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A futures pricing model with long-term and short-term traders

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  • Gao, Bin
  • Xie, Jun
  • Jia, Yun

Abstract

This paper proposes a dynamic futures pricing model, analyzes different investors’ sentiment effect, and tries to find an explanation for financial anomalies of market inefficiency. The model focuses on the interaction of different types of investors in futures market, with a vast majority being short-term investors, and shows how this interaction sustains incorrect prices. The major findings are: firstly, in calm situations, short-term sentiment has a greater impact on pricing than long-term sentiment; secondly, in crashes situations, the number of short-term investors decrease, and market efficiency is improved when regulators introduce policies to reduce the number of short-term investors; lastly, the empirical results show that the pricing model in the paper outperforms other models, which is useful to better predict futures price.

Suggested Citation

  • Gao, Bin & Xie, Jun & Jia, Yun, 2019. "A futures pricing model with long-term and short-term traders," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 9-28.
    • Handle: RePEc:eee:reveco:v:64:y:2019:i:c:p:9-28
    DOI: 10.1016/j.iref.2019.05.010
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    References listed on IDEAS

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    Cited by:

    1. Li, Jinfang, 2022. "The sentiment pricing dynamics with short-term and long-term learning," The North American Journal of Economics and Finance, Elsevier, vol. 63(C).
    2. Miwa, Kotaro, 2019. "Trading hours extension and intraday price behavior," International Review of Economics & Finance, Elsevier, vol. 64(C), pages 572-585.
    3. Shu‐Lien Chang & Hsiu‐Chuan Lee & Donald Lien, 2022. "The global latent factor and international index futures returns predictability," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 514-538, April.

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