Knowledge-Driven Approaches for Financial News Analytics

Bikesh Upreti, Philipp Back, Pekka Malo, Oskar Ahlgren, Ankur Sinha

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

Computational finance is one of the fastest-growing application areas for natural language processing technologies. Already today, algorithmic trading funds are successfully using robo readers and sentiment analysis techniques to support adaptive algorithms that are capable of making automated decisions with little or no human intervention. However, these technologies are still in a nascent state and the competition to improve approaches within the industry is fierce. In this chapter, we discuss financial news analytics and learning strategies that help machines combine domain knowledge with other linguistic information that is extracted from text sources. We provide an overview of existing linguistic resources and methodological approaches that can be readily utilized to develop knowledge-driven solutions for financial news analysis.
Original languageEnglish
Title of host publicationNetwork Theory and Agent-Based Modeling in Economics and Finance
Pages375-404
ISBN (Electronic)978-981-13-8319-9
DOIs
Publication statusPublished - 24 Oct 2019
MoE publication typeA3 Part of a book or another research book

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