Credit rating transition prediction odsc 2019

Apr 17, 2019Download as PPTX, PDF0 likes62 views

This document presents a study on developing an automated early warning system for credit rating transitions using news sentiment analysis. The objective is to eliminate subjective bias in credit ratings assigned by analysts and provide more real-time ratings. A hybrid model is created using traditional credit rating parameters like financial ratios and economic conditions along with quantified news sentiment scores. The model was backtested on 200 companies and predicted rating transitions with over 95% accuracy, validating the use of sentiment analysis to complement traditional credit rating approaches.

Credit Rating Transition Prediction
Using News Sentiment Analysis
- An Early Warning System
Dr.Chandrasekhar M. Tech(IIT Kanpur), PhD(USA)
Sr Professor Chairman Centre of Excellence – Business Analytics
IFIM Business School
Moovendhan.V M.Tech.
Fellow (Industry), IIM Indore
Accendere Knowledge Management Pvt Ltd

Objective of Study
? To Develop An Automated Credit Rating Transition’s Early Warning
System.
? To Eliminate Subjective Bias Arises During Rating Assignment By Analysts.
? A Cross Validation Of Analyst’s Credit Rating Assignments
? To Gain Insight By Including Quantified News Sentiment Into Credit
Rating Process

? Financial Ratios
? Industry Performance
? Management Background
? Economic Conditions
Issues
? Subjectivity
? Revised once it
quarter/Half yearly
? Delayed /Reactive
information to Investsors
Conventional Credit Rating Models
Parameters

? Near Real time Rating
Reports
? Machine Assigned Rating
? Unbiased
? Automated
? Financial Ratios
? Industry Performance
? Management Background
? Economic Conditions
and
? News Sentiment
Advantages
Sentiment Analysis Based Credit Rating
Parameters

Text and Sentiment Extraction
? Used news from different sources like CMIE
Data base Yahoo Finance Bloomberg
? Used Custom Dictionary
? KNIME was used for Sentiment Extraction
? COX Regression was used for final solution

Model Comparison
Sentiment Score
Financial
Ratios
Economic
ConditionNews
Industry Score
Hybrid Model
(Sentiment + Traditional) Traditional Approach
Unrevised StatusReal Time Status

Study Procedure & Validation
? A Sample of 200 firms from Indian Textile and Metal sectors chosen.
? Sentiment score extracted using news data for the past 7 years for
each of 200 companies.
? Key Financial ratios were extracted for the past 7 years.
? Default estimates were calculated using the hybrid model
? Back Tested with sample of 15 companies.
? Cross validated and compared with agencies actual Credit Rating.
? The Hybrid model predicted the transition with >95% accuracy.

Sentiment Analysis Based Credit Rating
Accuracy and Model Validation
ROCE
Interest
Cover Ratio
Sentiment
Score
Probability of Failure
( May 2015)
Actual Rating by
Agencies
( November 2016) Validity
Aarvee Denims & Exports
Ltd. -2.28 0.65 2 0.742673305 Suspended(
Alicon Castalloy Ltd. 8.59 2.58 4 0.280077323 Turned Positive Matching
Ambika Cotton Mills Ltd. 14.12 11.07 4 0.211573061 Positive reaffirmed Matching
Arvind Ltd. 6.05 2.48 5 0.270786149 Positive reaffirmed(CARE) Matching
Banswara Syntex Ltd. 0.84 1.15 3 0.510486386 Stable to Positive Not matching
Bhagyanagar India Ltd. 0.45 1.25 3 0.543876671 Revised to Negative from stable Matching
Celebrity Fashions Ltd. -4.06 0.54 3 0.250858751
Positive Reaffirmed ( Icra
Suspended) Matching
Century Enka Ltd. 3.58 4.23 3 0.511656501 Stable Matching
Century Textiles & Inds. Ltd. -0.41 0.87 3 0.516952538 Stable Matching
Donear Industries Ltd. 0.8 1.03 3 0.269443632 Stable Not Matching
Ginni Filaments Ltd. 1.17 1.24 3 0.497621675 Stable Matching
Grasim Industries Ltd. 4.29 6.86 4 0.400572968
Improved from Watch
Development(stable) Matching
Hilton Metal Forging Ltd. 1.09 1.68 3 0.283444603 Suspended(2015)
Himatsingka Seide Ltd. 9.41 3.01 3 0.46293452 Stable Matching
Hindalco Industries Ltd. 2.35 1.77 3 0.524795152 stable Matching

