![]() Tasy ( 1989) proposed a simple yet widely applicable model-building procedure for threshold autoregressive models as well as a test for threshold nonlinearity. To account for nonlinearities resulting from regime changes in economies, some researchers have used Markov regime-switching models and threshold autoregressive (TAR) models assuming nonlinear stationary processes to predict stock prices (Hamilton, 1989 Tong, 1990). Quite a few studies found that ARIMA models produced inferior forecasts for financial time series data (Zhang, 2003 Adebiyi and Oluinka, 2014 Khandelwal et al., 2015). Some studies have been conducted by employing ARIMA models to forecast stock market returns (Al-Shaib, 2006 Ojo and Olatayo, 2009 Adebiyi and Oluinka, 2014 Mondal et al., 2014). Autoregressive integrated moving average (ARIMA) models were proposed by Box and Jenkins ( 1970) for time series analysis and forecasting. Various forecasting techniques are available for time series forecasting. However, most empirical studies have found that stock prices are predictable (Darrat and Zhong, 2000 Lo and MacKinlay, 2002 Harrison and Moore, 2012 Owido et al., 2013 Radikoko, 2014 Said, 2015 Almudhaf, 2018). Some empirical studies have shown the presence of ‘random walk’ in stock prices (e.g., Tong et al., 2014 Konak and Seker, 2014 Erdem and Ulucak, 2016). These approaches were challenged in the 1960s by random walk theory, popularly known as the efficient market hypothesis (Fama, 1970), which proposes that future changes in stock prices cannot be predicted from past price changes. These theories coexisted for several decades as strategies for investment decision making. Technical analysts, meanwhile, use historical securities data and predict future prices on the assumption that stock prices are determined by market forces and that history tends to repeat itself (Levy, 1967). Fundamentalists forecast stock prices on the basis of financial analyses of companies or industries. ![]() There are two distinct schools of thought-namely, fundamental analysis and technical analysis-for predicting stock price movements. It would enhance investment flows into stock markets and also be useful for policymakers and regulators in making appropriate decisions and taking corrective measures. If a forecasting model or technique can precisely predict the direction of the market, investment risk and uncertainty can be minimized. The profitability of investments in stock markets highly depends on the predictability of stock movements. Hence, forecasting stock returns can become a challenging task. Movements in stock markets are influenced by several factors, such as macro-economic factors, international events, and human behavior. However, stock markets are characterized by high volatility, dynamism, and complexity (Johnson et al., 2003 Cristelli, 2014 Wieland, 2015). Given the significance of financial markets, forecasting financial returns occupies a paramount position in investment decision making. Theoretical and empirical studies have shown that a positive relationship exists between financial markets and economic growth (e.g., Levine, 1997 Rajan and Zingales, 1998 Rousseau and Watchel, 2000 Beck et al., 2003 Guptha and Rao, 2018). However, traditional linear and nonlinear models outperformed artificial intelligence and frequency domain models in providing accurate forecasts. The results showed that no single model out of the five models could be applied uniformly to all 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. 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. The findings of previous studies indicate that there is no single method that can be applied uniformly to all markets. However, there have been very few studies of groups of stock markets or indices. Numerous empirical studies have employed such methods to investigate the returns of different individual stock indices. There are several forecasting techniques in the literature for obtaining accurate forecasts for investment decision making. Forecasting stock market returns is one of the most effective tools for risk management and portfolio diversification.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |