Template-Type: ReDIF-Paper 1.0
Author-Name: Kim Kaivanto
Author-Name-First: Kim
Author-Name-Last: Kaivanto
Author-Name: Peng Zhang
Author-Name-First: Peng
Author-Name-Last: Zhang
Title: Popular Music, Sentiment, and Noise Trading
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.
Creation-Date: 2019
File-URL: http://www.lancaster.ac.uk/media/lancaster-university/content-assets/documents/lums/economics/working-papers/LancasterWP2019_020.pdf
File-Format: application/pdf
Number: 279326509
Classification-JEL: G12, G17, C55
Keywords: investor sentiment, stock-return predictability, big data, textual analysis, natural language processing, popular music, noise trading, behavioural finance
Handle: RePEc:lan:wpaper:279326509