IDEAS home Printed from https://ideas.repec.org/a/gam/jworld/v6y2025i1p5-d1558165.html
   My bibliography  Save this article

A Time Series Approach to Forecasting Financial Indicators in the Wholesale and Retail Trade

Author

Listed:
  • Sylvia Jenčová

    (Faculty of Management and Business, University of Prešov, 080 01 Prešov, Slovakia)

  • Petra Vašaničová

    (Faculty of Management and Business, University of Prešov, 080 01 Prešov, Slovakia)

  • Martina Košíková

    (Faculty of Management and Business, University of Prešov, 080 01 Prešov, Slovakia)

  • Marta Miškufová

    (Faculty of Management and Business, University of Prešov, 080 01 Prešov, Slovakia)

Abstract

Forecasting using historical time series data has become increasingly important in today’s world. This paper aims to assess the potential for stable positive development within the wholesale and retail trade sector (SK NACE Section G) and the operations of HORTI, Ltd.( Košice, Slovakia), a company within this industry (SK NACE 46.31—wholesale of fruit and vegetables) by predicting three financial indicators: costs, revenues, and earnings before taxes (EBT) (or earnings after taxes (EAT)). We analyze quarterly data from Q1 2009 to Q4 2023 taken from the sector and monthly data from January 2013 to December 2022 for HORTI, Ltd. Through time series analysis, we aim to identify the most suitable model for forecasting the trends in these financial indicators. The study demonstrates that simple legacy forecasting methods, such as exponential smoothing and Box–Jenkins methodology, are sufficient for accurately predicting financial indicators. These models were selected for their simplicity, interpretability, and efficiency in capturing stable trends, and seasonality, especially in sectors with relatively stable financial behavior. The results confirm that traditional Holt–Winters’ and Autoregressive Integrated Moving Average (ARIMA) models can provide reliable forecasts without the need for more complex approaches. While advanced methods, such as GARCH or machine learning, could improve predictions in volatile conditions, the traditional models offer robust, interpretable results that support managerial decision-making. The findings can help managers estimate the financial health of the company and assess risks such as bankruptcy or insolvency, while also acknowledging the limitations of these models in predicting large shifts due to external factors or market disruptions.

