Fake News Detection in Financial Markets: Methodology and Capital Market Implications
PI: Dr. Peeyush Taori, HKU
With the advent of Internet and social media, fake news has become a significant topic of interest to researchers and practitioners. Fake news can be broadly classified as false stories with no verifiability and have fabricated or misleading content. With a lot of automated decision making (such as algorithmic trading) using Fintech, there are concerns that such articles can impact companies and capital markets adversely. Issue of fake news in business is a severe problem as it can impact firm valuations and destroy shareholder wealth. While there is some work on fake news in general, there is limited methodology to systematically identify and label articles that can be potentially misleading in the world of finance because of difference in characteristics of business language from mainstream conversations.
In this project, I aim to address two important issues related to fake news:
1) Based on the advancements in Natural Language Processing (NLP) and Artificial Intelligence (AI), and by combining domain knowledge in financial communications, develop and implement a methodology of detecting fake news articles.
2) The dataset of fake news articles generated as a result of the project would be used to study the prevalence of fake articles and their impact on firm valuations, stock prices, and disclosures.