Abstract
This paper provides evidence that forecasts based on global stock returns transmission yield better returns in day trading, for both developed and emerging stock markets. The study investigates the performance of global stock market price transmission information in forecasting stock prices using support vector regression for six global markets—USA (Dow Jones, S&P500), UK (FTSE-100), India (NSE), Singapore (SGX), Hong Kong (Hang Seng) and China (Shanghai Stock Exchange) over the period 1999–2011. The empirical analysis shows that models with other global market price information 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 (57.69), 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 opening price (55.22), and Nifty was found to be the most predictable stock index. Trading using global cues-based forecast model generates greater returns than other models in all the markets. The study provides evidence that stock markets across the globe are integrated and the information on price transmission across markets, including emerging markets, can induce better returns in day trading.
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Acknowledgments
We thank the anonymous referees and the discussant’s critical comments on an earlier version of this paper presented at the European Financial Management Association Annual Conference, Henley Business School, United Kingdom, June 26–29, 2013. We also acknowledge the suggestions given by the Conference participants, for improving the paper. We are also grateful to the referees of the journal for their valuable suggestions to improve this paper.
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Appendix
The daily closing price plot and the corresponding daily closing return plots of SCI, Nifty, HSI, STI, FTSE, S&P500 and DJIA are shown below (Figs. 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15).
The closing price plots clearly reveal the impact of the global financial crisis during 2008–2009 in all the markets. Moreover, we also find a more cyclical trend in the index prices of the developed markets (UK, USA) compared to a linear increasing trend in the emerging markets (China, India). Thus, the data used covers periods of varying trends, and testing the models with rolling samples, prove that the forecast model developed is robust across different economic cycles.
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Thenmozhi, M., Sarath Chand, G. Forecasting stock returns based on information transmission across global markets using support vector machines. Neural Comput & Applic 27, 805–824 (2016). https://doi.org/10.1007/s00521-015-1897-9
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DOI: https://doi.org/10.1007/s00521-015-1897-9