The relative importance of textual indexes in predicting the future performance of banks: A connection weight approach

Abstract

We employ a neural network approach to predict banks' future performance with the help of textual information and financial data. Neural network models are considered black boxes because they do not help explain the role of each input variable in predicting the output variable. Therefore, the study uses the connection weight approach to identify the relative importance of textual indexes to represent the sentiments of management in the annual reports of 62 banks from 2007 to 2018. The results of the connection weight approach suggest that several textual indexes are very important for predicting the future financial performance of banks. This study is the first of its kind by suggesting that a better predictive model (neural network) needs to be built not only on bank-level financial variables but also include textual information, which is very informative for performance prediction.

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