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The Utilization of Autoregressive Forecasting Models in Strategic Management

Author

Listed:
  • Mustafa Ozguven

    (Business School, University of International Business and Economics, Beijing, China.)

  • Chong Yan Gao

    (Business School, University of International Business and Economics, Beijing, China.)

  • Mohamed Yacine Si Tayeb

    (Business School, University of International Business and Economics, Beijing, China.)

Abstract

This study explores the utilization of autoregressive forecasting models in strategic management. Business forecasting denotes one of the recent developments in the business environment. The approach complements strategic management to foster the optimal performance of businesses. Business strategists use forecasting models to develop foresight on the future performance of their respective firms; however, there is limited literature on the effectiveness of these models. For this reason, this exploratory inquiry delved into generating autoregressive models and further examining their predictive effectiveness. The methods entailed the collection of secondary data (Tesla Motors Inc. revenue data) and subjecting it to univariate regression analysis to generate the linear forecasting equation. The findings revealed that autoregressive models are generated from the current and past data and can be used to forecasting future business performance. However, the accuracy of these equations relies on the quality of data and the stability of the industry. Therefore, the results of this inquiry contribute to the existing literature on forecasting models. Policy planners can use the information to improve the accuracy of their prediction models.

Suggested Citation

  • Mustafa Ozguven & Chong Yan Gao & Mohamed Yacine Si Tayeb, 2021. "The Utilization of Autoregressive Forecasting Models in Strategic Management," International Journal of Science and Business, IJSAB International, vol. 5(7), pages 170-185.
  • Handle: RePEc:aif:journl:v:5:y:2021:i:7:p:170-185
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    References listed on IDEAS

