IDEAS home Printed from https://ideas.repec.org/a/ami/journl/v24y2024i2p328-360.html
   My bibliography  Save this article

Applying Forecasting Methods to Accrual-Based and Cash-Based Ratio Analysis

Author

Listed:
  • Alexey Litvinenko

    (University of Tartu, Estonia)

  • Anna Litvinenko

    (Tallinn University of Technology, Tallinn School of Business and Governance, Estonia)

  • Samuli Saarinen

    (Estonian Business School, Estonia)

Abstract

Research Questions- Which of the forecasting methods (SMA, ARIMA, ES) is the most informative? Can forecasting methods be used to verify each other's results? How do the manipulations in historical data affect the forecasting of accrual and cash ratios? Motivation- addressing the challenge of analytical precision in financial forecasting, the research proposes and empirically investigates the financial forecasting approach based on integrated cash-based and accrual-based ratio analysis in the dimensions of solvency, liquidity, efficiency and profitability. Idea- The effectiveness of the forecasting methods based on ratio analysis is evaluated by determining the most informative approach while examining how data manipulations influence forecasting outcomes. Data- Historical panel data for seven years (2015-2022) from financial statements of two production companies listed on the Baltic Stock Exchange was taken as a base for equally-weighted ratio calculations: solvency, liquidity, efficiency and profitability. Based on the ratio results, the forecasting for three years was done. Tools- Quantitative forecasting methods included Simple Moving Average method implemented in Excel, and ARIMA and Exponential Smoothing done via R-Script. Findings- Exponential Smoothing is the most informative method of forecasting for three years due to its sensitivity to data fluctuations, particularly in cash-based ratios. The forecasts based on accrual data show smoother trends when a company manipulates its data in accrual-based financial statements but does not manipulate the historical cash data. Volatility or conflicting results within the accrual-based and cash-based ratio pairs reveal the actual situation. Contribution- The research contributes to knowledge and empirical research on financial forecasting by integrating accrual and cash-based ratios for enhanced precision and demonstrating superior capabilities of Exponential Smoothing for detecting anomalies and improving credit risk analysis frameworks.

Suggested Citation

  • Alexey Litvinenko & Anna Litvinenko & Samuli Saarinen, 2025. "Applying Forecasting Methods to Accrual-Based and Cash-Based Ratio Analysis," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 24(2), pages 328-360, June.
    • Handle: RePEc:ami:journl:v:24:y:2024:i:2:p:328-360
    as

