Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.’S Net Income and Stock Prices
This article presents a comprehensive methodology for processing financial datasets of Apple Inc., encompassing quarterly income and daily stock prices, spanning from March 31, 2009, to December 31, 2023. Leveraging 60 observations for quarterly income and 3774 observations for daily stock prices, s...
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| Format: | Article |
| Language: | English |
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2025-03-01
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| Series: | Studia Universitatis Vasile Goldis Arad, Seria Stiinte Economice |
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| Online Access: | https://doi.org/10.2478/sues-2025-0004 |
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| author | Ungar Kevin Oprean-Stan Camelia |
| author_facet | Ungar Kevin Oprean-Stan Camelia |
| author_sort | Ungar Kevin |
| collection | DOAJ |
| description | This article presents a comprehensive methodology for processing financial datasets of Apple Inc., encompassing quarterly income and daily stock prices, spanning from March 31, 2009, to December 31, 2023. Leveraging 60 observations for quarterly income and 3774 observations for daily stock prices, sourced from Macrotrends and Yahoo Finance respectively, the study outlines five distinct datasets crafted through varied preprocessing techniques. Through detailed explanations of aggregation, interpolation (linear, polynomial, and cubic spline) and lagged variables methods, the study elucidates the steps taken to transform raw data into analytically rich datasets. Subsequently, the article delves into regression analysis, aiming to decipher which of the five data processing methods best suits capital market analysis, by employing both linear and polynomial regression models on each preprocessed dataset and evaluating their performance using a range of metrics, including cross-validation score, MSE, MAE, RMSE, R-squared, and Adjusted R-squared. The research findings reveal that linear interpolation with polynomial regression emerges as the top-performing method, boasting the lowest validation MSE and MAE values, alongside the highest R-squared and Adjusted R-squared values. |
| format | Article |
| id | doaj-art-ee328e534044455fb8dad19e4592635c |
| institution | Kabale University |
| issn | 2285-3065 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Studia Universitatis Vasile Goldis Arad, Seria Stiinte Economice |
| spelling | doaj-art-ee328e534044455fb8dad19e4592635c2025-01-20T11:10:13ZengSciendoStudia Universitatis Vasile Goldis Arad, Seria Stiinte Economice2285-30652025-03-01351498210.2478/sues-2025-0004Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.’S Net Income and Stock PricesUngar Kevin0Oprean-Stan Camelia11Lucian Blaga University of Sibiu, Faculty of Economic Sciences, Sibiu, Romania2Lucian Blaga University of Sibiu, Faculty of Economic Sciences, Sibiu, RomaniaThis article presents a comprehensive methodology for processing financial datasets of Apple Inc., encompassing quarterly income and daily stock prices, spanning from March 31, 2009, to December 31, 2023. Leveraging 60 observations for quarterly income and 3774 observations for daily stock prices, sourced from Macrotrends and Yahoo Finance respectively, the study outlines five distinct datasets crafted through varied preprocessing techniques. Through detailed explanations of aggregation, interpolation (linear, polynomial, and cubic spline) and lagged variables methods, the study elucidates the steps taken to transform raw data into analytically rich datasets. Subsequently, the article delves into regression analysis, aiming to decipher which of the five data processing methods best suits capital market analysis, by employing both linear and polynomial regression models on each preprocessed dataset and evaluating their performance using a range of metrics, including cross-validation score, MSE, MAE, RMSE, R-squared, and Adjusted R-squared. The research findings reveal that linear interpolation with polynomial regression emerges as the top-performing method, boasting the lowest validation MSE and MAE values, alongside the highest R-squared and Adjusted R-squared values.https://doi.org/10.2478/sues-2025-0004linear regression analysispolynomial regressionstock pricesfinancial data processingpython programmingg14g21c45g32 |
| spellingShingle | Ungar Kevin Oprean-Stan Camelia Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.’S Net Income and Stock Prices Studia Universitatis Vasile Goldis Arad, Seria Stiinte Economice linear regression analysis polynomial regression stock prices financial data processing python programming g14 g21 c45 g32 |
| title | Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.’S Net Income and Stock Prices |
| title_full | Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.’S Net Income and Stock Prices |
| title_fullStr | Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.’S Net Income and Stock Prices |
| title_full_unstemmed | Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.’S Net Income and Stock Prices |
| title_short | Optimizing Financial Data Analysis: A Comparative Study of Preprocessing Techniques for Regression Modeling of Apple Inc.’S Net Income and Stock Prices |
| title_sort | optimizing financial data analysis a comparative study of preprocessing techniques for regression modeling of apple inc s net income and stock prices |
| topic | linear regression analysis polynomial regression stock prices financial data processing python programming g14 g21 c45 g32 |
| url | https://doi.org/10.2478/sues-2025-0004 |
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