Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.



Published on

Jesper Andersen's talk on Freerisk at the NEXT6 Conference.

  • Be the first to comment

  • Be the first to like this


  1. 1. I Just Don’t Trust You: Can Engineers Change Credit Follow us on Twitter: NEXT #6 @jandersen, @kiwitobes
  2. 2. “The story of the credit rating agencies is a colossal story of failure.” 2
  3. 3. Freerisk is + A commons approach to credit risk + An open financial-data source + A user-contributed financial data source + A set of contributed rating algorithms
  4. 4. Risk metrics will be commons
  5. 5. Moody’s Standard & Poor’s Fitch PROTECTION Mutual Funds Securitized Bonds Government Government Bonds Investing Corporate Bonds Retirement Accounts Structured Debt Pension Funds
  6. 6. There are two superpowers in the world today in my opinion. There’s the United States and there’s Moody’s Bond Rating Service... And believe me, it’s not clear sometimes who’s more powerful. –Thomas Friedman, 1996 6
  7. 7. Structural problems beget moral failures
  8. 8. “AA” “AAA” “AA” Who would you hire to rate your bonds?
  9. 9. This kind of bribery is completely legal
  10. 10. Payments Create Bad Ratings
  11. 11. NRSRAs Enjoy Patent-Like Protection Without Patent-Like Disclosure
  12. 12. Today’s rating action follows the collapse in market confidence in the firm, and Lehman’s announcement that it was filing for Chapter 11 bankruptcy protection after its failure to reach a merger agreement with a stronger strategic partner. –Moody’s Press Release, 9.15.2008
  13. 13. IN OTHER WORDS: In fact, a miracle did not happen.
  14. 14. Opacity Creates Bad Ratings
  15. 15. “The CDO world relied almost exclusively on this Copula based correlation “Correlation trading has spread through the psyche of the financial markets like a highly infectious through virus.”
  16. 16. Contrarian Investing Is Forbidden Fruit Most mutual funds and pension funds must invest in highly rated bonds
  17. 17. If you over-promise everyone will pick your monster
  18. 18. Lack of Diversity Creates Bad
  19. 19. Risks are hidden
  20. 20. We don’t listen or they don’t talk
  21. 21. Single-Sourced Information Creates Bad
  22. 22. This is a chance to make a big dierence 22
  23. 23. Dangerous Accuracy Payments Consens us Data Sources
  24. 24. FRS: Rating Creation Needs to Be Free FRS: Rating Data Needs to Be Open FRS: Rating Scores Need to Be Diverse FRS: Rating Processes Need
  25. 25. Because: Commons Need Transparency Approach Data Needs To Be Open Open Data Source Creation Needs To Be UGC Data Source Free Contributed Scores Need To Be Algorithms Diverse Testing Framework Scores Need Accuracy
  26. 26. Free risk requires open data
  27. 27. XBRL is hard 17 legal jurisdictions 3500 defined elements 100s of use 43
  28. 28. Quarterly 2008-03- Statement $14.5 31 statement period ending revenu current $41.5 Quarterly Microsoft assets Statement stateme freerisk:to current $27 by liabilities stateme footnot annotatio User Annual Footnote-2 assertions Statement ??? ??? ??? ???
  29. 29. JSON { quot;2007quot;: { quot;;: quot;9162000quot;, quot;;: quot;19332000quot;, quot; 2008-03-31#AccountsReceivableNetCurrentquot;: quot;1901000quot;, quot;;: quot;23070000quot;, quot;;: quot;11196000quot;, quot;;: quot;336377000quot;, quot; 2008-03-31#SellingAndMarketingExpensequot;: quot;26571000quot;, quot;;: quot;30058000quot;, quot;;: quot;97207000quot;, quot;;: quot;2007quot;, quot;;:
  30. 30. Open
  31. 31. You too, can make a risk /query? company=fb:en.microsoftperiod=20 07
  32. 32. class CreditController ApplicationController def query(company, period) render :text = sqarquery end def defaultRisk(data) hash_data = JSON::parse(params[:data]) render :text calculate(hash_date).to_json
  33. 33. Check out the work of others source= baseurl period=2008 rankbycreditscore?source=http://
  34. 34. YOU GET IF Covariance Your score goes up with defaults rates for better companies Correctness Your score is measured of defaults rates in probability
  35. 35. Piotroski Score 1. Positive Net Income (+1 for each) 2. Positive Cash Flow 3. Return on Assets up 4. Cash Flow Net Income 5. Debt / Assets up 6. Current Ratio increased 7. #Shares Outstanding same 8. Gross Margin last year 9. % increase Sales the % increase Total Assets
  36. 36. Altman Z-Score EBIT / Total Assets x 3.3 + Net Sales / Total Assets x 0.99 + Value of Equity / Total Liabilities x 0.6
  37. 37. Microsoft AIG Lehman 7 5 3 2 -0 Nov 31, 07 Feb 28, 08 May 31, August 31, 08 (Year Before Lehman Collapse)
  38. 38. Where do we go from here?
  39. 39. More Data Complete XBRL for US Based Schemas Support for non-XBLR US data sources
  40. 40. More Algorithms More credit rating algorithms Multiple approaches Unbiased and biased
  41. 41. There’s every reason to believe that with your help we can create a new financial framework.
  42. 42. Thanks. Jesper Andersen Open Data Group Toby Segaran Metaweb