Power of deep learning: Quantifying language to explain cross-sectional returns
Power of deep learning: Quantifying language to explain cross-sectional returns
dc.contributor.author | Cao, Sean | |
dc.contributor.author | Kim, Yongtae | |
dc.contributor.author | Wang, Angie | |
dc.contributor.author | Xiao, Houping | |
dc.date.accessioned | 2021-11-12T18:48:28Z | |
dc.date.available | 2021-11-12T18:48:28Z | |
dc.date.issued | 2021 | |
dc.description.abstract | When quantifying information from unstructured textual data, the traditional bag-of-words approach only captures semantic features of single words or phrases. The context, the sequence of words, and the relationship between words are ignored. This paper introduces a novel approach to incorporate complex syntactical featuresin the textual analysis using machine learning (i.e., neural-network-based natural language parser and word embedding). We construct a new measure of sentiment 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 the umbrella sentiment proxies used in the literature | |
dc.identifier.uri | http://hdl.handle.net/10125/76989 | |
dc.subject | textual analysis | |
dc.subject | machine learning | |
dc.subject | neural networks | |
dc.subject | natural language processing | |
dc.subject | sentiment analysis | |
dc.subject | conference calls | |
dc.title | Power of deep learning: Quantifying language to explain cross-sectional returns | |
dc.type.dcmi | Text |
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