Abstract
In this article, the problem of the day-of-the-week effect is investigated for small emerging stock market (Vilnius Stock Exchange), where this effect is vaguely expressed and its presence is difficult to confirm. The research results helped to conclude the effectiveness of application of neural networks, as compared to the traditional linear statistical methods for identifying differences of Monday and Friday stock trading anomalies. The effectiveness of the method was confirmed by exploring impact of different variables to the day-of-the-week effect, evaluation of their influence and sensitivity analysis, and by selecting best performing neural network type.
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References
Balaban, E., Bayar, A., Kan, O.B.: Stock returns, seasonality and asymmetricconditional volatility in World Equity Markets. Applied Economics Letters 8, 263–268 (2001)
Basher, S.A., Sadorsky, P.: Day-of-the-week effects in emerging stock markets, vol. 13, pp. 621–628. Taylor and Francis, Abington (2006)
Brooks, C., Persand, G.: Seasonality in Southeast Asian stock markets: somenew evidence on day-of-the-week effects. Applied Economics Letters 8, 155–158 (2001)
Fama, E.F.: The behaviour of stock market prices. J. Busin. 38, 34–105 (1965)
Flannery, M.J., Protopapadakis, A.A.: From T-bills to common stocks: investigating the generality of intra-week return seasonality. Journal of Finance 43, 431–450 (1988)
Gencay, R.: The predictability of security returns with simple technical trading. Journal of Empirical Finance 5, 347–359 (1998), http://dapissarenko.com/resources/fyp/Pissarenko2002.pdf
Kamath, R., Chusanachoti, J.: An investigation of the day-of-the-week effect in Korea: has the anomalous effect vanished in the 1990s? International Journal of Business 7, 47–62 (2002)
Kohers, G., Kohers, N., Pandey, V., Kohers, T.: The disappearing day-of-the- week effect in the world’s largest equity markets. Applied Economics Letters 11, 167–171 (2004)
Kumar, M., Thenmozhi, M.: Forecasting Nifty Index Futures Returns usingNeural Network and ARIMA Models. Financial Engineering and Applications (2004)
Nekipelov, N.: An Experiment on Forecasting the Financial Markets, BaseGroup Labs (2007), http://www.basegroup.ru/tech/stockmarket.en.htm
Pissarenko, D.: Neural networks for financial time series prediction: Overview over recent research. BSc thesis (2002)
Qi, M.: Nonlinear predictability of stock returns using financial and economic variables. Journal of Business and Economic Statistics 17, 419–429 (1999)
Reschenhofer, E.: Unexpected Features of Financial Time Series: Higher-Order Anomalies and Predictability. Journal of Data Science 2, 1–15 (2004)
Sakalauskas, V., Kriksciuniene, D.: The impact of daily trade volume on the day- of-the-week effect in emerging stock markets. Information Technology and Control 36(1A), 152–158 (2007)
StatSoft Inc., Electronic Statistics Textbook. Tulsa, OK: StatSoft. WEB (2006), http://www.statsoft.com/textbook/stathome.html
Steeley, J.M.: A note on information seasonality and the disappearance of the weekend effect in the UK stock market. Journal of Banking and Finance 25, 1941–1956 (2001)
Sullivan, R., Timmermann, A., White, H.: Dangers of Data-Driven Inference: The Case of Calendar Effects in Stock Returns, Working Paper, University of California, San Diego (1998), ucsd.edu/hwcv081a.pdf
Tang, G.Y.N.: Day-of-the-week effect on skewness and kurtosis: a direct test and portfolio effect The European Journal of Finance.  2, 333–351 (1998)
The Nordic Exchange (2006), http://www.baltic.omxgroup.com/
Virili, F., Reisleben, B.: Nonstationarity and data preprocessing for neural network predictions of an economic time series. In: Proc. Int. Joint Conference on Neural Networks 2000, Como, vol. 5, pp. 129–136 (2000)
Yao, J.T., Tan, C.L.: Guidelines for financial forecasting with neural networks. In: Proc. International Conference on Neural Information Processing, Shanghai, China, pp. 757–761 (2001)
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Sakalauskas, V., Kriksciuniene, D. (2009). Application of Neural Networks for Investigating Day-of-the-Week Effect in Stock Market. In: Koutsojannis, C., Sirmakessis, S. (eds) Tools and Applications with Artificial Intelligence. Studies in Computational Intelligence, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88069-1_7
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DOI: https://doi.org/10.1007/978-3-540-88069-1_7
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