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Eforecasting Financial Indexes With Model Of Composite Events Influence

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  • Sergey SVESHNIKOV
  • Victor BOCHARNIKOV

Abstract

In this article we propose the model for the forecast of various financial indexes: stock markets indexes; currency exchange rates; crediting rates. Behaviour of financial indexes depends on psychological sentiments of players (investors, traders) and their inclination to buy or sell financial tools. We have made the supposition that political, economical, financial and other events are preconditions for formation of the future psychological sentiments of players. Therefore, for forecasting financial indexes we estimate influence of all topical events on the future inclination of players to buy or sell. The proposed model calculates the composite influence of events on the basis of estimations of influence direction, influence force, influence time, events importance and confidence to the information about events. The model fulfils the calculations with help of fuzzy integral Sugeno (1972). We have used this model for forecasting indexes of various economical natures: Ukrainian stock index (PFTS); exchange rate EUR/USD; crediting rate KievPrime 1M and quotations of Eurobonds Ukraine 2015. We also have estimated errors and horizons of forecasts..

Suggested Citation

  • Sergey SVESHNIKOV & Victor BOCHARNIKOV, 2009. "Eforecasting Financial Indexes With Model Of Composite Events Influence," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 4(3(9)_Fall).
    • Handle: RePEc:ush:jaessh:v:4:y:2009:i:3(9)_fall2009:76
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    1. Marshall, Ben R. & Cahan, Rochester H., 2005. "Is technical analysis profitable on a stock market which has characteristics that suggest it may be inefficient?," Research in International Business and Finance, Elsevier, vol. 19(3), pages 384-398, September.
    2. Fong Chan, Kam & Gray, Philip, 2006. "Using extreme value theory to measure value-at-risk for daily electricity spot prices," International Journal of Forecasting, Elsevier, vol. 22(2), pages 283-300.
    3. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    4. Bradley, Michael D. & Jansen, Dennis W., 2004. "Forecasting with a nonlinear dynamic model of stock returns and industrial production," International Journal of Forecasting, Elsevier, vol. 20(2), pages 321-342.
    5. Jorge Caiado, 2004. "Modelling And Forecasting The Volatility Of The Portuguese Stock Index Psi-20," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 9(1), pages 3-21.
    6. Chin-Shien Lin & Haider Ali Khan & Chi-Chung Huang, 2002. "Can the neuro fuzzy model predict stock indexes better than its rivals?," CIRJE F-Series CIRJE-F-165, CIRJE, Faculty of Economics, University of Tokyo.
    7. Hekuran NEZIRI, 2009. "Can Credit Default Swaps Predict Financial Crises? Empirical Study On Emerging Markets," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 4(1(7)_ Spr).
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    Cited by:

    1. K. Senthil KUMAR & C. VIJAYABANU & R. AMUDHA, 2012. "A Case Study On Investors’ Financial Literacy In Indian Scenario," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 7(3(21)/ Fa), pages 262-269.
    2. Kostyantyn MALYSHENKO & Vadim MALYSHENKO & Elena Yu. PONOMAREVA & Marina ANASHKINA, 2019. "Analysis of the stock market anomalies in the context of changing the information paradigm," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 10, pages 239-270, June.

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