Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis

Tutcu, Burçin; Kayakuş, Mehmet; Terzioğlu, Mustafa; Ünal Uyar, Güler Ferhan; Talaş, Hasan; Yetiz, Filiz

doi:10.3390/app14177459

Open AccessArticle

Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis

by

Burçin Tutcu

^1,*,

Mehmet Kayakuş

²

,

Mustafa Terzioğlu

¹

,

Güler Ferhan Ünal Uyar

³

,

Hasan Talaş

^1,*

and

Filiz Yetiz

⁴

¹

Accounting and Tax Department, Korkuteli Vocational School, Akdeniz University, Antalya 07800, Türkiye

²

Department of Management Information Systems, Faculty of Social and Human Sciences, Akdeniz University, Antalya 07800, Türkiye

³

Department of Business Administration, Faculty of Economics and Administrative Sciences, Akdeniz University, Antalya 07058, Türkiye

⁴

Department of Finance and Banking, Faculty of Applied Sciences, Akdeniz University, Antalya 07058, Türkiye

^*

Authors to whom correspondence should be addressed.

Appl. Sci. 2024, 14(17), 7459; https://doi.org/10.3390/app14177459

Submission received: 19 July 2024 / Revised: 21 August 2024 / Accepted: 21 August 2024 / Published: 23 August 2024

(This article belongs to the Special Issue Machine Learning and Soft Computing: Current Trends and Applications)

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Abstract

IT is recognized as the engine of the digital world. The fact that this technology has multiple sub-sectors makes it the driving force of the economy. With these characteristics, the sector is becoming the center of attention of investors. Considering that investors prioritize profitability, it becomes a top priority for managers to make accurate and reliable profitability forecasts. The aim of this study is to estimate the profitability of IT sector firms traded in Borsa Istanbul using machine learning methods. In this study, the financial data of 13 technology firms listed in the Borsa Istanbul Technology index and operating between March 2000 and December 2023 were used. Return on assets (ROA) and return on equity (ROE) were estimated using machine learning methods such as neural networks, multiple linear regression and decision tree regression. The results obtained reveal that the performance of artificial neural networks (ANN) and multiple linear regression (MLR) are particularly effective.

Keywords: ROA; ROE; machine learning; financial forecasting

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MDPI and ACS Style

Tutcu, B.; Kayakuş, M.; Terzioğlu, M.; Ünal Uyar, G.F.; Talaş, H.; Yetiz, F. Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis. Appl. Sci. 2024, 14, 7459. https://doi.org/10.3390/app14177459

AMA Style

Tutcu B, Kayakuş M, Terzioğlu M, Ünal Uyar GF, Talaş H, Yetiz F. Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis. Applied Sciences. 2024; 14(17):7459. https://doi.org/10.3390/app14177459

Chicago/Turabian Style

Tutcu, Burçin, Mehmet Kayakuş, Mustafa Terzioğlu, Güler Ferhan Ünal Uyar, Hasan Talaş, and Filiz Yetiz. 2024. "Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis" Applied Sciences 14, no. 17: 7459. https://doi.org/10.3390/app14177459

APA Style

Tutcu, B., Kayakuş, M., Terzioğlu, M., Ünal Uyar, G. F., Talaş, H., & Yetiz, F. (2024). Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis. Applied Sciences, 14(17), 7459. https://doi.org/10.3390/app14177459

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis

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