Smart Beta and Risk Factors Based on Textural Data and Machine Learning

Publication type: Book Chapter
Publication date: 2022-10-31
Springer Nature
SJR: —
CiteScore: —
Impact factor: —
ISSN: 25238221, 2523823X
Abstract
As one of the main sources of data, text plays an important role in various fields. This chapter mainly introduces the application of textural analysis in the financial field. Firstly, we introduce two techniques of text analysis, including natural language processing and Machine Learning/Deep Learning. Secondly, we also introduce factors for finance built on textural dataset analysis, which includes readability, tone and sentiment factors, similarity, semantic, uncertainty, accuracy, and popularity. Through this article, we have explained the importance and potential of textural analysis in finance.
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Zhang Q. T., Beibei L., Xie D. Smart Beta and Risk Factors Based on Textural Data and Machine Learning // Palgrave Studies in Risk and Insurance. 2022. pp. 111-128.
GOST all authors (up to 50) Copy
Zhang Q. T., Beibei L., Xie D. Smart Beta and Risk Factors Based on Textural Data and Machine Learning // Palgrave Studies in Risk and Insurance. 2022. pp. 111-128.
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RIS Copy
TY - GENERIC
DO - 10.1007/978-3-031-11612-4_6
UR - https://doi.org/10.1007/978-3-031-11612-4_6
TI - Smart Beta and Risk Factors Based on Textural Data and Machine Learning
T2 - Palgrave Studies in Risk and Insurance
AU - Zhang, Qingquan Tony
AU - Beibei, Li
AU - Xie, Danxia
PY - 2022
DA - 2022/10/31
PB - Springer Nature
SP - 111-128
SN - 2523-8221
SN - 2523-823X
ER -
BibTex
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BibTex (up to 50 authors) Copy
@incollection{2022_Zhang,
author = {Qingquan Tony Zhang and Li Beibei and Danxia Xie},
title = {Smart Beta and Risk Factors Based on Textural Data and Machine Learning},
publisher = {Springer Nature},
year = {2022},
pages = {111--128},
month = {oct}
}