Predicting the Performance of Retail Market Firms: Regression and Machine Learning Methods

Darko B. Vukovic, Lubov Spitsina, Ekaterina Gribanova, Vladislav Spitsin () and Ivan Lyzin
Additional contact information
Darko B. Vukovic: Graduate School of Management, Saint Petersburg State University, Volkhovskiy Pereulok 3, 199004 Saint Petersburg, Russia
Lubov Spitsina: Division for Social Sciences and Humanities, School of Engineering Education, National Research Tomsk Polytechnic University, Lenina Avenue, 30, 634050 Tomsk, Russia
Ekaterina Gribanova: Division for Social Sciences and Humanities, School of Engineering Education, National Research Tomsk Polytechnic University, Lenina Avenue, 30, 634050 Tomsk, Russia
Vladislav Spitsin: School of Engineering Entrepreneurship, National Research Tomsk Polytechnic University, Lenina Avenue, 30, 634050 Tomsk, Russia
Ivan Lyzin: School of Information Technology and Robotics Engineering, National Research Tomsk Polytechnical University, Lenina Avenue, 30, 634050 Tomsk, Russia

Mathematics, 2023, vol. 11, issue 8, 1-23

Abstract: The problem of predicting profitability is exceptionally relevant for investors and company owners. This paper examines the factors affecting firm performance and tests and compares various methods based on linear and non-linear dependencies between variables for predicting firm performance. In this study, the methods include random effects regression, individual machine learning algorithms with optimizers (DNN, LSTM, and Random Forest), and advanced machine learning methods consisting of sets of algorithms (portfolios and ensembles). The training sample includes 551 retail-oriented companies and data for 2017–2019 (panel data, 1653 observations). The test sample contains data for these companies for 2020. This study combines two approaches (stages): an econometric analysis of the influence of factors on the company’s profitability and machine learning methods to predict the company’s profitability. To compare forecasting methods, we used parametric and non-parametric predictive measures and ANOVA. The paper shows that previous profitability has a strong positive impact on a firm’s performance. We also find a non-linear positive effect of sales growth and web traffic on firm profitability. These variables significantly improve the prediction accuracy. Regression is inferior in forecast accuracy to machine learning methods. Advanced methods (portfolios and ensembles) demonstrate better and more steady results compared with individual machine learning methods.

Keywords: firm performance; non-linear models of panel data forecasting; retail market companies; profitability prediction; random effects regression; machine learning methods; Random Forest; long short-term memory; deep neural network; portfolio algorithm; ensemble algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/8/1916/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/8/1916/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:8:p:1916-:d:1126725

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().