1. © Brammertz Consulting, 20091Date: 24.01.2016 Unified Financial Analysis Risk & Finance Lab Chapters 1&2 Willi Brammertz / Ioannis Akkizidis

2. © Brammertz Consulting, 20092Date: 24.01.2016 Agenda > The target > Origin of the problem > New paradigm > What is new? > Financial analysis > Static > Dynamic > Organization of the lectures

3. © Brammertz Consulting, 20093Date: 24.01.2016 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. © Brammertz Consulting, 20094Date: 24.01.2016 Agenda > The target > Origin of the problem > New paradigm > What is new? > Financial analysis > Static > Dynamic > Organization of the lectures

5. © Brammertz Consulting, 20095Date: 24.01.2016 Book keeping: The origine of writing

6. © Brammertz Consulting, 20096Date: 24.01.2016 Fra Luca Pacioli 1494 Summa de Arithmetica, Geometria, Proportioni et Proportionalità

7. © Brammertz Consulting, 20097Date: 24.01.2016 Progress of book keeping > Middle age to 19th Century: Pacioli > 19th Century: Cash flow statement > Late 20th Century: new valuation methods

8. © Brammertz Consulting, 20098Date: 24.01.2016 Evolution of IT in the banking sector > Appearance of systems 1.Book keeping 2.Transaction processing (loans, deposits) 3.Trading (classical instruments, derivatives...) 4.Advanced analytics > Under > Constant financial pressure > Increased transaction speed > Constant regulatory pressure

9. © Brammertz Consulting, 20099Date: 24.01.2016 Data from Transaction Systems Analytical Systems n Transaction Systems, m Analytical Systems = n*m Interfaces Interfacing Transaction Systems with Analytic Systems

10. © Brammertz Consulting, 200910Date: 24.01.2016 Data Warehouse Transaction Systems Analytical Systems n Transaction Systems, m Analytical Systems = n+m Interfaces Interfacing Analytic Systems via a Data Warehouse: The Ideal World

11. © Brammertz Consulting, 200911Date: 24.01.2016 Transaction Systems Analytical Systems n+m? No, logically still n*m Data Warehouse Interfacing Analytic Systems via a Data Warehouse: The Real World

12. © Brammertz Consulting, 200912Date: 24.01.2016 > > > > > < < > < > < > Ideal DW Transaction Systems Analytical Systems LIQ VAR CAD FTP IAS Etc. Consistent, Comparable? Consistent, Comparable? Consistent, Comparable? The answer to the consistency question will be No Is Integrated Data Enough?

13. © Brammertz Consulting, 200913Date: 24.01.2016 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. © Brammertz Consulting, 200914Date: 24.01.2016 Agenda > The target > Origin of the problem > New paradigm > What is new? > Financial analysis > Static > Dynamic > Organization of the lectures

15. © Brammertz Consulting, 200915Date: 24.01.2016 What constitutes a fact in finance? First question 15

16. © Brammertz Consulting, 200916Date: 24.01.2016 Facts of finance

17. © Brammertz Consulting, 200917Date: 24.01.2016 Hard and soft facts Input elements

18. © Brammertz Consulting, 200918Date: 24.01.2016 Input, contract events and analysis elements

19. © Brammertz Consulting, 200919Date: 24.01.2016 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 19

20. © Brammertz Consulting, 200920Date: 24.01.2016 The Role of Contract Events 20

21. © Brammertz Consulting, 200921Date: 24.01.2016 Contract Types ≈30 Patterns (Contract Types)

22. © Brammertz Consulting, 200922Date: 24.01.2016 Agenda > The target > Origin of the problem > New paradigm > What is new? > Financial analysis > Static > Dynamic > Organization of the lectures

23. © Brammertz Consulting, 200923Date: 24.01.2016 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. © Brammertz Consulting, 200924Date: 24.01.2016 Old situation laura.anzoni@access.uzh.ch Data A1 Contract Algorithms State Contingent Cash Flows A2 Contract Algorithms State Contingent Cash Flows … Contract Algorithms State Contingent Cash Flows A3 Contract Algorithms State Contingent Cash Flows An Contract Algorithms State Contingent Cash Flows

25. © Brammertz Consulting, 200925Date: 24.01.2016 New architecture Contract Algorithms Data A1 A2 A3 … An Contract Events State Contingent Cash Flows

26. © Brammertz Consulting, 200926Date: 24.01.2016 A standard that represents the terms of the contracts is needed Contract Algorithms Data A1 A2 A3 … An State Contingent Cash Flows ≈30 Patterns (Contract Types)

27. © Brammertz Consulting, 200927Date: 24.01.2016 Agenda > The target > Origin of the problem > New paradigm > What is new? > Financial analysis > Static > Dynamic > Organization of the lectures

28. © Brammertz Consulting, 200928Date: 24.01.2016 No 28

29. © Brammertz Consulting, 200929Date: 24.01.2016 Book keepers vs. rocket scientists 29

30. © Brammertz Consulting, 200930Date: 24.01.2016 #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 Characteristics of the system

31. © Brammertz Consulting, 200931Date: 24.01.2016 #1: Input and analysis elements and start from input AssetsLiabilities 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. © Brammertz Consulting, 200932Date: 24.01.2016 > Contracts vs. Risk factors > Contract modeling: Mechanic, close approximation of reality > Risk factor modeling: risky business! > Example: Vasicek short term interest rate model #2: Separate hard from soft facts

