Popular Music, Sentiment, and Noise Trading

Kim Kaivanto and Peng Zhang

No 279326509, Working Papers from Lancaster University Management School, Economics Department

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.

Keywords: investor sentiment; stock-return predictability; big data; textual analysis; natural language processing; popular music; noise trading; behavioural finance (search for similar items in EconPapers)
JEL-codes: C55 G12 G17 (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-big
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Citations: View citations in EconPapers (1)

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