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Neural Computing and Applications

, Volume 27, Issue 4, pp 805–824 | Cite as

Forecasting stock returns based on information transmission across global markets using support vector machines

  • M. Thenmozhi
  • G. Sarath Chand
Original Article

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.

Keywords

Forecasting Support vector machine Stock returns Information transmission Global markets 

Notes

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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Copyright information

© The Natural Computing Applications Forum 2015

Authors and Affiliations

  1. 1.Department of Management StudiesIndian Institute of Technology MadrasChennaiIndia

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