    as
    1. Magdalena Alina ILCUS, 2018. "Impact of Digitalization in Business World," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 19(4), pages 350-358, October.
    2. Rodolphe Durand & Robert M. Grant & Tammy L. Madsen & Lenos Trigeorgis & Jeffrey J. Reuer, 2017. "Real options theory in strategic management," Strategic Management Journal, Wiley Blackwell, vol. 38(1), pages 42-63, January.
    3. Anand Prakash & Sanjay Kumar Jha & Kapil Deo Prasad & Abhishek Kumar Singh, 2017. "Productivity, quality and business performance: an empirical study," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 66(1), pages 78-91, January.
    4. Christophe Boone & Boris Lokshin & Hannes Guenter & René Belderbos, 2019. "Top management team nationality diversity, corporate entrepreneurship, and innovation in multinational firms," Strategic Management Journal, Wiley Blackwell, vol. 40(2), pages 277-302, February.
    5. Rodolphe Durand & Robert M. Grant & Tammy L. Madsen & Rodolphe Durand & Robert M. Grant & Tammy L. Madsen, 2017. "The expanding domain of strategic management research and the quest for integration," Strategic Management Journal, Wiley Blackwell, vol. 38(1), pages 4-16, January.
    6. Ji Yong Lee & John A. Fox & Rodolfo M. Nayga, 2019. "Effect of substitutes in contingent valuation for a private market good," Applied Economics Letters, Taylor & Francis Journals, vol. 26(14), pages 1153-1156, August.
    7. Sepehr Ramyar & Farhad Kianfar, 2019. "Forecasting Crude Oil Prices: A Comparison Between Artificial Neural Networks and Vector Autoregressive Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 743-761, February.
    8. Liu, Yuan-Yuan & Tseng, Fang-Mei & Tseng, Yi-Heng, 2018. "Big Data analytics for forecasting tourism destination arrivals with the applied Vector Autoregression model," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 123-134.
    9. van der Kamp, Denise & Lorentzen, Peter & Mattingly, Daniel, 2017. "Racing to the Bottom or to the Top? Decentralization, Revenue Pressures, and Governance Reform in China," World Development, Elsevier, vol. 95(C), pages 164-176.
    10. Alexander Osharin & Valery Verbus, 2018. "Heterogeneity of consumer preferences and trade patterns in a monopolistically competitive setting," Journal of Economics, Springer, vol. 125(3), pages 211-237, November.
    11. Darin, Sarah Goodrich & Stellwagen, Eric, 2020. "Forecasting the M4 competition weekly data: Forecast Pro’s winning approach," International Journal of Forecasting, Elsevier, vol. 36(1), pages 135-141.
    12. Gaganis, Chrysovalantis & Pasiouras, Fotios & Voulgari, Fotini, 2019. "Culture, business environment and SMEs' profitability: Evidence from European Countries," Economic Modelling, Elsevier, vol. 78(C), pages 275-292.
    13. Delia Deliu, 2019. "Empathetic Leadership – Key Element for Inspiring Strategic Management and a Visionary Effective Corporate Governance," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, vol. 1(1), pages 280-292, November.
    14. Newell, Richard G. & Pizer, William A. & Raimi, Daniel, 2019. "U.S. federal government subsidies for clean energy: Design choices and implications," Energy Economics, Elsevier, vol. 80(C), pages 831-841.
    15. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    16. Nakamura, Koji & Kaihatsu, Sohei & Yagi, Tomoyuki, 2019. "Productivity improvement and economic growth: lessons from Japan," Economic Analysis and Policy, Elsevier, vol. 62(C), pages 57-79.
    17. Marvin B. Lieberman & Gwendolyn K. Lee & Timothy B. Folta, 2017. "Entry, exit, and the potential for resource redeployment," Strategic Management Journal, Wiley Blackwell, vol. 38(3), pages 526-544, March.
    18. Cubadda, Gianluca & Guardabascio, Barbara, 2019. "Representation, estimation and forecasting of the multivariate index-augmented autoregressive model," International Journal of Forecasting, Elsevier, vol. 35(1), pages 67-79.
    19. Richard Makadok & Richard Burton & Jay Barney, 2018. "A practical guide for making theory contributions in strategic management," Strategic Management Journal, Wiley Blackwell, vol. 39(6), pages 1530-1545, June.
    20. Haibin Xie, 2019. "Financial volatility modeling: The feedback asymmetric conditional autoregressive range model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(1), pages 11-28, January.
    21. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    22. Beladi, Hamid & Mukherjee, Arijit, 2017. "Union bargaining power, subcontracting and innovation," Journal of Economic Behavior & Organization, Elsevier, vol. 137(C), pages 90-104.
    23. Imen Mzid & Nada Khachlouf & Richard Soparnot, 2019. "How does family capital influence the resilience of family firms?," Journal of International Entrepreneurship, Springer, vol. 17(2), pages 249-277, June.
    24. Hongquan Chen & Saixing Zeng & Han Lin & Hanyang Ma, 2017. "Munificence, Dynamism, and Complexity: How Industry Context Drives Corporate Sustainability," Business Strategy and the Environment, Wiley Blackwell, vol. 26(2), pages 125-141, February.
    25. Stef, Nicolae & Zenou, Emmanuel, 2021. "Management-to-staff ratio and a firm's exit," Journal of Business Research, Elsevier, vol. 125(C), pages 252-260.
    26. Sheikh, Shahbaz, 2018. "Corporate social responsibility, product market competition, and firm value," Journal of Economics and Business, Elsevier, vol. 98(C), pages 40-55.
    27. Rusi Sun & Weijie Wang, 2017. "Transformational leadership, employee turnover intention, and actual voluntary turnover in public organizations," Public Management Review, Taylor & Francis Journals, vol. 19(8), pages 1124-1141, September.
    28. Muhammad Haseeb & Hafezali Iqbal Hussain & Sebastian Kot & Armenia Androniceanu & Kittisak Jermsittiparsert, 2019. "Role of Social and Technological Challenges in Achieving a Sustainable Competitive Advantage and Sustainable Business Performance," Sustainability, MDPI, vol. 11(14), pages 1-23, July.
    29. Oscar Claveria & Enric Monte & Salvador Torra, 2019. "Evolutionary Computation for Macroeconomic Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 833-849, February.
    30. Boratyńska, Katarzyna & Grzegorzewska, Emilia, 2018. "Bankruptcy prediction in the agribusiness sector: Lessons from quantitative and qualitative approaches," Journal of Business Research, Elsevier, vol. 89(C), pages 175-181.
    31. Meagher, Kieron J. & Wong, Arlene & Zauner, Klaus G., 2020. "A competitive analysis of fail fast: Shakeout and uncertainty about consumer tastes," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 589-600.
    32. Florian Huber & Martin Feldkircher, 2019. "Adaptive Shrinkage in Bayesian Vector Autoregressive Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 27-39, January.
    33. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    34. Timothy Gubler & Ian Larkin & Lamar Pierce, 2018. "Doing Well by Making Well: The Impact of Corporate Wellness Programs on Employee Productivity," Management Science, INFORMS, vol. 64(11), pages 4967-4987, November.
    35. Arora, Siddharth & Taylor, James W., 2018. "Rule-based autoregressive moving average models for forecasting load on special days: A case study for France," European Journal of Operational Research, Elsevier, vol. 266(1), pages 259-268.
    36. Theodos, Brett & Stacy, Christina Plerhoples & Daniels, Rebecca, 2018. "Client led coaching: A random assignment evaluation of the impacts of financial coaching programs," Journal of Economic Behavior & Organization, Elsevier, vol. 155(C), pages 140-158.
    37. Mohammad Ebrahim Banihabib & Reihaneh Bandari & Richard C. Peralta, 2019. "Auto-Regressive Neural-Network Models for Long Lead-Time Forecasting of Daily Flow," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 159-172, January.
    38. Mina Nasiri & Tero Rantala & Minna Saunila & Juhani Ukko & Hannu Rantanen, 2018. "Transition towards Sustainable Solutions: Product, Service, Technology, and Business Model," Sustainability, MDPI, vol. 10(2), pages 1-18, January.
    39. Aleš Popovič & Ray Hackney & Rana Tassabehji & Mauro Castelli, 2018. "The impact of big data analytics on firms’ high value business performance," Information Systems Frontiers, Springer, vol. 20(2), pages 209-222, April.
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