Modeling financial analysts’ decision making via the pragmatics and semantics of earnings calls
Table 7: Results on the test set for additional models. Comparable to Table 6 in the main document.
['Feature type', 'Features', 'Regression Task Models', 'Regression Task MSE', 'Regression Task  [ITALIC] R2', 'Regression Task % err.', 'Classification Task Models', 'Classification Task Acc.', 'Classification Task F1', 'Classification Task % err.']
For a number of features the correlations are not statistically significant. However, we expand upon the statistically significant results for negative (−) and positive (+) correlations with the bullishness of an analyst: [leftmargin=*] (+) Bullishness and turn order. This suggests bullish analysts tend to be called on earlier in the call and bearish and neutral analysts tend to be called on later in the call which confirms the conclusion of Cohen et al. (+ ) Bullishness and positive sentiment. Bullish analysts tend to ask more positive (less negative) questions and the reverse is true for neutral/bearish analysts. Intuitively, this makes sense since bullish analysts are more favorable towards the firm and thus probably cast the firm in a positive light. (+) Bullishness and entities. Here we find that bullish analysts are slightly more concrete in their questions towards the company and tend to ask more about organizations and people. (−) Bullishness and past predicates. This suggests bearish and neutral analysts tend to talk about the past more. For the prediction task in §5, in addition to ridge regression and logistic regression, we also experimented with Gaussian kernel ridge regression and support vector machines but found they performed worse or similarly.