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Language and Domain Specificity: A Chinese Financial Sentiment Dictionary
[The effects of analyst-country institutions on biased research: Evidence from target prices]

Author

Listed:
  • Zijia Du
  • Alan Guoming Huang
  • Russ Wermers
  • Wenfeng Wu

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.

Suggested Citation

  • Zijia Du & Alan Guoming Huang & Russ Wermers & Wenfeng Wu, 2022. "Language and Domain Specificity: A Chinese Financial Sentiment Dictionary [The effects of analyst-country institutions on biased research: Evidence from target prices]," Review of Finance, European Finance Association, vol. 26(3), pages 673-719.
    • Handle: RePEc:oup:revfin:v:26:y:2022:i:3:p:673-719.
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    References listed on IDEAS

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    Cited by:

    1. Mikhail Stolbov & Maria Shchepeleva, 2023. "Sentiment-based indicators of real estate market stress and systemic risk: international evidence," Annals of Finance, Springer, vol. 19(3), pages 355-382, September.
    2. Li, Jing & Li, Nan & Xia, Tongshui & Guo, Jinjin, 2023. "Textual analysis and detection of financial fraud: Evidence from Chinese manufacturing firms," Economic Modelling, Elsevier, vol. 126(C).
    3. Stolbov, Mikhail & Shchepeleva, Maria & Karminsky, Alexander, 2022. "When central bank research meets Google search: A sentiment index of global financial stress," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 81(C).
    4. Li, Guowen & Wang, Shuai & Feng, Yuyao, 2024. "Making differences work: Financial fraud detection based on multi-subject perceptions," Emerging Markets Review, Elsevier, vol. 60(C).
    5. Durand, Robert B. & Khuu, Joyce & Smales, Lee A., 2023. "Lost in translation. When sentiment metrics for one market are derived from two different languages," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    6. Chu, Xiaojun & Wan, Xinmin & Qiu, Jianying, 2023. "The relative importance of overnight sentiment versus trading-hour sentiment in volatility forecasting," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).

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    More about this item

    Keywords

    Financial word dictionary; Chinese; Sentiment analysis; Political words; Financial news;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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