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Formation of an integrated stock price forecast model in Lithuania

    Audrius Dzikevičius Affiliation

Abstract

Technical and fundamental analyses are widely used to forecast stock prices due to lack of knowledge of other modern models and methods such as Residual Income Model, ANN-APGARCH, Support Vector Machine, Probabilistic Neural Network and Genetic Fuzzy Systems. Although stock price forecast models integrating both technical and fundamental analyses are currently used widely, their integration is not justified comprehensively enough. This paper discusses theoretical one-factor and multi-factor stock price forecast models already applied by investors at a global level and determines possibility to create and apply practically a stock price forecast model which integrates fundamental and technical analysis with the reference to the Lithuanian stock market. The research is aimed to determine the relationship between stock prices of the 14 Lithuanian companies listed in the Main List by the Nasdaq OMX Baltic and various fundamental variables. Based on correlation and regression analysis results and application of c-Squared Test, ANOVA method, a general stock price forecast model is generated. This paper discusses practical implications how the developed model can be used to forecast stock prices by individual investors and suggests additional check measures.

Keyword : stock price, forecast, correlation analysis, regression analysis, fundamental analysis, technical analysis, model, investment

How to Cite
Dzikevičius, A. (2016). Formation of an integrated stock price forecast model in Lithuania. Business, Management and Economics Engineering, 14(2), 292-307. https://doi.org/10.3846/bme.2016.337
Published in Issue
Dec 29, 2016
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This work is licensed under a Creative Commons Attribution 4.0 International License.