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The Long of It: Odds that Investor Sentiment Spuriously Predicts Anomaly Returns

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  • Robert F. Stambaugh
  • Jianfeng Yu
  • Yu Yuan

Abstract

Extremely long odds accompany the chance that spurious-regression bias accounts for investor sentiment's observed role in stock-return anomalies. We replace investor sentiment with a simulated persistent series in regressions reported by Stambaugh, Yu and Yuan (2012), who find higher long-short anomaly profits following high sentiment, due entirely to the short leg. Among 200 million simulated regressors, we find none that support those conclusions as strongly as investor sentiment. The key is consistency across anomalies. Obtaining just the predicted signs for the regression coefficients across the 11 anomalies examined in the above study occurs only once for every 43 simulated regressors.

Suggested Citation

  • Robert F. Stambaugh & Jianfeng Yu & Yu Yuan, 2012. "The Long of It: Odds that Investor Sentiment Spuriously Predicts Anomaly Returns," NBER Working Papers 18231, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:18231
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    References listed on IDEAS

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    Cited by:

    1. Papapostolou, Nikos C. & Pouliasis, Panos K. & Nomikos, Nikos K. & Kyriakou, Ioannis, 2016. "Shipping investor sentiment and international stock return predictability," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 96(C), pages 81-94.
    2. repec:eee:ecofin:v:42:y:2017:i:c:p:504-512 is not listed on IDEAS
    3. Yang, Chunpeng & Zhou, Liyun, 2015. "Investor trading behavior, investor sentiment and asset prices," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 42-62.
    4. Jacobs, Heiko, 2016. "Market maturity and mispricing," Journal of Financial Economics, Elsevier, vol. 122(2), pages 270-287.
    5. Robert F. Stambaugh & Yu Yuan, 2015. "Mispricing Factors," NBER Working Papers 21533, National Bureau of Economic Research, Inc.
    6. Bucher, Melk C., 2017. "Investor Attention and Sentiment: Risk or Anomaly?," Working Papers on Finance 1712, University of St. Gallen, School of Finance.
    7. Xiaomeng Lu & Robert F. Stambaugh & Yu Yuan, 2017. "Anomalies Abroad: Beyond Data Mining," NBER Working Papers 23809, National Bureau of Economic Research, Inc.
    8. Labidi, Chiraz & Yaakoubi, Soumaya, 2016. "Investor sentiment and aggregate volatility pricing," The Quarterly Review of Economics and Finance, Elsevier, vol. 61(C), pages 53-63.
    9. Jacobs, Heiko, 2015. "What explains the dynamics of 100 anomalies?," Journal of Banking & Finance, Elsevier, vol. 57(C), pages 65-85.
    10. repec:kap:rqfnac:v:50:y:2018:i:1:d:10.1007_s11156-017-0628-y is not listed on IDEAS
    11. Yang, Chunpeng & Zhou, Liyun, 2016. "Individual stock crowded trades, individual stock investor sentiment and excess returns," The North American Journal of Economics and Finance, Elsevier, vol. 38(C), pages 39-53.
    12. Zheng, Yao, 2015. "The linkage between aggregate investor sentiment and metal futures returns: A nonlinear approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 58(C), pages 128-142.
    13. Sibley, Steven E. & Wang, Yanchu & Xing, Yuhang & Zhang, Xiaoyan, 2016. "The information content of the sentiment index," Journal of Banking & Finance, Elsevier, vol. 62(C), pages 164-179.
    14. repec:eee:finlet:v:22:y:2017:i:c:p:1-4 is not listed on IDEAS

    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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