Evaluation of forecasting methods from selected stock market returns
Autor: | R. Prabhakara Rao, M. Mallikarjuna |
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Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
Mean squared error
02 engineering and technology lcsh:K4430-4675 Empirical research Management of Technology and Innovation lcsh:Finance lcsh:HG1-9999 0502 economics and business ddc:650 0202 electrical engineering electronic engineering information engineering Frontier markets Econometrics Economics lcsh:Public finance C53 Risk management Stock (geology) G17 050208 finance Forecasting techniques business.industry Financial markets 05 social sciences Financial market G15 Stock market index Root mean square error Stock returns 020201 artificial intelligence & image processing Stock market Linear and nonlinear business Finance C22 |
Zdroj: | Financial Innovation, Vol 5, Iss 1, Pp 1-16 (2019) |
Popis: | Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification. There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making. Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices. However, there have been very few studies of groups of stock markets or indices. The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets. In this context, this study aimed to examine the predictive performance of linear, nonlinear, artificial intelligence, frequency domain, and hybrid models to find an appropriate model to forecast the stock returns of developed, emerging, and frontier markets. We considered the daily stock market returns of selected indices from developed, emerging, and frontier markets for the period 2000–2018 to evaluate the predictive performance of the above models. The results showed that no single model out of the five models could be applied uniformly to all markets. However, traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts. |
Databáze: | OpenAIRE |
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