Neural Network Translated into Bag-of-Words: Lexicon of Attentions

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2021
Authors
Iwasaki, Hitoshi
Chen, Ying
Huang, Allen
Wang, Hui
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We present a framework that translates trained neural network's decision making process to a lexicon. First, we train an interpretable neural network, hierarchical neural network (HAN) that predicts cumulative abnormal returns (CAR) with analyst reports. Second, we relate the trained attentions with words and compile the analytical results as a parsimonious lexicon. The attention-based lexicon reflects contextual information, and through out-of-sample experiments, we show that it outperforms as well as complements with Loughran and McDonald (LM) lexicon, and also offers a smart weighting scheme that dominates existing word weighting methods. Additional experiments confirm the advantage of the proposed lexicon for earnings call transcripts and generalize its usefulness beyond the original training corpus. Our proposed framework materializes contextual information in financial texts and allows bag-of-words models to incorporate it, and thus it provides subsequent users with a way of exploring contextual information in an interpretable manner.
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Textual Analysis, Attention, Information Content
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