    Download full text from publisher

    File URL: http://online-cig.ase.ro/RePEc/ami/articles/24_2_6.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fromm, Gary & Klein, Lawrence R, 1973. "A Comparison of Eleven Econometric Models of the United States," American Economic Review, American Economic Association, vol. 63(2), pages 385-393, May.
    2. John Geweke, 1999. "Using simulation methods for bayesian econometric models: inference, development,and communication," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 1-73.
    3. Allan Timmermann, 2018. "Forecasting Methods in Finance," Annual Review of Financial Economics, Annual Reviews, vol. 10(1), pages 449-479, November.
    4. Dangerfield, Byron J. & Morris, John S., 1992. "Top-down or bottom-up: Aggregate versus disaggregate extrapolations," International Journal of Forecasting, Elsevier, vol. 8(2), pages 233-241, October.
    5. Sergey Krylov, 2018. "Target financial forecasting as an instrument to improve company financial health," Cogent Business & Management, Taylor & Francis Journals, vol. 5(1), pages 1540074-154, January.
    6. Christ, Carl F, 1975. "Judging the Performance of Econometric Models of the U.S. Economy," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 16(1), pages 54-74, February.
    7. Taylor, James W., 2004. "Volatility forecasting with smooth transition exponential smoothing," International Journal of Forecasting, Elsevier, vol. 20(2), pages 273-286.
    8. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    9. Alexey Litvinenko, 2023. "A Comparative Analysis of Altman's Z-Score and T. Jury's Cash-Based Credit Risk Models with The Application to The Production Company and The Data for The Years 2016-2022," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 22(3), pages 518-553, September.
    10. Diebold, F. X. & West, Kenneth D., 2001. "Forecasting and empirical methods in finance and macroeconomics," Journal of Econometrics, Elsevier, vol. 105(1), pages 1-3, November.
    11. Snyder, Ralph D. & Koehler, Anne B. & Ord, J. Keith, 2002. "Forecasting for inventory control with exponential smoothing," International Journal of Forecasting, Elsevier, vol. 18(1), pages 5-18.
    12. Billah, Baki & King, Maxwell L. & Snyder, Ralph D. & Koehler, Anne B., 2006. "Exponential smoothing model selection for forecasting," International Journal of Forecasting, Elsevier, vol. 22(2), pages 239-247.
    13. Andreea-Cristina PETRICĂ & Stelian STANCU & Alexandru TINDECHE, 2016. "Limitation of ARIMA models in financial and monetary economics," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(4(609), W), pages 19-42, Winter.
    14. Jorgenson, Dale W & Hunter, Jerald & Nadiri, M Ishaq, 1970. "The Predictive Performance of Econometric Models of Qtrly Investment Behavior," Econometrica, Econometric Society, vol. 38(2), pages 213-224, March.
    15. Hu, Jinshuai & Kim, Jeong-Bon, 2019. "The relative usefulness of cash flows versus accrual earnings for CEO turnover decisions across countries: The role of investor protection," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 34(C), pages 91-107.
    16. McNees, Stephen K, 1978. "Are Econometricians Useful? Folklore versus Fact," The Journal of Business, University of Chicago Press, vol. 51(4), pages 573-577, October.
    17. Yingqi Zhu & Ying Wang & Tianxue Liu & Qi Sui, 2018. "Assessing macroeconomic recovery after a natural hazard based on ARIMA—a case study of the 2008 Wenchuan earthquake in China," 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. 91(3), pages 1025-1038, April.
    18. Ball, Ray & Nikolaev, Valeri V., 2022. "On earnings and cash flows as predictors of future cash flows," Journal of Accounting and Economics, Elsevier, vol. 73(1).
    19. Noury, Benjamin & Hammami, Helmi & Ousama, A.A. & Zeitun, Rami, 2020. "The prediction of future cash flows based on operating cash flows, earnings and accruals in the French context," Journal of Behavioral and Experimental Finance, Elsevier, vol. 28(C).
    20. Montgomery, Jacob M. & Hollenbach, Florian M. & Ward, Michael D., 2012. "Improving Predictions using Ensemble Bayesian Model Averaging," Political Analysis, Cambridge University Press, vol. 20(3), pages 271-291, July.
    21. Andreea-Cristina PETRICĂ & Stelian STANCU & Alexandru TINDECHE, 2016. "Limitation of ARIMA models in financial and monetary economics," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(609), W), pages 19-42, Winter.
    22. Timmermann, Allan, 2018. "Forecasting Methods in Finance," CEPR Discussion Papers 12692, C.E.P.R. Discussion Papers.
    23. Ferbar Tratar, Liljana & Mojškerc, Blaž & Toman, Aleš, 2016. "Demand forecasting with four-parameter exponential smoothing," International Journal of Production Economics, Elsevier, vol. 181(PA), pages 162-173.
    24. Li Wang & Haofei Zou & Jia Su & Ling Li & Sohail Chaudhry, 2013. "An ARIMA‐ANN Hybrid Model for Time Series Forecasting," Systems Research and Behavioral Science, Wiley Blackwell, vol. 30(3), pages 244-259, May.
