Final published version, 614 KB, PDF document
Research output: Working paper
Research output: Working paper
}
TY - UNPB
T1 - Popular Music, Sentiment, and Noise Trading
AU - Kaivanto, Kim
AU - Zhang, Peng
PY - 2019/10/31
Y1 - 2019/10/31
N2 - 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.
AB - 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.
KW - investor sentiment
KW - stock-return predictability
KW - big data
KW - textual analysis
KW - natural language processing
KW - popular music
KW - noise trading
KW - behavioural finance
M3 - Working paper
T3 - Economics Working Papers Series
BT - Popular Music, Sentiment, and Noise Trading
PB - Lancaster University, Department of Economics
CY - Lancaster
ER -