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On the predictability of firm performance via simple time-series and econometric models: evidence from UK SMEs

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  • Vicky Bamiatzi
  • Konstantinos Bozos
  • Konstantinos Nikolopoulos

Abstract

This article examines the predictive accuracy of simple time-series and econometric models on forecasting firm performance in terms of sales turnover. Evidence from Small and Medium sized Enterprises (SMEs) in the United Kingdom are presented. The study identifies operational rules under which the class of simple econometric regression models is more accurate than simple time-series forecasting alternatives, thus more appropriate to back-up multiple investment decisions.

Suggested Citation

  • Vicky Bamiatzi & Konstantinos Bozos & Konstantinos Nikolopoulos, 2010. "On the predictability of firm performance via simple time-series and econometric models: evidence from UK SMEs," Applied Economics Letters, Taylor & Francis Journals, vol. 17(3), pages 279-282, February.
  • Handle: RePEc:taf:apeclt:v:17:y:2010:i:3:p:279-282
    DOI: 10.1080/13504850701720163
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    References listed on IDEAS

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    1. Assimakopoulos, V. & Nikolopoulos, K., 2000. "The theta model: a decomposition approach to forecasting," International Journal of Forecasting, Elsevier, vol. 16(4), pages 521-530.
    2. D. E. Allen & H. M. Salim, 2005. "Forecasting profitability and earnings: a study of the UK market (1982-2000)," Applied Economics, Taylor & Francis Journals, vol. 37(17), pages 2009-2018.
    3. Gabriel Hawawini & Venkata Subban Subramanian & Paul Verdin, 2003. "Is performance driven by industry- or firm-specific factors? A new look at the evidence," ULB Institutional Repository 2013/14188, ULB -- Universite Libre de Bruxelles.
    4. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    5. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    6. C. Petropoulos & K. Nikolopoulos & A. Patelis & V. Assimakopoulos, 2005. "A technical analysis approach to tourism demand forecasting," Applied Economics Letters, Taylor & Francis Journals, vol. 12(6), pages 327-333.
    7. George Halkos & Ilias Kevork, 2006. "Forecasting the stationary AR(1) with an almost unit root," Applied Economics Letters, Taylor & Francis Journals, vol. 13(12), pages 789-793.
    8. Perry Sadorsky, 2005. "Stochastic volatility forecasting and risk management," Applied Financial Economics, Taylor & Francis Journals, vol. 15(2), pages 121-135.
    9. K. Maris & K. Nikolopoulos & K. Giannelos & V. Assimakopoulos, 2007. "Options trading driven by volatility directional accuracy," Applied Economics, Taylor & Francis Journals, vol. 39(2), pages 253-260.
    10. K. Maris & G. Pantou & K. Nikolopoulos & E. PagourtzI & V. Assimakopoulos, 2004. "A study of financial volatility forecasting techniques in the FTSE/ASE 20 index," Applied Economics Letters, Taylor & Francis Journals, vol. 11(7), pages 453-457.
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    Cited by:

    1. Varum, Celeste Amorim & Rocha, Vera Catarina Barros, 2011. "Do foreign and domestic firms behave any different during economic slowdowns?," International Business Review, Elsevier, vol. 20(1), pages 48-59, February.

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