Share:


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

    Ingrida Vaiciulyte Affiliation
    ; Zivile Kalsyte Affiliation
    ; Leonidas Sakalauskas Affiliation
    ; Darius Plikynas Affiliation

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.

Keyword : skew t distribution, maximum likelihood method, random forests, , forecasting, rating, management, assessment

How to Cite
Vaiciulyte, I., Kalsyte, Z., Sakalauskas, L., & Plikynas, D. (2017). Assessment of market reaction on the share performance on the basis of its visualization in 2D space. Journal of Business Economics and Management, 18(2), 309-318. https://doi.org/10.3846/16111699.2017.1285348
Published in Issue
Apr 21, 2017
Abstract Views
590
PDF Downloads
522
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.