Language and Domain Specificity: A Chinese Financial Sentiment Dictionary*

The effects of analyst-country institutions on biased research: Evidence from target prices

Zijia Du, Alan Guoming Huang, Russ Wermers and Wenfeng Wu

Review of Finance, 2022, vol. 26, issue 3, 673-719

Abstract: We use Word2vec to develop a financial sentiment dictionary from 3.1 million Chinese-language financial news articles. Our dictionary maps semantically similar words to a subset of human-expert generated financial sentiment words. In validation tests, our dictionary scores the sentiment of articles consistently with human reading of full articles. In return association tests, our dictionary outperforms and subsumes previous Chinese financial sentiment dictionaries, such as direct translations of Loughran and McDonald’s (2011, Journal of Finance, 66, 35–65) English-language financial dictionary. We also generate a list of politically related positive words that is unique to China; we find that this list has a weaker association with returns than does the list of other positive words. We demonstrate that state media uses more politically related positive and fewer negative words, and exhibits a sentiment bias. This bias renders the state media’s sentiment as less return-informative. Our findings demonstrate that dictionary-based sentiment analysis exhibits strong language and domain specificity.

Keywords: Financial word dictionary; Chinese; Sentiment analysis; Political words; Financial news (search for similar items in EconPapers)
JEL-codes: G10 G12 G14 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (7)

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