US SEC Mandates, Python, and Financial Modeling


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US SEC Mandates, Python, and Financial Modeling

  1. 1. Connecting the Dots: US SEC, ABS Mandates, Financial Modeling and Python Presented by Ann Rutledge, R&R Consulting Diane Mueller, ActiveState
  2. 2. Speakers • Ann Rutledge • R&R Consulting • • Diane Mueller • ActiveState •
  3. 3. About ActiveState Founded 1997 2 million developers, 97% of Fortune 1000 rely on ActiveState Development, management, distribution solutions for dynamic languages Core languages: Python, Perl, Tcl Other: PHP, Ruby, Javascript
  4. 4. We’ve got your universe covered
  5. 5. About R&R Consulting • R&R Consulting is a 10-year-old leader in cybernetic finance • A standards firm for debt capital markets • Balanced emphasis on risk-responsibility and value creation at each link in the global supply chain of debt capital
  6. 6. What we will cover • eGov & the Appearance of Transparency • Case Study: Financial Content at US SEC • Why Reg AB was not enough • Python & Financial Models – the next Wave
  7. 7. Premise • “Open” Gov’t Data needs “Open” Tools • Tool Vendors need Open Source technologies – Expedite delivery of consumption tools to market – Ensure consistency in interpretation of the data – Ensure equal access to tools across the supply chain
  8. 8. So what is eGov? • The way in which government has to adapt itself to a world in which most people regularly use the internet –
  9. 9. Motivation • Transparency and engagement – holding government accountable and promoting choice by informing citizens • Efficiency and enhanced public services – enabling re-use of information within the public sector • Innovation and economic growth – encouraging and supporting data-driven innovation
  10. 10. eGov in US: Open Government Directive January 2009 • President Obama issued a memo on transparency directing his top officials to develop plans for an Open Government Directive to promote transparency, participation, and collaboration.
  11. 11. Growing availability of Open Gov Data • US, UK, Australia, Netherlands, Denmark, Sweden, Spain ... • Washington, Madrid, London, Vancouver, ...
  12. 12. Publishing Data on Data.Gov • XML – Industry, agency, project- specific • PDF • RDFa • Shape • Excel
  13. 13. Unraveling Open Data • It’s more than just documents for people to read • Need to enable machines to – traverse, aggregate, analyze, answer • Tim Berners-Lee's vision of a “Semantic Web” – "Semantics", "Ontologies” – RDFa, Linked Data
  14. 14. More Context • Open government data is a reality – “Open” doesn’t necessarily mean: • “Equal Access” • “Free” • “Transparent” • Semantic Web is a vision – Should be nurtured, supported and encouraged • But we’re in the middle of a Financial Crisis – We need to work with the tools we have now
  15. 15. Financial Content @ US SEC • A Case Study • Lessons Learned the hard way Part One: The Data
  16. 16. XBRL @ SEC • A standard format in which to prepare financial reports that can subsequently be presented in a variety of ways • A standard format in which information can be exchanged between different software applications • Permits the automated, efficient and reliable extraction of information by software applications • Facilitates the automated comparison of financial and other business information, accounting policies, notes to financial statements between companies, and other items about which users may wish make comparisons that today are performed manually
  17. 17. Why did XBRL make sense for US SEC? • *Responsible* Publishing of data • *Easier* for people to consume financial data • *Solve* some snags other approaches miss
  18. 18. 10 years later… more rules, rss feeds & a viewer
  19. 19. It’s still a black box world • Processing Financial Content – requires specialized tools – proprietary processors • Business Decisions are all about timing – access to data to make decision that happen in *nano* seconds – driven by algorithmic, computerized trading
  20. 20. Lesson Learned in First Phase: Open Data at US SEC • Appearance of transparency • Granting of Access is insufficient • No Open Standard API for XBRL available (yet) • Asynchronous access to data (latency)
  21. 21. But what if the data isn’t enough • A little structure goes a long way if you combine it with – A human being with a lot of intelligence/domain knowledge – (tools, protocols, means of communication) – (browser, http, share) • Complex legalese in Filings & Prospectuses – Logic embedded in them needs to be interpreted
  22. 22. _crisis_impact_timeline Off to the Next Crisis Enter the Financial Ninjas
  23. 23. Seeds of the Latest Crisis • Industry participants have real problems interpreting how the cash flow is allocated • The lack of certainty creates serious valuation problems for holders of the securities • Investors and their custodians also have very real settlement problems Regulators/Watchdogs
  24. 24. ABS is the new black box s Ra ting Assets it Liabilities/Capital Structure C red • Corporate credit is stochastic: • WYSIWYG/Not WYSIWYG. YES/NO. ABS is statistical, not • Deal waterfalls defines value. stochastic: YIELD/% OF PAR? • To implement the standard form or • Corporate data disclosures are defined amortizations, modeling public. ABS data disclosures are skills and knowledge of finance are private. required. • Corporate data are a moving • Waterfalls are not always target—ratios change with business unidirectional (as we intuit them). flows. ABS credit-sensitive data is Some are complex, with recursions taken on a static pool basis. and nonlinearities. • Corporate disclosures are a snapshot • At origination, Sellers and Buyers are of performance, easily “doctored.” supposed to be risk-neutral. ABS data should be updated: when Modeling mistakes totally change the pool amortizes, so does risk. the game.
