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The complementary role of cross-sectional and time-series information in forecasting stock returns

Author

Listed:
  • Qing Zhou

    (UQ Business School, The University of Queensland, Australia; School of Management, Xi’an Jiaotong University, China)

  • Robert Faff

    (UQ Business School, The University of Queensland, Australia; Department of Accounting and Finance, University of Strathclyde, UK)

Abstract

While linear time-series models, technical analysis, and momentum models all extract information from past market data, they each interpret data differently. We test the informative role of three representative models and examine the trading performance of a combined forecasting model at the individual stock level. Our results indicate that these models all contain marginal information and complement each other. The combined trading model captures higher upward trending returns and provides the same downward trending returns compared with the buy-and-hold strategy.

Suggested Citation

  • Qing Zhou & Robert Faff, 2017. "The complementary role of cross-sectional and time-series information in forecasting stock returns," Australian Journal of Management, Australian School of Business, vol. 42(1), pages 113-139, February.
    • Handle: RePEc:sae:ausman:v:42:y:2017:i:1:p:113-139
    DOI: 10.1177/0312896215575888
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    More about this item

    Keywords

    Combination; complementarity; forecasting; out-of-sample; stock returns;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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