Do Global stock market cues matter in forecasting stock returns in developed and developing markets?

Financial markets all over the world have witnessed growing integration within as well as across boundaries, spurred by deregulation, globalization and advances in information technology. However, none of the researches have investigated the trading profitability of models that employed the financial market integration information as input variables especially in the case of day trading. Moreover traditional methods employed linear correlation techniques to study the market integration though it is strongly believed that their relationship could be nonlinear. This paper examines the usefulness of international stock market price transmission information (global cues) in day trading in developed and emerging stock markets. The study investigates the performance of global stock market cues in forecasting stock prices using Support Vector Regression for seven global markets – US (Dow Jones Industrial Average, S&P500), UK (FTSE -100), India (Nifty), Singapore (Straits Times Index), Hong Kong (Hang Seng Index), China (Shanghai Stock Composite) over the period 1999-2011. The empirical analysis shows that hit ratio of the models with other market cues outperform forecast models based merely on Auto-regressive past lags and technical indicators. Shanghai stock index movement was predicted best by Hang Seng Index opening price with a hit ratio of 57.69, while, Hang Seng Index by previous day’s S&P500 closing price (54.34), FTSE by previous day’s S&P500 closing price (57.94), Straits Times Index by previous day’s Dow Jones closing price (54.44), Nifty by HSI opening price (60), S&P500 by STI closing price (55.31) and DJIA by HSI closing price (55.22) and Nifty was found to be the most predictable stock index. The study provides evidence that stock markets across the globe are integrated and that information on price transmissions across markets can induce arbitrage opportunities even in day trading.