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Assessment of market reaction on the share performance on the basis of its visualization in 2D space

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Listed:
  • Ingrida Vaiciulyte
  • Zivile Kalsyte
  • Leonidas Sakalauskas
  • Darius Plikynas

Abstract

This paper provides a new methodology for company assessment besides other traditional assessment measures such as share price or forecasts of the analysts. It is suggested to assess the market reaction on change in share price via using graphical approaches. Investors buy shares with the expectation that its price will rise in the future. But sometimes expectations don’t coincide with reality and then shares are sold. This work has been taken into account in the asymmetry between expectations of investors and results. In order to identify the position of a company in 2D space, the paper uses classification algorithm of random forests with data on change in share price during the period of the year in the inputs, and the forecasts of analysts, i.e., whether a price will increase or decrease, for the same year in the outputs. Thus, two clusters of companies are seeking to represent: one of the companies whose changes in share price coincide with investors’ expectations, and another one – on the contrary. This method can be useful to investors, for whom it is important to identify the market reaction about companies from the whole industry or its branches and analyze its trend.

Suggested Citation

  • Ingrida Vaiciulyte & Zivile Kalsyte & Leonidas Sakalauskas & Darius Plikynas, 2017. "Assessment of market reaction on the share performance on the basis of its visualization in 2D space," Journal of Business Economics and Management, Taylor & Francis Journals, vol. 18(2), pages 309-318, March.
  • Handle: RePEc:taf:jbemgt:v:18:y:2017:i:2:p:309-318
    DOI: 10.3846/16111699.2017.1285348
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    References listed on IDEAS

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    1. Archer, Kellie J. & Kimes, Ryan V., 2008. "Empirical characterization of random forest variable importance measures," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 2249-2260, January.
    2. Panagiotelis, Anastasios & Smith, Michael, 2008. "Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions," International Journal of Forecasting, Elsevier, vol. 24(4), pages 710-727.
    3. Barber, Brad M. & Lehavy, Reuven & McNichols, Maureen & Trueman, Brett, 2006. "Buys, holds, and sells: The distribution of investment banks' stock ratings and the implications for the profitability of analysts' recommendations," Journal of Accounting and Economics, Elsevier, vol. 41(1-2), pages 87-117, April.
    4. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
    5. Adelchi Azzalini & Marc G. Genton, 2008. "Robust Likelihood Methods Based on the Skew‐t and Related Distributions," International Statistical Review, International Statistical Institute, vol. 76(1), pages 106-129, April.
    6. Hapfelmeier, A. & Ulm, K., 2013. "A new variable selection approach using Random Forests," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 50-69.
    7. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    8. Kenneth Peasnell & Yuan Yin & Martien Lubberink, 2018. "Analysts’ stock recommendations, earnings growth and risk," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(1), pages 217-254, March.
    Full references (including those not matched with items on IDEAS)

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