Suggested Citation

  • Sylvia Jenčová & Petra Vašaničová & Martina Košíková & Marta Miškufová, 2025. "A Time Series Approach to Forecasting Financial Indicators in the Wholesale and Retail Trade," World, MDPI, vol. 6(1), pages 1-40, January.
    • Handle: RePEc:gam:jworld:v:6:y:2025:i:1:p:5-:d:1558165
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2673-4060/6/1/5/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2673-4060/6/1/5/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    2. Taylor, James W., 2003. "Exponential smoothing with a damped multiplicative trend," International Journal of Forecasting, Elsevier, vol. 19(4), pages 715-725.
    3. DRITSAKIS, Nikolaos & KLAZOGLOU, Paraskevi, 2019. "Time Series Analysis using ARIMA Models: An Approach to Forecasting Health Expenditure in USA," Economia Internazionale / International Economics, Camera di Commercio Industria Artigianato Agricoltura di Genova, vol. 72(1), pages 77-106.
    4. Ivan D. Trofimov, 2024. "A Time Series Analysis of Corporate Profit Rates in Selected Developed Economies: Asymmetries, Non-linearity and Mean Reversion," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 22(2), pages 303-338, June.
    5. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    6. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    7. Ngo Thai Hung, 2022. "Spillover Effects Between Stock Prices and Exchange Rates for the Central and Eastern European Countries," Global Business Review, International Management Institute, vol. 23(2), pages 259-286, April.
    8. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    9. Dominika Gajdosikova & Katarina Valaskova & George Lazaroiu, 2024. "The Relevance of Sectoral Clustering in Corporate Debt Policy: The Case Study of Slovak Enterprises," Administrative Sciences, MDPI, vol. 14(2), pages 1-30, January.
    10. S. A. Roberts, 1982. "A General Class of Holt-Winters Type Forecasting Models," Management Science, INFORMS, vol. 28(7), pages 808-820, July.
    11. Paeng, Seongcheol & Senteney, Dave & Yang, Taewon, 2024. "Spillover effects, lead and lag relationships, and stable coins time series," The Quarterly Review of Economics and Finance, Elsevier, vol. 95(C), pages 45-60.
    12. Manuel Benazić, 2014. "Determinants of retail trade in Croatia," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 27(1), pages 607-628, January.
    13. Aslina Nasir & Yeny Nadira Kamaruzzaman, 2024. "Does future tuna landing stock meet the target? Forecasting tuna landing in Malaysia using seasonal ARIMA model," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 51(11), pages 1434-1449, March.
    14. C. Di Berardino & D. D’Ingiullo & A. Sarra, 2017. "Distributive trade and regional productivity growth," The Service Industries Journal, Taylor & Francis Journals, vol. 37(13-14), pages 833-857, October.
    15. Lihki Rubio & Alejandro J. Gutiérrez-Rodríguez & Manuel G. Forero, 2021. "EBITDA Index Prediction Using Exponential Smoothing and ARIMA Model," Mathematics, MDPI, vol. 9(20), pages 1-14, October.
    16. Justo De Jorge Moreno & Oscar Rojas Carrasco, 2015. "Evolution of efficiency and its determinants in the retail sector in Spain: new evidence," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 16(1), pages 244-260, February.
    17. Simon Stevenson, 2007. "A comparison of the forecasting ability of ARIMA models," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 25(3), pages 223-240, May.
    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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. R Fildes & K Nikolopoulos & S F Crone & A A Syntetos, 2008. "Forecasting and operational research: a review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(9), pages 1150-1172, September.
    3. Jan G. De Gooijer & Rob J. Hyndman, 2005. "25 Years of IIF Time Series Forecasting: A Selective Review," Monash Econometrics and Business Statistics Working Papers 12/05, Monash University, Department of Econometrics and Business Statistics.
    4. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    5. Fischer, Thomas & Krauss, Christopher & Treichel, Alex, 2018. "Machine learning for time series forecasting - a simulation study," FAU Discussion Papers in Economics 02/2018, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    6. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    7. Siran Fang & Yunjie Wei & Shouyang Wang, 2024. "30 years of exchange rate analysis and forecasting: A bibliometric review," Journal of Economic Surveys, Wiley Blackwell, vol. 38(3), pages 973-1007, July.
    8. Kim, Jae H. & Wong, Kevin & Athanasopoulos, George & Liu, Shen, 2011. "Beyond point forecasting: Evaluation of alternative prediction intervals for tourist arrivals," International Journal of Forecasting, Elsevier, vol. 27(3), pages 887-901, July.
    9. Syed Ali Asad Rizvi & Stephen J. Roberts & Michael A. Osborne & Favour Nyikosa, 2017. "A Novel Approach to Forecasting Financial Volatility with Gaussian Process Envelopes," Papers 1705.00891, arXiv.org.
    10. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    11. Taylor, James W., 2006. "Density forecasting for the efficient balancing of the generation and consumption of electricity," International Journal of Forecasting, Elsevier, vol. 22(4), pages 707-724.
    12. Gardner, Everette Jr., 2006. "Exponential smoothing: The state of the art--Part II," International Journal of Forecasting, Elsevier, vol. 22(4), pages 637-666.
    13. Hess, Alexander & Spinler, Stefan & Winkenbach, Matthias, 2021. "Real-time demand forecasting for an urban delivery platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    14. Minot, Nicholas, 2014. "Food price volatility in sub-Saharan Africa: Has it really increased?," Food Policy, Elsevier, vol. 45(C), pages 45-56.
    15. Shively, Gerald E., 2001. "Price thresholds, price volatility, and the private costs of investment in a developing country grain market," Economic Modelling, Elsevier, vol. 18(3), pages 399-414, August.
    16. Tomanova, Lucie, 2013. "Exchange Rate Volatility and the Foreign Trade in CEEC," EY International Congress on Economics I (EYC2013), October 24-25, 2013, Ankara, Turkey 267, Ekonomik Yaklasim Association.
    17. Chang, Chia-Lin, 2015. "Modelling a latent daily Tourism Financial Conditions Index," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 113-126.
    18. Goncalves, Silvia & Kilian, Lutz, 2004. "Bootstrapping autoregressions with conditional heteroskedasticity of unknown form," Journal of Econometrics, Elsevier, vol. 123(1), pages 89-120, November.
    19. Taoufik Bouezmarni & Mohamed Doukali & Abderrahim Taamouti, 2024. "Testing Granger non-causality in expectiles," Econometric Reviews, Taylor & Francis Journals, vol. 43(1), pages 30-51, January.
    20. ?ikolaos A. Kyriazis, 2021. "Impacts of Stock Indices, Oil, and Twitter Sentiment on Major Cryptocurrencies during the COVID-19 First Wave," Bulletin of Applied Economics, Risk Market Journals, vol. 8(2), pages 133-146.

    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:jworld:v:6:y:2025:i:1:p:5-:d:1558165. 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.