Milena Jakšić, Marina Milanović, Dragan Stojković

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At the onset of 2019, global economy has been facing a number of macroeconomic issues, which significantly multiplied in the course of the past ten-year period. Slow-moving rate of economic growth, increased fiscal deficits, enormous public and private debt – these are just some of the issues which led to the plunge of the leading stock market indices at the end of 2018. Bearing in mind that S&P 500, DJIA and NASDAQ Composite stopped the multiannual growth trend which started on March 22, 2009, new quakes on the global financial market may well be expected. Unlike developed global stock markets, which hugely recovered from the 2008 crash, the Belgrade Stock Exchange showed no significant growth trend in the observed period. In this respect, regardless of the detected declines of the world’s best known stock market indices, it is not realistic to expect any significant change in the Belgrade Stock Exchange share market, which the conducted empirical research should confirm. The basic goal of the research is to establish the monthly tendencies of BELEXline and BELEX15 movements in the forthcoming one-year period. The basic hypothesis of the research is that there will be no significant changes in the movement of the values of selected stock market indicators in the Belgrade Stock Exchange share market during the one-year period to come. 


Short-term forecasting, BELEXline, BELEX15, Winters’ additive model, Winters’ multiplicative method

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Bovas, A. & Ledolter, Ј. (2009). Statistical Methods for Forecasting, New Jersey: John Wiley & Sons, Inc.

Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple Technical Trading Rules and Stochastic Properties of Stock Returns. Journal of Finance, 47(5), 1731-1764.

Chatfield, Ch. (2001). Time-Series Forecasting, USA: Chapman & Hall/CRC.

Kalekar, P. S. (2004). Time series forecasting using holt-winters exponential smoothing. Kanwal Rekhi School of Information Technology, 1-13.

Leigh, W., Hightower, R., & Modani, N. (2005). Forecasting the New York Stock Exchange Composite Index with Past Price and Interest Rate on Condition of Volume Spike. Expert Systems with Applications, 28(1), 1-8.

Lovrić, M., Milanović, M., & Stamenković, M. (2013). Kratkoročno prognoziranje kretanja ključnih makroekonomskih indikatora u Srbiji [Short-term forecasting of key macroeconomic indicators in Serbia]. Zbornik radova: Matematičko-statistički modeli i informaciono-komunikacione tehnologije u funkciji razvoja sistema, urednici: Drenovak, M., Arsovski, Z. & Ranković, V., Kragujevac: Ekonomski fakultet.

Mostafa, M. (2010). Forecasting Stock Exchange Movements using Neural Networks: Empirical evidence from Kuwait. Expert Systems with Application, 37(9), 6302-6309.

Rusu, V., & Rusu, C. (2003). Forecasting methods and stock market analysis. Creative Math, 12, 103-110.

Siegel, J. J. (2008). Stocks for the Long Run. 4th edition, New York: McGraw-Hill.

Sullivan, R., Timmermann, A., & White, H. (1999). Data-Snooping, Technical Trading Rule Perfformance, and the Bootstrap. Journal of Finance, 54(5), 1647-1691.

Tolosa, H., Camano, M. S., De Lumen, C. A., Charmaine, T. J., & Marieve L. E. E. (2015). Forecasting the Philippine Stock Exchange Index using Time Series Analysis Box-Jenkins. European Academic Research, 3(3), 3097-3113.

Tseng, K. C. Ojoung K., & Tjung L. C. (2012). Time series and neural network forecasts of daily stock prices. Investment Management and Financial Innovations, 9(1).

Varghese, A., Tarhen, H., Shaikh, A., Banik, P., & Ramadasi, A. (2016). Stock Market Prediction Using Time Series. International Journal on Recent and Innovation Trends in Computing and Communication, 4(5), 427-430.

Yumlu, S., Gurgen, F., & Okay, N. (2005). A Comparison of global, Recurrent and Smoothed-Piecewise Neural Models for Istanbul Stock Exchange (ISE) Prediction. Pattern Recognition Letters, 26(13), 2093-2103.



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