IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i8p1916-d1126725.html
   My bibliography  Save this article

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

Author

Listed:
  • Darko B. Vukovic

    (Graduate School of Management, Saint Petersburg State University, Volkhovskiy Pereulok 3, 199004 Saint Petersburg, Russia
    Geographical Institute “Jovan Cvijic” SASA, Djure Jaksica 9, 11000 Belgrade, Serbia)

  • 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)

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.

Suggested Citation

  • Darko B. Vukovic & Lubov Spitsina & Ekaterina Gribanova & Vladislav Spitsin & Ivan Lyzin, 2023. "Predicting the Performance of Retail Market Firms: Regression and Machine Learning Methods," Mathematics, MDPI, vol. 11(8), pages 1-23, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1916-:d:1126725
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/8/1916/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/8/1916/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Coad, Alex & Rao, Rekha & Tamagni, Federico, 2011. "Growth processes of Italian manufacturing firms," Structural Change and Economic Dynamics, Elsevier, vol. 22(1), pages 54-70, February.
    2. Vithessonthi, Chaiporn & Tongurai, Jittima, 2015. "The effect of firm size on the leverage–performance relationship during the financial crisis of 2007–2009," Journal of Multinational Financial Management, Elsevier, vol. 29(C), pages 1-29.
    3. Wawrzyniak, Jakub & Drozdowski, Maciej & Sanlaville, Éric, 2020. "Selecting algorithms for large berth allocation problems," European Journal of Operational Research, Elsevier, vol. 283(3), pages 844-862.
    4. Plaza, Beatriz, 2011. "Google Analytics for measuring website performance," Tourism Management, Elsevier, vol. 32(3), pages 477-481.
    5. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    6. Christoffersen, Peter & Jacobs, Kris, 2004. "The importance of the loss function in option valuation," Journal of Financial Economics, Elsevier, vol. 72(2), pages 291-318, May.
    7. Flores, Benito E, 1986. "A pragmatic view of accuracy measurement in forecasting," Omega, Elsevier, vol. 14(2), pages 93-98.
    8. Jahn, Malte, 2020. "Artificial neural network regression models in a panel setting: Predicting economic growth," Economic Modelling, Elsevier, vol. 91(C), pages 148-154.
    9. Alex Coad, 2010. "Exploring the processes of firm growth: evidence from a vector auto-regression," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 19(6), pages 1677-1703, December.
    10. Athanasios Anagnostis & Elpiniki Papageorgiou & Dionysis Bochtis, 2020. "Application of Artificial Neural Networks for Natural Gas Consumption Forecasting," Sustainability, MDPI, vol. 12(16), pages 1-29, August.
    11. Dolega, Les & Rowe, Francisco & Branagan, Emma, 2021. "Going digital? The impact of social media marketing on retail website traffic, orders and sales," Journal of Retailing and Consumer Services, Elsevier, vol. 60(C).
    12. Shujian Liao & Jian Chen & Hao Ni, 2021. "Forex Trading Volatility Prediction using Neural Network Models," Papers 2112.01166, arXiv.org, revised Dec 2021.
    13. Shahzad Zaheer & Nadeem Anjum & Saddam Hussain & Abeer D. Algarni & Jawaid Iqbal & Sami Bourouis & Syed Sajid Ullah, 2023. "A Multi Parameter Forecasting for Stock Time Series Data Using LSTM and Deep Learning Model," Mathematics, MDPI, vol. 11(3), pages 1-24, January.
    14. Liang, Deron & Tsai, Chih-Fong & Lu, Hung-Yuan (Richard) & Chang, Li-Shin, 2020. "Combining corporate governance indicators with stacking ensembles for financial distress prediction," Journal of Business Research, Elsevier, vol. 120(C), pages 137-146.
    15. Ibhagui, Oyakhilome W. & Olokoyo, Felicia O., 2018. "Leverage and firm performance: New evidence on the role of firm size," The North American Journal of Economics and Finance, Elsevier, vol. 45(C), pages 57-82.
    16. Coad, Alex, 2007. "Testing the principle of `growth of the fitter': The relationship between profits and firm growth," Structural Change and Economic Dynamics, Elsevier, vol. 18(3), pages 370-386, September.
    17. Anokhin, Sergey Alexander & Spitsin, Vladislav & Akerman, Elena & Morgan, Todd, 2021. "Technological leadership and firm performance in Russian industries during crisis," Journal of Business Venturing Insights, Elsevier, vol. 15(C).
    18. Minhas Akbar & Ahsan Akbar & Muhammad Umar Draz, 2021. "Global Financial Crisis, Working Capital Management, and Firm Performance: Evidence From an Islamic Market Index," SAGE Open, , vol. 11(2), pages 21582440211, May.
    19. Juan Federico & Joan-Lluis Capelleras, 2015. "The heterogeneous dynamics between growth and profits: the case of young firms," Small Business Economics, Springer, vol. 44(2), pages 231-253, February.
    20. Vladislav Spitsin & Darko Vukovic & Sergey Anokhin & Lubov Spitsina, 2020. "Company performance and optimal capital structure: evidence of transition economy (Russia)," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 48(2), pages 313-332, May.
    21. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    22. Poonawala, Sakina H. & Nagar, Neerav, 2019. "Gross profit manipulation through classification shifting," Journal of Business Research, Elsevier, vol. 94(C), pages 81-88.
