IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2022i1p211-d1021419.html
   My bibliography  Save this article

Dynamic Model of Enterprise Revenue Management Based on the SFA Model

Author

Listed:
  • Aliya Alimhanova

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Andrey Vazhdaev

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Artur Mitsel

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia
    Department of Experimental Physics, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Anatoly Sidorov

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

Abstract

The actual problem of enterprise revenue management that requires an effective solution is considered. Revenue is the main source of cash proceeds specifically from the main enterprise activities, as well as one of the main factors affecting enterprise functioning. As a result, the amount of revenue is extremely important for the company—it must be sufficient to ensure the repayment of all expenses of the company and the formation of the required profit amount. However, the amount of revenue itself is not the only important characteristic of revenue; the revenue stability over time and the revenue receipt regularity are no less important. The purpose of this work is to develop a dynamic model of enterprise revenue management, which differs from the model known in the literature by considering the parameter of enterprise performance efficiency. The parametric method of Stochastic Frontier Analysis (SFA) is used as a method to evaluate the efficiency of an enterprise. Financial indicators are used as input and output data. The model was tested on six small business sectors of a single-industry town for the period from 2007 to 2021. Data collection was carried out using the SPARK system, which allows selecting enterprises for research by the status of the enterprise (bankrupt/operating), by the size of the enterprise (large/medium/small/micro), etc. The above calculations based on the constructed modified model have demonstrated the possibility of using the enterprise’s revenue management with the desired rate of change and with the work efficiency parameter.

Suggested Citation

  • Aliya Alimhanova & Andrey Vazhdaev & Artur Mitsel & Anatoly Sidorov, 2022. "Dynamic Model of Enterprise Revenue Management Based on the SFA Model," Mathematics, MDPI, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:211-:d:1021419
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/1/211/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/1/211/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wheelock, David C & Wilson, Paul W, 1995. "Explaining Bank Failures: Deposit Insurance, Regulation, and Efficiency," The Review of Economics and Statistics, MIT Press, vol. 77(4), pages 689-700, November.
    2. Mogilat , Anastasia & Ipatova, Irina, 2016. "Technical efficiency as a factor of Russian industrial companies’ risks of financial distress," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 42, pages 05-29.
    3. Ruey-Ching Hwang & Jhao-Siang Siao & Huimin Chung & C. Chu, 2011. "Assessing bankruptcy prediction models via information content of technical inefficiency," Journal of Productivity Analysis, Springer, vol. 36(3), pages 263-273, December.
    4. Dimara, Efthalia & Skuras, Dimitris & Tsekouras, Kostas & Tzelepis, Dimitris, 2008. "Productive efficiency and firm exit in the food sector," Food Policy, Elsevier, vol. 33(2), pages 185-196, April.
    5. Kai Sun & Ruhul Salim, 2020. "A semiparametric stochastic input distance frontier model with application to the Indonesian banking industry," Journal of Productivity Analysis, Springer, vol. 54(2), pages 139-156, December.
    6. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    7. Quang Nguyen & Sean Pascoe & Louisa Coglan & Son Nghiem, 2021. "The sensitivity of efficiency scores to input and other choices in stochastic frontier analysis: an empirical investigation," Journal of Productivity Analysis, Springer, vol. 55(1), pages 31-40, February.
    8. Alecos Papadopoulos & Christopher F. Parmeter, 2022. "Quantile Methods for Stochastic Frontier Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 12(1), pages 1-120, November.
    9. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    10. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    11. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    12. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    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. Pavlos Almanidis & Robin C. Sickles, 2016. "Banking Crises, Early Warning Models, and Efficiency," International Series in Operations Research & Management Science, in: Juan Aparicio & C. A. Knox Lovell & Jesus T. Pastor (ed.), Advances in Efficiency and Productivity, chapter 0, pages 331-364, Springer.
    2. Sanchez González, Jim & Restrepo-Tobón, Diego & Ramírez Hassan, Andrés, 2021. "Inefficiency and bank failure: A joint Bayesian estimation method of stochastic frontier and hazards models," Economic Modelling, Elsevier, vol. 95(C), pages 344-360.
    3. Ruey-Ching Hwang & Chih-Kang Chu, 2013. "Forecasting forward defaults: a simple hazard model with competing risks," Quantitative Finance, Taylor & Francis Journals, vol. 14(8), pages 1467-1477, August.
    4. Ruey-Ching Hwang & Huimin Chung & Jiun-Yi Ku, 2013. "Predicting Recurrent Financial Distresses with Autocorrelation Structure: An Empirical Analysis from an Emerging Market," Journal of Financial Services Research, Springer;Western Finance Association, vol. 43(3), pages 321-341, June.
    5. Hwang, Ruey-Ching, 2012. "A varying-coefficient default model," International Journal of Forecasting, Elsevier, vol. 28(3), pages 675-688.
    6. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    7. Pesaran, M. Hashem & Schuermann, Til & Treutler, Bjorn-Jakob & Weiner, Scott M., 2006. "Macroeconomic Dynamics and Credit Risk: A Global Perspective," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 38(5), pages 1211-1261, August.
    8. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    9. Huang, Hsing-Hua & Lee, Han-Hsing, 2013. "Product market competition and credit risk," Journal of Banking & Finance, Elsevier, vol. 37(2), pages 324-340.
    10. Filipe, Sara Ferreira & Grammatikos, Theoharry & Michala, Dimitra, 2016. "Forecasting distress in European SME portfolios," Journal of Banking & Finance, Elsevier, vol. 64(C), pages 112-135.
    11. Giesecke, Kay & Longstaff, Francis A. & Schaefer, Stephen & Strebulaev, Ilya, 2011. "Corporate bond default risk: A 150-year perspective," Journal of Financial Economics, Elsevier, vol. 102(2), pages 233-250.
    12. Tsukahara, Fábio Yasuhiro & Kimura, Herbert & Sobreiro, Vinicius Amorim & Zambrano, Juan Carlos Arismendi, 2016. "Validation of default probability models: A stress testing approach," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 70-85.
    13. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    14. Tao, Qizhi & Chen, Carl & Lu, Rui & Zhang, Ting, 2017. "Underfunding or distress? An analysis of corporate pension underfunding and the cross-section of expected stock returns," International Review of Economics & Finance, Elsevier, vol. 48(C), pages 116-133.
    15. Sigrist, Fabio & Leuenberger, Nicola, 2023. "Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1390-1406.
    16. Til Schuermann, 2005. "A review of recent books on credit risk," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(1), pages 123-130, January.
    17. Suparatana Tanthanongsakkun & Sirimon Treepongkaruna, 2008. "Explaining Credit Ratings of Australian Companies—An Application of the Merton Model," Australian Journal of Management, Australian School of Business, vol. 33(2), pages 261-275, December.
    18. Ester Chen & Ilanit Gavious & Nadav Steinberg, 2019. "Dividends from unrealized earnings and default risk," Review of Accounting Studies, Springer, vol. 24(2), pages 491-535, June.
    19. Mark Clintworth & Dimitrios Lyridis & Evangelos Boulougouris, 2023. "Financial risk assessment in shipping: a holistic machine learning based methodology," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 25(1), pages 90-121, March.
    20. Charitou, Andreas & Dionysiou, Dionysia & Lambertides, Neophytos & Trigeorgis, Lenos, 2013. "Alternative bankruptcy prediction models using option-pricing theory," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2329-2341.

    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:2022:i:1:p:211-:d:1021419. 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.