Stock2Vec: An Embedding to Improve Predictive Models for Companies
Description:
Building predictive models for companies often relies on inference using historical data of companies in the same industry sector. However, companies are similar across a variety of dimensions that should be leveraged in relevant prediction problems. This is particularly true for large, complex organizations which may not be well defined by a single industry and have no clear peers. To enable prediction using company information across a variety of dimensions, we create an embedding of company …
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Date:
June 2022
Creator:
Yi, Ziruo; Xiao, Ting; Kaz-Onyeakazi, Ijeoma; Ratnam, Cheran; Medeiros, Theophilus; Nelson, Phillip et al.
Item Type:
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Partner:
UNT College of Information