  25. 25. How a Deal Waterfall Works, Conceptually • The whole thing is a state machine that needs to be precise in every detail; as generally once the deal is set up, the trustees and managing agents have no discretion • They have to follow the script precisely as set out in the deal prospectus
  26. 26. What Real Waterfalls Look Like (This One Has Loops) Sample Waterfall (AmeriCredit 2001) Copyright 2010 26
  27. 27. Here, the Master Waterfall Has A Trigger Distribution Structure: Calculate Available Funds using the SWAP Payment and Yield Maintenance Cap. The result will be distributed across all tranches in the liability structure. Allocation Structure: Use a Trigger to determine, “Which Allocation Plan do I call, A or B?” Sample Waterfall (Bear Stearns 2005) Trigger Copyright 2010
  28. 28. …If Tripped, Pro Rata Becomes Sequential Sample Waterfall (Bear Stearns 2005) Distribution 28
  29. 29. …Mezzanine Class Credit Enhancement Is Linked to 60+ Delinquencies Sample Waterfall (Bear Stearns 2005) A simple difference linking two Asset Trigger Event complex calculations: 60+ days’ delinquent accounts as a percentage of the current pool balance and a run-rate target enhancement percentage.
  30. 30. Enter Regulation (“Reg”) AB The necessary, robust disclosure framework developed by the SEC for structured securities. • Necessary because: – ABS use different (private data) and different measures (static pool delinquencies, defaults, losses) – ABS have a different risk/return profile than corporate debt securities—the risk amortizes as the pool amortizes • Robust because: – Reg AB incorporated all key disclosure items as recommended by the market – It was strongly endorsed by large pension funds and institutional investors
  31. 31. Why Reg AB was not enough..
  32. 32. Regulation AB: Evolving, Closing Loopholes , ure 2005 Version clos ck, Dis dba 2010 Proposal t Fee cemen • Requires disclosure of static pool or • Requires updated disclosures. Enf data in prospectus, website • Lowers the threshold of materiality disclosure of performance data to 1% – Delinquency • No shelf registration without risk – Loss retention • Sets a 5% standard of materiality in • Waterfalls must be published, as misleading disclosures part of the data set
  33. 33. How Access to Financial Models helps.. • Today, it’s hard to Price Securities – complex prospectus documentation – write your own waterfall program – data must be mined from investor reports – determine the cash flow every month • Proprietary analytic packages that do this, but it’s expensive – pay 100K USD for the model to find out the securities are fairly priced • Having the model & updated data freely means: – Securities can be valued in a far more cost effective way – Leaves less up to interpretation – Run scenario’s on the portfolio and access the impact on the underlying securities in real time
  34. 34.
  35. 35. What is the SEC asking for? • Python code to document the waterfall model – decision logic which determines the cash payouts made to all the securities attached to the a specific mortgage pool. – depending on specific events the cash flows distributed to securities will change over time based on events occurring in the underlying portfolio • This is documented in the prospectus which can be > 600 pages of detailed legal language
  36. 36. Why Python makes sense for US SEC • Open Freely Available now • Available libraries for working with complex algorithms – Numpy, matplotlib, Rpy • Bindings exist for other proprietary numerical Financial libraries • Already widely used for Financial Modeling • Python is a very readable language when coupled with the other proposal that detailed asset level data be also provided in machine readable (XML) format
  37. 37. Next Steps • Key: Give Feedback to SEC on it’s Proposal – Depends upon economics, politics, culture and technology – Which could easily change in radical ways.. • Through an invention • Through a insight into a practical application of an existing technology • Fostered by Open Collaboration, Open APIs
  38. 38. It’s still a black box world • Collaboration across constituencies.. – “Open” Gov’t Data needs “Open” Tools • Open Source technologies – Expedite delivery of consumption tools to market – Ensure consistency in interpretation of the data – Ensure equal access to tools across the supply chain
  39. 39. Q&A Twitter: @activestate Twitter: @annrutledgerr
  40. 40. Thank You Speak to a representative about ActivePython Enterprise or OEM: 1-866-510-2914