes, 9(6), Article 204. https://doi.org/10.3390/fishes9060204
Zhang, L., & Hua, L. (2025). Major issues in high-frequency financial data analysis: A survey of solutions. Mathematics, 13(3), Article 347. https://doi.org/10.3390/math13030347
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