Unified Financial Analysis Risk & Finance Lab

1. Unified Financial Analysis Risk & Finance Lab Chapters 1&2 Willi Brammertz / Ioannis Akkizidis

2. Agenda • The target • Origin of the problem • New paradigm • What is new? • Financial analysis • Static • Dynamic • Organization of the lectures

3. Target Combining good theory with good practice • Understanding the principles of financial analysis • From base principles to detail • Core ideas • Data structures • Algorithms • Analysis tools • Applying the principles • Software • Model building

4. Agenda • The target • Origin of the problem • New paradigm • What is new? • Financial analysis • Static • Dynamic • Organization of the lectures

5. Book keeping: The origine of writing

6. Fra Luca Pacioli 1494 Summa de Arithmetica, Geometria, Proportioni et Proportionalità

7. Progress of book keeping • Middle age to 19th Century: Pacioli • 19th Century: Cash flow statement • Late 20th Century: new valuation methods

8. Evolution of IT in the banking sector • Appearance of systems • Book keeping • Transaction processing (loans, deposits) • Trading (classical instruments, derivatives...) • Advancedanalytics • Under • Constant financial pressure • Increased transaction speed • Constant regulatory pressure

9. Interfacing Transaction Systems withAnalytic Systems Data from Transaction Systems Analytical Systems n Transaction Systems, m Analytical Systems = n*m Interfaces

10. Interfacing Analytic Systems via a Data Warehouse: The Ideal World Transaction Systems Data Warehouse Analytical Systems n Transaction Systems, m Analytical Systems = n+m Interfaces

11. Interfacing Analytic Systems via a Data Warehouse: The Real World Transaction Systems Analytical Systems Data Warehouse n+m? No, logically still n*m

12. Is Integrated Data Enough? Transaction Systems Consistent, Comparable? > < > < > > < > Ideal DW Consistent, Comparable? Consistent, Comparable? > < > > Analytical Systems LIQ VAR CAD FTP IAS Etc. The answer to the consistency question will be No

13. Two strong assumptions There are two strong assumptions behind the “DW-idea” • The results “are there” • Results are additive These assumptions hold only under a traditional book keeping regime

14. Agenda • The target • Origin of the problem • New paradigm • What is new? • Financial analysis • Static • Dynamic • Organization of the lectures

15. First question What constitutes a fact in finance?

16. Facts of finance

17. Hard and soft factsInput elements

18. Input, contract eventsand analysis elements

19. Contract events • Reading the financial contract • Along the time line • Given position of risk factors • Homogenizes financial contracts • Event level: Rock bottom of finance • Contract events lead to “State Contingent Cash Flows” • Any financial report can be constructed from state contingent cash-flows

20. The Role of Contract Events

21. Contract Types ≈30 Patterns (Contract Types)

22. Agenda • The target • Origin of the problem • New paradigm • What is new? • Financial analysis • Static • Dynamic • Organization of the lectures

23. Steps of analysis • Financial analysis requires analytical engines • Analytical engines require the state contingent cash flows of individual contracts • State contingent cash flows require an algorithmic representation of financial contracts that use contract terms and risk factor states • Legacy financial data architectures do not support this

24. laura.anzoni@access.uzh.ch Old situation Data A1 An A2 … A3 Contract Algorithms Contract Algorithms Contract Algorithms Contract Algorithms Contract Algorithms State Contingent Cash Flows State Contingent Cash Flows State Contingent Cash Flows State Contingent Cash Flows State Contingent Cash Flows

25. New architecture A3 … A2 A1 An Contract Events State Contingent Cash Flows Contract Algorithms Data

26. A standard that representsthe terms of the contracts is needed A3 … A2 A1 An State Contingent Cash Flows ≈30 Patterns (Contract Types) Contract Algorithms Data

27. Agenda • The target • Origin of the problem • New paradigm • What is new? • Financial analysis • Static • Dynamic • Organization of the lectures

28. No

29. Book keepers vs. rocket scientists

30. Characteristics of the system #1: Separate input from analysis elements and start from input #2: Separate hard facts from the rest #3: Pivotal role of the contracts and treatment as objects (ch. 3) #4: Completeness

31. #1: Input and analysis elementsand start from input Assets Liabilities • Cash • Interbank • Short term • Upto 1Y • Long term • Loans • Uncollaterlized • Mortgages • Variable • Fixed • .... • Trading portfolio • Others Interbank Short term Upto 1Y Long term Savings Deposists Demand Term Short term Long term Reserves Equity • Regulators: • demand results (analysis elements) • Non aggregatable (no control over implicit input)

32. #2: Separate hard from soft facts • Contracts vs. Risk factors • Contract modeling: Mechanic, close approximation of reality • Risk factor modeling: risky business! • Example: Vasicek short term interest rate model

33. The certainty – risk – uncertainty spectrum • Certain is the promise embedded in the financial contract • “Quite certain” is the current state of the risk factors • The future state of the risk factors is • Risky at its best • Often uncertain

34. The certainty – risk – uncertainty spectrum • Risk can be represented by classical market models • Uncertainty by stress tests • Market stress • Credit stress • Liquidity stress Yield Time to Maturity AAA AA A ... 1M 10% 3M 10% 6M 15% 1Y 25% >1Y 40% A BBB BB ... 20% 40% 30% 10%

35. #3: Pivotal role of the financial contract • Modelling financial contracts – thier interrnal mechanics – is absolutely pivotal to the system • System is as good as it is capable to model contracts • Standard CT´s play essential role in systemic risk analysis • UfA Chapter 3 • Standards: www.projectactus.org

36. #4: Completeness • Heuristic argument • Completeness demands richness

37. Agenda • The target • Origin of the problem • New paradigm • What is new? • Financial analysis • Static • Dynamic • Organization of the lectures

38. Static Market rate NPV Assets Existing Contracts Time Liabilities t0 Volatility in t0 ()

39. Example 1 – Liquidity GAP

40. Example 2 - Counterparty exposure breakdown

41. Example 3 - Value at risk (V@R)

42. Example 4 - Management summary

43. Op. income Income Revenue ROE Admin. cost Capital utilis. RORAC Ø equity Eco. capital Expense Plan Plan Plan Plan Plan Plan Plan Plan Plan Plan 43’439’567.78 200’000’000.00 21’427’465.48 3’650’000.00 5.84% 8.89% 304’398’803.18 17’777’465.48 64‘867‘033.26 65.70% 20‘000‘000.00 220‘000‘000.00 7.86% 250’000’000.00 60’000’000.00 6.92% 17‘300‘000.00 88.00% 80‘000‘000.00 2‘700‘000.00 Example 5 : Capital allocation – risk adjusted performance x / / - Achievement Less than 75% Between 75% and 95% - More than 95% Information type Profitability Revenue and expense Capital (value) Capital (risk)

44. Multiple Valuation • Two reasons why values differ • Risk factor models • Book keeping methods • Multiple parallel book values

45. Multiple Risk Sources • Market risk • Interest • Stocks • Commodities • FX • Counterparty risk • Behavioral risks

46. Agenda • The target • Origin of the problem • New paradigm • What is new? • Financial analysis • Static • Dynamic • Organization of the lectures

47. Why dynamic? • Liquidation view • Going concern view • Life is a going concern!

48. The role of time in financial analysis

49. Dynamic t0 ... Yield Time to Maturity Yield curve t0 Yield curve t1 Yield curve t2 P&L Assets Time Liabilities t0 Spread

50. Markets Counter-parties Behavior Contracts Dynamic Simulation Natural Time