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Popular Music, Sentiment, and Noise Trading

Author

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  • Kim Kaivanto
  • Peng Zhang

Abstract

We construct a sentiment indicator as the first principal component of thirteen emotion metrics derived from the lyrics and composition of music-chart singles. This indicator performs well, dominating the Michigan Index of Consumer Sentiment and bettering the Baker-Wurgler index in long-horizon regression tests as well as in out-of-sample forecasting tests. The music-sentiment indicator captures both signal and noise. The part associated with fundamentals predicts more distant market returns positively. The second part is orthogonal to fundamentals, and predicts one-month-ahead market returns negatively. This is evidence of noise trading explained by the emotive content of popular music.

Suggested Citation

  • Kim Kaivanto & Peng Zhang, 2019. "Popular Music, Sentiment, and Noise Trading," Working Papers 279326509, Lancaster University Management School, Economics Department.
  • Handle: RePEc:lan:wpaper:279326509
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    File URL: http://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/lums/economics/working-papers/LancasterWP2019_020.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    investor sentiment; stock-return predictability; big data; textual analysis; natural language processing; popular music; noise trading; behavioural finance;

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis

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