Abstract
The aim of the article is to explore the day-of-the-week effect in emerging stock markets. This effect relates to the attempts to find statistically significant dependences of stock trading anomalies, which occur in particular days of the week (usually the first or the last trading day), and which could be important for creating profitable investment strategies. The main question of the research is to define, if this anomalies affects the entire market, or it is applicable only for the specific groups of stocks, which could be recognized by identifying particular features. The investigation of the day-of-the-week effect is performed by applying two methods: traditional statistical analysis and artificial neural networks. The experimental analysis is based on financial data of the Vilnius Stock Exchange, as of the case of emerging stock market with relatively low turnover and small number of players. Application of numerous tests and methods reveals better effectiveness of the artificial neural networks for indicating significance of day-of-the-week effect.
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Sakalauskas, V., Kriksciuniene, D. (2007). Analysis of the Day-of-the-Week Anomaly for the Case of Emerging Stock Market. In: Neves, J., Santos, M.F., Machado, J.M. (eds) Progress in Artificial Intelligence. EPIA 2007. Lecture Notes in Computer Science(), vol 4874. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77002-2_31
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DOI: https://doi.org/10.1007/978-3-540-77002-2_31
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