    23. Munjal, Surender & Requejo, Ignacio & Kundu, Sumit K., 2019. "Offshore outsourcing and firm performance: Moderating effects of size, growth and slack resources," Journal of Business Research, Elsevier, vol. 103(C), pages 484-494.
    24. Joonhyuck Lee & Dongsik Jang & Sangsung Park, 2017. "Deep Learning-Based Corporate Performance Prediction Model Considering Technical Capability," Sustainability, MDPI, vol. 9(6), pages 1-12, May.
    25. Kourentzes, Nikolaos, 2014. "On intermittent demand model optimisation and selection," International Journal of Production Economics, Elsevier, vol. 156(C), pages 180-190.
    26. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    27. Lee, Sanghoon, 2014. "The relationship between growth and profit: evidence from firm-level panel data," Structural Change and Economic Dynamics, Elsevier, vol. 28(C), pages 1-11.
    28. Dan Lovallo & Alexander L. Brown & David J. Teece & David Bardolet, 2020. "Resource re‐allocation capabilities in internal capital markets: The value of overcoming inertia," Strategic Management Journal, Wiley Blackwell, vol. 41(8), pages 1365-1380, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Giovanni Dosi & Emanuele Pugliese & Pietro Santoleri, 2017. "Growth and survival of the `fitter'? Evidence from US new-born firms," LEM Papers Series 2017/06, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    2. Arranz Sombría, M. Rosa & Miralles-Quirós, José Luis & Daza-Izquierdo, Julio, 2017. "Interrelación entre el crecimiento y la rentabilidad de la banca comercial en España/Interrelation between Growth and Profitability in Spanish Commercial Banks," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 35, pages 611-628, Agosto.
    3. Lee, Sanghoon, 2014. "The relationship between growth and profit: evidence from firm-level panel data," Structural Change and Economic Dynamics, Elsevier, vol. 28(C), pages 1-11.
    4. Juan Federico & Joan-Lluis Capelleras, 2015. "The heterogeneous dynamics between growth and profits: the case of young firms," Small Business Economics, Springer, vol. 44(2), pages 231-253, February.
    5. Alex Coad & Marc Cowling & Josh Siepel, 2017. "Growth processes of high-growth firms as a four-dimensional chicken and egg," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 26(4), pages 537-554.
    6. Pascal Nguyen & Tarek Miloud & Ruoyun Zhao, 2017. "CEO tenure and firm growth: A conditional analysis," Economics Bulletin, AccessEcon, vol. 37(4), pages 2301-2308.
    7. Seungkyu Yoo & Jaejun Kim, 2015. "The Dynamic Relationship between Growth and Profitability under Long-Term Recession: The Case of Korean Construction Companies," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
    8. Alaa Adden A. Abuhommous, 2017. "Net Working Capital and Firm Growth," International Review of Management and Marketing, Econjournals, vol. 7(4), pages 131-137.
    9. Hou, Lei & Elsworth, Derek & Zhang, Fengshou & Wang, Zhiyuan & Zhang, Jianbo, 2023. "Evaluation of proppant injection based on a data-driven approach integrating numerical and ensemble learning models," Energy, Elsevier, vol. 264(C).
    10. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    11. Patrick Krennmair & Timo Schmid, 2022. "Flexible domain prediction using mixed effects random forests," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(5), pages 1865-1894, November.
    12. Jie Shi & Arno P. J. M. Siebes & Siamak Mehrkanoon, 2023. "TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start," Papers 2311.18749, arXiv.org.
    13. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    14. Coad, Alex & Segarra, Agustí & Teruel, Mercedes, 2013. "Like milk or wine: Does firm performance improve with age?," Structural Change and Economic Dynamics, Elsevier, vol. 24(C), pages 173-189.
    15. Bourdouxhe, Axel & Wibail, Lionel & Claessens, Hugues & Dufrêne, Marc, 2023. "Modeling potential natural vegetation: A new light on an old concept to guide nature conservation in fragmented and degraded landscapes," Ecological Modelling, Elsevier, vol. 481(C).
    16. Manuel J. García Rodríguez & Vicente Rodríguez Montequín & Francisco Ortega Fernández & Joaquín M. Villanueva Balsera, 2019. "Public Procurement Announcements in Spain: Regulations, Data Analysis, and Award Price Estimator Using Machine Learning," Complexity, Hindawi, vol. 2019, pages 1-20, November.
    17. Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023. "Targeting predictors in random forest regression," International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
    18. Yiyi Huo & Yingying Fan & Fang Han, 2023. "On the adaptation of causal forests to manifold data," Papers 2311.16486, arXiv.org, revised Dec 2023.
    19. Akshita Bassi & Aditya Manchanda & Rajwinder Singh & Mahesh Patel, 2023. "A comparative study of machine learning algorithms for the prediction of compressive strength of rice husk ash-based concrete," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 209-238, August.
    20. Nuri YILDIRIM, 2011. "No Appealing Future For High Growth – Low Profitability Firms: Evidence from Turkey’s Top 1000," Iktisat Isletme ve Finans, Bilgesel Yayincilik, vol. 26(307), pages 31-45.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1916-:d:1126725. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.