| dc.description.abstract | Financial sector is expected to be at the forefront of the adoption of machine learning methods, driven by the superior performance of the data-driven approaches over traditional modelling approaches. There has been a widespread interest in automatically extracting information from financial news flow as the signals might be useful for investment decisions. While quantitative finance focuses on analysis of structured financial data for investment decisions, the potential of utilizing unstructured news flow in decision making is not fully tapped. Research in financial news analytics tries to address this gap by detecting events and aspects that provide buy, sell or hold information in news, commonly interpreted as financial sentiments. In this paper, we develop a framework utilizing information theoretic concepts and machine learning methods that understands the context and is capable of extracting buy, sell or hold information contained within news headlines. The proposed framework is also capable of detecting conflicting sentiments on multiple companies within the same news headline, which to our best knowledge has not been studied earlier. Further, we develop an information system which analyzes the news flow in real-time, allowing users to track financial sentiments by company, sector and index via a dashboard. Through this study we make three dataset related contributions - firstly, a training dataset consisting of more than 12,000 news headlines annotated for entities and their relevant financial sentiments by multiple annotators, secondly, an entity database of over 1,000 financial and economic entities relevant to Indian economy and their forms of appearance in news media amounting to over 5,000 phrases and thirdly, make improvements in existing financial dictionaries. Using the proposed system, we study the effect of the information derived from daily news flow in the years 2012 to 2017, over the Indian broad market equity index NSE 500, and show that the information has predictive value. | en_US |
