Analysis shows optimistic MD&A tone improves investment efficiency in non-state owned firms, suggesting greater text quality matters.
Purpose In the context of Chinese listed firms, this study aims to test the relationship between a firm’s Management Discussions and Analysis (MD&A) forward-looking tone with its subsequent investment efficiency. Design/methodology/approach This study constructed a sentiment dictionary on the basis of the BosonNLP semantic sentiment dictionary, negative word dictionary and degree adverb dictionary. Findings The main findings show that firms with more optimistic MD&A tone tend to present higher subsequent investment efficiencies. This study further finds that this positive link is stronger in firms that disclose higher text quality of MD&A, is non-state owned and has fewer common owners. This study also discovers that, compared to their rival firms, firms with more optimistic MD&A tone show higher investment efficiency. However, their investment efficiency is not affected by rival firms’ MD&A tone. Research limitations/implications First, the empirical results should be carefully interpreted as they can only infer association instead of causality between forward-looking MD&A tones and subsequent investment efficiencies. Second, previous studies have criticised the low power of dictionary-based tone measures (Li, 2010). Therefore, similar to other studies that apply natural language text analysis techniques, the selection of MD&A tone measures and their reliability may affect empirical results. Finally, in the depth in this paper, this study did not further examine the effect of managerial characteristics and corporate governance mechanisms on the relationship between forward-looking MD&A tones and subsequent investment efficiencies. Originality/value Few studies have identified the relationship between firms’ forward-looking textual disclosures and their investment efficiency. The focus on MD&A tone provides an interesting yet underexplored perspective to the current literature.
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Chenggang Li (2025) studied this question.
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