    25. Ehsan Khansalar & Mohammad Namazi, 2017. "Cash flow disaggregation and prediction of cash flow," Journal of Applied Accounting Research, Emerald Group Publishing Limited, vol. 18(4), pages 464-479, November.
    26. Benjamin Noury & Helmi Hammami & A.A. Ousama & Rami Zeitun, 2020. "The prediction of future cash flows based on operating cash flows, earnings and accruals in the French context," Post-Print hal-03163637, HAL.
    27. repec:eme:jaar00:jaar-02-2015-0011 is not listed on IDEAS
    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. Saeed, Naima & Nguyen, Su & Cullinane, Kevin & Gekara, Victor & Chhetri, Prem, 2023. "Forecasting container freight rates using the Prophet forecasting method," Transport Policy, Elsevier, vol. 133(C), pages 86-107.
    2. Shi, Qi & Li, Bin, 2022. "Further evidence on financial information and economic activity forecasts in the United States," The North American Journal of Economics and Finance, Elsevier, vol. 60(C).
    3. Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
    4. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    5. Jordi Galí & Mark Gertler, 2007. "Macroeconomic Modeling for Monetary Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 21(4), pages 25-46, Fall.
    6. Ralph C. Bryant & John F. Helliwell & Peter Hooper, 1989. "Domestic and cross-border consequences of U.S. macroeconomic policies," International Finance Discussion Papers 344, Board of Governors of the Federal Reserve System (U.S.).
    7. Rostami-Tabar, Bahman & Babai, Mohamed Zied & Ducq, Yves & Syntetos, Aris, 2015. "Non-stationary demand forecasting by cross-sectional aggregation," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 297-309.
    8. Pan, Zhiyuan & Pettenuzzo, Davide & Wang, Yudong, 2020. "Forecasting stock returns: A predictor-constrained approach," Journal of Empirical Finance, Elsevier, vol. 55(C), pages 200-217.
    9. Fildes, Robert & Petropoulos, Fotios, 2015. "Simple versus complex selection rules for forecasting many time series," Journal of Business Research, Elsevier, vol. 68(8), pages 1692-1701.
    10. Florian Huber & Gregor Kastner & Michael Pfarrhofer, 2025. "Introducing shrinkage in heavy-tailed state space models to predict equity excess returns," Empirical Economics, Springer, vol. 68(2), pages 535-553, February.
    11. Wang, Jianzhou & Zhu, Suling & Zhang, Wenyu & Lu, Haiyan, 2010. "Combined modeling for electric load forecasting with adaptive particle swarm optimization," Energy, Elsevier, vol. 35(4), pages 1671-1678.
    12. Shalini Sharma & Víctor Elvira & Emilie Chouzenoux & Angshul Majumdar, 2021. "Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting," Post-Print hal-03184841, HAL.
    13. Shi, Qi, 2023. "The RP-PCA factors and stock return predictability: An aligned approach," The North American Journal of Economics and Finance, Elsevier, vol. 64(C).
    14. Nonejad, Nima, 2021. "Predicting equity premium using news-based economic policy uncertainty: Not all uncertainty changes are equally important," International Review of Financial Analysis, Elsevier, vol. 77(C).
    15. Spithourakis, Georgios P. & Petropoulos, Fotios & Nikolopoulos, Konstantinos & Assimakopoulos, Vassilios, 2015. "Amplifying the learning effects via a Forecasting and Foresight Support System," International Journal of Forecasting, Elsevier, vol. 31(1), pages 20-32.
    16. Evangelos Spiliotis & Fotios Petropoulos & Vassilios Assimakopoulos, 2023. "On the Disagreement of Forecasting Model Selection Criteria," Forecasting, MDPI, vol. 5(2), pages 1-12, June.
    17. Han Lin Shang & Rob J Hyndman, 2016. "Grouped functional time series forecasting: An application to age-specific mortality rates," Monash Econometrics and Business Statistics Working Papers 4/16, Monash University, Department of Econometrics and Business Statistics.
    18. Petropoulos, Fotios & Wang, Xun & Disney, Stephen M., 2019. "The inventory performance of forecasting methods: Evidence from the M3 competition data," International Journal of Forecasting, Elsevier, vol. 35(1), pages 251-265.
    19. Fildes, Robert & Petropoulos, Fotios, 2013. "An evaluation of simple forecasting model selection rules," MPRA Paper 51772, University Library of Munich, Germany.
    20. Chai, Jian & Zhang, Zhong-Yu & Wang, Shou-Yang & Lai, Kin Keung & Liu, John, 2014. "Aviation fuel demand development in China," Energy Economics, Elsevier, vol. 46(C), pages 224-235.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

    Statistics

    Access and download statistics

    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:ami:journl:v:24:y:2024:i:2:p:328-360. 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: Cristina Tartavulea (email available below). General contact details of provider: .

    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.