Chandrasekhar Subramanyam

Chandrasekhar Subramanyam

Sr Professor and Director Business Analytics Center, IFIM Business School


Dr.S.Chandrasekhar is currently Sr Professor at IFIM Business school B”Lore & also Director-Business Analytics Centre at IFIM. Prior to joining IFIM he was Professor & also Officiated as Director( during April 2009-Jan 2010) at FORE School of Management, New Delhi. Also worked for about ten years at IIM Lucknow as Professor, Chair Quantitative & Information Systems Group. Total of about forty years of experience in Research , Teaching & consulting. Holds a B Tech, from IIT Kanpur & Doctorate from University of Georgia. Published in International and National journals , Regular speaker at Various Conferences both with in and outside the country. Fellow of Institution of Engineers , Institution of Electronics and Telecommunication Engineers , Institute of Pattern recognition Society.

Utilization of Market News for improving the decision making process in Financial sector.
With the popularity of Digital Media large volume of Information about the Company,Industry are available from various sources some paid and unpaid like Bloomberg,Revenpack,News Agencies , Analyst reports , Company Web sites , Blogs , etc. All this is not used for decision making. Majority of these data is in the form of Text , Viedeo , Images etc.. They are also collectively known as unstructured data as they do not comply with standard definition of Data base. Sentiment Analysis is a type of Unstructured data analysis. It is a combination of Natural Language Processing , Statistics & Machine Learning to identify and extract subjective information from text.

This information can be effectively utilised to improve the decision making by extracting sentiment from large volume of such data and combined with other Machine Learning techniques such as Decision Trees, Support Vector Machines and many more techniques. Two use cases will demonstrate how this can be implemented to predict the Rating Transition of Financial instruments and predicting the Enterprise value of companies using real life data. Also talks about some of the challenges involved in data Integration

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