Credit Rating Prediction Using Financial And Sentiment Data

The credit rating of financial instruments is one of the factors that play a significant role while making investment decisions. Credit rating companies provide information about the credit stability/ transitions (deterioration/ up gradation) of the debt instruments using financial parameters, industry parameters, and external environment at quarterly intervals. In the present scenario, investment managers have to wait for quite some time to know about the credit rating stability/ transition. We propose that incorporating the market news available from different sources in almost real time along with the usual financial parameters should be able to predict the rating stability/ transition well in advance. The present work focus on incorporating sentiment scores extracted from various news sources like new aggregators like Bloomberg, Reuters, Company Web sites, Blogs etc. Depending upon the source of news a weight will be given taking into consideration trust worthiness of the source, Location of the Company information in the body of the news, Frequency of appearance. Using deep learning weights are assigned to each source. A Composite Sentiment score is computed as weighed average of the various individual news sources. This composite score is additional Input along with other financial parameters about eleven to survival analysis which in turn will forecast credit events. Our results show that Return on Capital Employed ROCE and Interest cover ratio along with sentiment scores are the best predictor variables to predict the rating stability/ transition. We tested our methodology on about three hundred companies. Most of them are from mining & textile segment as majority of downgrades and rating transitions have happened in those sectors. Back testing results show that proposed model very well predicts the credit rating stability/ transition. Without using the sentiment score and our weighing scheme the prediction accuracy of a transition was about 45% on hold out Samples (About 30 of 300) and with Financial and sentiment score it went up to about 70%. This work will be very helpful from the regulatory point of view also as one has to compute the Future value of Loan portfolio ie Credit Risk VAR. This method computes the Credit Risk VAR much accurately compared to existing methods of Credit Risk Computation.

 
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Outline/Structure of the Case Study

PPT and Demo of KNIME Work Flow

Learning Outcome

Learn how sentiment and text mining can be used along with conventional models to predict the rating transition of companies and take corrective action well in advance.

At present there is a long delay between rating upgrade/down grade as many technique use only financial data.

Will help to manage the Credit Risk efficiently.

KNIME is used for this case

Target Audience

Any one interested to know the application of Text Mining for a Financial application Pirticulary for Portfolio Managers in Banks Mutual Funds Rating Analysts Investment Bankers

Prerequisites for Attendees

Some amount of brief about Text Mining though not essential

schedule Submitted 1 month ago

Public Feedback

comment Suggest improvements to the Speaker
  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  2 weeks ago
    reply Reply

    Sir, do you happen to have a video of yourself speaking at a different venue or could you please record one introducing the topic for our audience and post it with the proposal. Warm Regards, Vikas

  • Anoop Kulkarni
    By Anoop Kulkarni  ~  4 weeks ago
    reply Reply

    Thanks for your proposal. While the introduction is good, would it be possible for you to share a time breakup and overall organization of your talk? 

    ~anoop

    • Subramanyam Chandrasekhar
      By Subramanyam Chandrasekhar  ~  3 weeks ago
      reply Reply
      Total presentation is for about 45 minutes

      Will spend about fifteen minutes or so for the benefit of incorporating sentiment into rating methodology.

      Brief introduction to challenges in handling the textual data 

      Data preprocessing

      Next fifteen minutes or so

      Model building for rating transition by incorporating sentiment and survival analysis .

      Results discussion

      Next five minutes or so

      Future road map

      Last ten minutes for Q/A. 

      Professor Chandrasekhar

      On Thu, 25 Apr 2019, 16:47 ODSC India 2019, <info@confengine.com> wrote:
      Dear Subramanyam Chandrasekhar,

      Please note that the proposal: Credit Rating Prediction Using Financial And Sentiment Data has received a new comment from Anoop Kulkarni

      Thanks for your proposal. While the introduction is good, would it be possible for you to share a time breakup and overall organization of your talk? 

      ~anoop


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