The role of textual data has been widely explored in the finance area. The measures include readability, sentiment and similarly etc. In the online crowdfunding market, Gao and Lin examine whether linguistic styles of texts can help mitigate issues of information asymmetry, and more importantly, whether investors can “correctly” interpret the economic value of texts. Using data from online debt crowdfunding, The first finding is that investors indeed take into account the “loan purpose” descriptions that borrowers provide in their loan requests, even though these texts are not verified or legally binding. Further analysis shows that the linguistic features of these descriptions, and show that well-established features related to creditworthiness (readability, objectivity, negativity, and deception cues) all meaningfully relate to loan repayment. Interestingly, however, investors do not correctly interpret the economic values of all linguistic features, most notably deception cues. Finally, the result shows that these automatically extracted features can improve the predictive accuracy of loan defaults. This suggests that even though “texts” are often considered “soft” or “non-standard” information in finance, it can be quantified and standardized into credit risk modelling.
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