Thank You
For More Information Please Contact
chandrasekar@ifimbschool.com

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Credit rating transition prediction odsc 2019

1. Credit Rating Transition Prediction Using News Sentiment Analysis - An Early Warning System Dr.Chandrasekhar M. Tech(IIT Kanpur), PhD(USA) Sr Professor Chairman Centre of Excellence – Business Analytics IFIM Business School Moovendhan.V M.Tech. Fellow (Industry), IIM Indore Accendere Knowledge Management Pvt Ltd

2. Objective of Study ? To Develop An Automated Credit Rating Transition’s Early Warning System. ? To Eliminate Subjective Bias Arises During Rating Assignment By Analysts. ? A Cross Validation Of Analyst’s Credit Rating Assignments ? To Gain Insight By Including Quantified News Sentiment Into Credit Rating Process

3. ? Financial Ratios ? Industry Performance ? Management Background ? Economic Conditions Issues ? Subjectivity ? Revised once it quarter/Half yearly ? Delayed /Reactive information to Investsors Conventional Credit Rating Models Parameters

4. ? Near Real time Rating Reports ? Machine Assigned Rating ? Unbiased ? Automated ? Financial Ratios ? Industry Performance ? Management Background ? Economic Conditions and ? News Sentiment Advantages Sentiment Analysis Based Credit Rating Parameters

5. Text and Sentiment Extraction ? Used news from different sources like CMIE Data base Yahoo Finance Bloomberg ? Used Custom Dictionary ? KNIME was used for Sentiment Extraction ? COX Regression was used for final solution

6. Model Comparison Sentiment Score Financial Ratios Economic ConditionNews Industry Score Hybrid Model (Sentiment + Traditional) Traditional Approach Unrevised StatusReal Time Status

7. Study Procedure & Validation ? A Sample of 200 firms from Indian Textile and Metal sectors chosen. ? Sentiment score extracted using news data for the past 7 years for each of 200 companies. ? Key Financial ratios were extracted for the past 7 years. ? Default estimates were calculated using the hybrid model ? Back Tested with sample of 15 companies. ? Cross validated and compared with agencies actual Credit Rating. ? The Hybrid model predicted the transition with >95% accuracy.

8. Sentiment Analysis Based Credit Rating Accuracy and Model Validation ROCE Interest Cover Ratio Sentiment Score Probability of Failure ( May 2015) Actual Rating by Agencies ( November 2016) Validity Aarvee Denims & Exports Ltd. -2.28 0.65 2 0.742673305 Suspended( Alicon Castalloy Ltd. 8.59 2.58 4 0.280077323 Turned Positive Matching Ambika Cotton Mills Ltd. 14.12 11.07 4 0.211573061 Positive reaffirmed Matching Arvind Ltd. 6.05 2.48 5 0.270786149 Positive reaffirmed(CARE) Matching Banswara Syntex Ltd. 0.84 1.15 3 0.510486386 Stable to Positive Not matching Bhagyanagar India Ltd. 0.45 1.25 3 0.543876671 Revised to Negative from stable Matching Celebrity Fashions Ltd. -4.06 0.54 3 0.250858751 Positive Reaffirmed ( Icra Suspended) Matching Century Enka Ltd. 3.58 4.23 3 0.511656501 Stable Matching Century Textiles & Inds. Ltd. -0.41 0.87 3 0.516952538 Stable Matching Donear Industries Ltd. 0.8 1.03 3 0.269443632 Stable Not Matching Ginni Filaments Ltd. 1.17 1.24 3 0.497621675 Stable Matching Grasim Industries Ltd. 4.29 6.86 4 0.400572968 Improved from Watch Development(stable) Matching Hilton Metal Forging Ltd. 1.09 1.68 3 0.283444603 Suspended(2015) Himatsingka Seide Ltd. 9.41 3.01 3 0.46293452 Stable Matching Hindalco Industries Ltd. 2.35 1.77 3 0.524795152 stable Matching

9. Thank You For More Information Please Contact chandrasekar@ifimbschool.com

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