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• W2896772983 abstract "We propose an innovative and forward-looking method to examine investor herd behavior and identify fundamental-driven spurious herding. A sentence-based sentiment analysis approach is conceived to automatically extract attitudes or emotions from textual documents. Then we build a mathematical model to discern herd behavior by exploiting the sentiment indices. Empirical tests in the Chinese stock market indicate that there exists significant true herd behavior among blue-chips. Investors with pessimistic sentiment are more likely to herd than those with optimistic sentiment. Moreover, macroeconomic information tends to play a dominant role in the decision-making processes, and the lack of rapid and efficient firm-specific information enables people to herd around the market consensus." @default.
• W2896772983 created "2018-10-26" @default.
• W2896772983 creator A5046472188 @default.
• W2896772983 creator A5053323823 @default.
• W2896772983 date "2018-01-01" @default.
• W2896772983 modified "2023-10-15" @default.
• W2896772983 title "An Innovative Sentiment Analysis to Measure Herd Behavior" @default.
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• W2896772983 doi "https://doi.org/10.1109/tsmc.2018.2864942" @default.
• W2896772983 hasPublicationYear "2018" @default.
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