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Do economic statistics contain information to predict stock indexes futures prices and returns? Evidence from Asian equity futures markets

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Abstract

This research examines the impact of local and international market factors on the pricing of stock indexes futures in East Asian countries. The purpose of this paper is to present a study of the significant factors that determine the major stock indexes futures’ prices of Hong Kong, Malaysia, Singapore, South Korea and Taiwan. This study first investigates the relationships between Hang Seng Index Futures, KLCI Futures, SiMSCI Futures, KOSPI Futures, Taiwan Exchange Index Futures and local interest rates, dividend yields, local exchange rates, overnight S&P500 index and a newly constructed index, Asian Tigers Malaysia Index (ATMI). 11 years historical data of stock indexes futures and the economic statistics are studied; 10 years in-sample data are used for testing and developing the pricing models, and 1 year out-of-sample data is used for the purpose of verifying the predicted values of the stock indexes futures. Using simple linear regressions, local interest rates, dividend yields, exchange rates, overnight S&P500 and ATMI are found to have significant impact on these futures contracts. In this research, the next period close is predicted using simple linear regression and non-linear artificial neural network (ANN). An examination of the prediction results using nonlinear autoregressive ANN with exogenous inputs (NARX) shows significant abnormal returns above the passive threshold buy and hold market returns and also above the profits of simple linear regression (SLR). The empirical evidence of this research suggests that economic statistics contain information which can be extracted using a hybrid SLR and NARX trading model to predict futures prices with some degree of confidence for a year forward. This justifies further research and development of pricing models using fundamentally significant economic determinants to predict futures prices.

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Notes

  1. Levenberg-Marquadrt Function for use with Matlab [Internet]. MathWorks. 2014 can be found on http://www.mathworks.com/help/nnet/ref/trainlm.html.

  2. It is estimated that the mean transaction cost for financial and commodity futures ranges from 0.0004 to 0.033% of the nominal value per contract for each round-trip trade (Locke and Venkatesh 1997). Szakmary and Sharma (2010) treat transactions costs as a percent of contract value and use the rate of 0.05% per side easily encompasses ‘all in’ transaction cost based on the percent of transaction (contract value). Due to the tough competition in the futures brokerage business, the rate charged for customers vary greatly and it is not likely to track the exact variations from the past (Szakmary and Sharma 2010) This rate is much higher than what is reported in Locke and Venkatesh 1997 as we presumed it inclusive of all exchange fees, clearing fees, and broker’s commission.

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Acknowledgements

The authors thank the anonymous reviewer for the sharp insights and observations on the initial paper which was an inferior reflection of this improved paper. The authors appreciate all the kind helpful comments made by the reviewer. The authors maintain that all lacking information are the authors’ weaknesses. The authors also wish to thank all who had given inputs to this research at the 13th NCTU Conference on January, 10, 2020 where the first draft of this paper was presented. The authors also wish to thank Kevin Ng and Foong Cheng Ho for helping us to prepare the slides and materials on this research for the 13th NCTU Conference.

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Authors and Affiliations

  1. Finance and Banking Department, Faculty of Business and Accountancy, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Jacinta Chan Phooi M’ng & Ham Yi Jer

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  1. Jacinta Chan Phooi M’ng
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  2. Ham Yi Jer
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Correspondence to Jacinta Chan Phooi M’ng.

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Appendices

Appendix 1

Correlations Analyses.

See Tables 8 and 9.

Table 8 Correlations between stock indices futures and Economic Factors
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Table 9 Correlations between regression variables
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Appendix 2

Analysis of trading performance results.

See Tables 10 and 11.

Table 10 Tests of paired mean returns
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Table 11 Regression results
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Appendix 3

Percentage profitable of trading performance.

$$Percentage Profitable = \frac{Number\, of\, Winning\, Trades}{{Number\, of\, Total\, Trades}}$$
(13)

See Table 12.

Table 12 Percentage profitable metric of NARX and SLR models
Full size table

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Chan Phooi M’ng, J., Ham Yi Jer Do economic statistics contain information to predict stock indexes futures prices and returns? Evidence from Asian equity futures markets. Rev Quant Finan Acc 57, 1033–1060 (2021). https://doi.org/10.1007/s11156-021-00969-2

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