Natural language based financial forecasting: a survey
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10462_2017_9588_ReferencePDF.pdf
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1.31 MB
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Author(s)
Cambria, Erik
Xing, Frank Z.
Welsch, Roy E
Date Issued
October 2017
Journal
Artificial Intelligence Review
Publisher
Springer Netherlands
Citation
Xing, Frank Z., et al. “Natural Language Based Financial Forecasting: A Survey.” Artificial Intelligence Review, vol. 50, no. 1, June 2018, pp. 49–73.
Version
Author's final manuscript
Abstract
Natural language processing (NLP), or the pragmatic research perspective of computational linguistics, has become increasingly powerful due to data availability and various techniques developed in the past decade. This increasing capability makes it possible to capture sentiments more accurately and semantics in a more nuanced way. Naturally, many applications are starting to seek improvements by adopting cutting-edge NLP techniques. Financial forecasting is no exception. As a result, articles that leverage NLP techniques to predict financial markets are fast accumulating, gradually establishing the research field of natural language based financial forecasting (NLFF), or from the application perspective, stock market prediction. This review article clarifies the scope of NLFF research by ordering and structuring techniques and applications from related work. The survey also aims to increase the understanding of progress and hotspots in NLFF, and bring about discussions across many different disciplines. Keywords: Financial forecasting, Natural language processing, Text mining Predictive analytics, Knowledge engineering , Computational finance
MIT Department
Sloan School of Management
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s10462-017-9588-9
