Predicting Firm’s Performance Based on Panel Data: Using Hybrid Methods to Improve Forecast Accuracy

Nikita V. Martyushev (), Vladislav Spitsin, Roman V. Klyuev, Lubov Spitsina, Vladimir Yu. Konyukhov, Tatiana A. Oparina and Aleksandr E. Boltrushevich
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Nikita V. Martyushev: Department of Information Technologies, Tomsk Polytechnic University, 634050 Tomsk, Russia
Vladislav Spitsin: Business School, National Research Tomsk Polytechnic University, Lenin Avenue, 30, 634050 Tomsk, Russia
Roman V. Klyuev: Technique and Technology of Mining and Oil and Gas Production Department, Moscow Polytechnic University, 107023 Moscow, Russia
Lubov Spitsina: Business School, National Research Tomsk Polytechnic University, Lenin Avenue, 30, 634050 Tomsk, Russia
Vladimir Yu. Konyukhov: Department of Automation and Control, Irkutsk National Research Technical University, 664074 Irkutsk, Russia
Tatiana A. Oparina: Department of Automation and Control, Irkutsk National Research Technical University, 664074 Irkutsk, Russia
Aleksandr E. Boltrushevich: Department of Information Technologies, Tomsk Polytechnic University, 634050 Tomsk, Russia

Mathematics, 2025, vol. 13, issue 8, 1-33

Abstract: The problem of predicting profitability is exceptionally relevant for investors and company owners making decisions about investment and business development. The global literature contains a number of studies where researchers predict the profitability of firms using various methods, including modern machine learning. However, these works hardly take advantage of panel data. This paper takes advantage of additional capabilities offered by panel data and proposes hybrid forecasting methods based on panel data, which allow significantly improving the accuracy of predicting the profitability. Our calculations show that when predicting the profitability, investors and company owners should take into account the profitability of the previous years and the trend in its change. The work shows that this approach can be successfully applied to high-tech companies whose profitability is characterised by increased volatility. Prediction forecasting includes STL-decomposition of time series, regression with random effects and machine learning (LSTM and CatBoost), and clustering. The training sample includes 1811 companies and data for 2013–2018 (panel data, 10,866 observations). The test sample contains data for these companies for 2019. As a result, the authors propose an approach significantly improving the accuracy of predicting ROA and ROE based on the panel nature of the data. The panel data allowed using the profitability of the previous years in forecast models and applying the STL-decomposition of the profitability of the previous years into three variables (Trend, Seasonal, and Residual), considerably improving the quality of the constructed forecast models (STL-CatBoost, STL-LSTM, and STL-RE hybrid models).

Keywords: firm’s performance; profitability prediction; ROA; ROE; panel data; machine learning; CatBoost; long short-term memory (LSTM); clustering; seasonal decomposition of time series by LOESS (STL); hybrid methods (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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