Sentiment-semantic word vectors: A new method to estimate management sentiment

Tri Minh Phan ()
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Tri Minh Phan: University of St. Gallen

Swiss Journal of Economics and Statistics, 2024, vol. 160, issue 1, 1-22

Abstract: Abstract This paper introduces a novel method to extract the sentiment embedded in the Management’s Discussion and Analysis (MD &A) section of 10-K filings. The proposed method outperforms traditional approaches in terms of sentiment classification accuracy. Utilizing this method, the MD &A sentiment is found to be a strong negative predictor of future stock returns, demonstrating consistency in both in-sample and out-of-sample settings. By contrast, if traditional sentiment extraction methods are used, the MD &A sentiment exhibits no predictive ability for stock markets. Additionally, the MD &A sentiment is associated with dividend-related macroeconomic channels regarding future stock return prediction.

Keywords: Knowledge distillation; MD& A; Stock return predictability; Word2Vec (search for similar items in EconPapers)
JEL-codes: G12 G17 J53 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1186/s41937-024-00126-1

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