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- W3118686483 abstract "When quantifying qualitative information from unstructured textual data, traditional bag-of-words approaches capture only semantic features of single words/phrases. The context, the sequence of words, and the relations among words (i.e., higher-order interaction features) are ignored. We introduce deep neural networks (NNs) to encode and mimic human intelligence in processing natural language. Using the NN-based artificial intelligence, we construct a new sentiment measure that is specific to performance discussions and is adjusted for complex contextual negations. We find that this performance-specific sentiment explains cross-sectional returns and future operating performance better than umbrella sentiment proxies used in the literature." @default.
- W3118686483 created "2021-01-18" @default.
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- W3118686483 date "2021-01-01" @default.
- W3118686483 modified "2023-09-28" @default.
- W3118686483 title "Power of deep learning: Quantifying language to explain cross-sectional returns" @default.
- W3118686483 hasPublicationYear "2021" @default.
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