15 May 2020

RESEARCH -Introduction to Natural Language Processing (NLP)


Specific to systematic investing, traditional market and factor data are typically structured in numerical terms and are relatively simple to use within the machine or deep learning models. However, despite the abundance of rich textual data gleaned from financial news, earnings reports, and transcripts and their correlation to markets, currently, quantitative managers rarely exploit this highly changing text data. Why? We take a dive into NLP techniques and their integration methods here at RAM in the following Q&A.

Take a first glance on how we apply NLP techniques to our models – A Q&A with RAM’s Data Scientist, Tian Guo.


A NLP based framework for ESG Integration -  A deeper dive

Incorporating ESG considerations into systematic investments has drawn significant attention recently. In this paper, we focus on the ESG events in financial newsflow and explore the predictive powers of ESG related financial news on stock volatility. In particular, we develop a pipeline of ESG news extraction, news representations, and Bayesian inference of deep learning models. Experimental evaluation on real data and different markets demonstrate the superior predictive performance as well as the relation of high volatility predictions of stocks with potentially high risk and low returns. We also show the prospect of the proposed pipeline as a flexible predictive framework for various textual data and target variables.

Access to our latest Academic Research Paper, is available HERE

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