33. © Brammertz Consulting, 200933Date: 24.01.2016 > 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 The certainty – risk – uncertainty spectrum 33

34. © Brammertz Consulting, 200934Date: 24.01.2016 > Risk can be represented by classical market models > Uncertainty by stress tests > Market stress > Credit stress > Liquidity stress The certainty – risk – uncertainty spectrum Time to Maturity Yield AAA AA A... A BBB BB... 1M 10% 3M 10% 6M 15% 1Y 25% >1Y 40% 20% 40% 30% 10%

35. © Brammertz Consulting, 200935Date: 24.01.2016 > 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.orgwww.projectactus.org #3: Pivotal role of the financial contract 35

36. © Brammertz Consulting, 200936Date: 24.01.2016 #4: Completeness > Heuristic argument > Completeness demands richness

37. © Brammertz Consulting, 200937Date: 24.01.2016 Agenda > The target > Origin of the problem > New paradigm > What is new? > Financial analysis > Static > Dynamic > Organization of the lectures

38. © Brammertz Consulting, 200938Date: 24.01.2016 Market rate Volatility in t 0 (  ) NPV Existing Contracts Assets t0t0 Time Liabilities Static

39. © Brammertz Consulting, 200939Date: 24.01.2016 Example 1 – Liquidity GAP

40. © Brammertz Consulting, 200940Date: 24.01.2016 Example 2 - Counterparty exposure breakdown

41. © Brammertz Consulting, 200941Date: 24.01.2016 Example 3 - Value at risk (V@R)

42. © Brammertz Consulting, 200942Date: 24.01.2016 Example 4 - Management summary

43. © Brammertz Consulting, 200943Date: 24.01.2016 Example 5 : Capital allocation – risk adjusted performance 80‘000‘000.0064‘867‘033.26 PlanRevenue Less than 75% Between 75% and 95% More than 95% Profitability Revenue and expense Capital (value) Achievement Information type 60’000’000.0043’439’567.78 PlanExpense 220‘000‘000.00200’000’000.00 PlanEco. capital 250’000’000.00304’398’803.18 PlanØ equity 2‘700‘000.003’650’000.00 PlanAdmin. cost 20‘000‘000.0021’427’465.48 PlanIncome 17‘300‘000.0017’777’465.48 PlanOp. income 88.00% 65.70 % PlanCapital utilis. 7.86%8.89% PlanRORAC 6.92%5.84% PlanROE - - x // Capital (risk)

44. © Brammertz Consulting, 200944Date: 24.01.2016 Multiple Valuation > Two reasons why values differ > Book keeping methods > Risk factor models > Multiple parallel book values

45. © Brammertz Consulting, 200945Date: 24.01.2016 Multiple Risk Sources > Market risk > Interest > Stocks > Commodities > FX > Counterparty risk > Behavioral risks

46. © Brammertz Consulting, 200946Date: 24.01.2016 Agenda > The target > Origin of the problem > New paradigm > What is new? > Financial analysis > Static > Dynamic > Organization of the lectures

47. © Brammertz Consulting, 200947Date: 24.01.2016 Why dynamic? > Liquidation view > Going concern view > Life is a going concern! > Thought experiment > How much risk can be there if Δ t=0? > What happens to value if everybody wants to liquidate?

48. © Brammertz Consulting, 200948Date: 24.01.2016 The role of time in financial analysis 48

49. © Brammertz Consulting, 200949Date: 24.01.2016 P&L Assets t0t0 Time Liabilities t0t0... Yield curve t 1 Yield curve t 2 Time to Maturity Yield curve t 0 Yield Spread Dynamic

50. © Brammertz Consulting, 200950Date: 24.01.2016 Dynamic Simulation Markets Counter- parties Behavior Contracts Natural Time

51. © Brammertz Consulting, 200951Date: 24.01.2016 Taxonomy of Analysis 51

52. © Brammertz Consulting, 200952Date: 24.01.2016 Example 6 - Balance sheet and P&L forecast

53. © Brammertz Consulting, 200953Date: 24.01.2016 Example 7 Distribution of Equity

54. © Brammertz Consulting, 200954Date: 24.01.2016 Example 8 – Liquidity stress test Gap and counter balancing capacity cum. KAB ≤ cum. CBC for each time-bucket Dynamic limiting of liquidity gaps through existing counter balancing capacity Daily management and controlling Considering uncertain cash flow forecasts for non- deterministic products Calculation of liquidity gaps resulting from the cash flows with daily maturity buckets for the first 180 days Appraisal of counter balancing capacity, i.e. the bank‘s ability, to liquidate liquid assets at the earliest possible time, e.g. by using the discount window or by repoing securities

55. © Brammertz Consulting, 200955Date: 24.01.2016 Agenda > The target > Origin of the problem > The solution > Static model > Dynamic model > Organization of the lectures

56. © Brammertz Consulting, 200956Date: 24.01.2016 Unified Financial Analysis – The missing links of finance > Part I: Introduction > Part II: Input Elements > Financial Contracts > Market Risk Factors > Counterparties > Behavior > (Cost) > Part III: Analysis Elements (Static Analysis) > Financial Events and Liquidity > Value, Income and FTP > Sensitivity > Risk > (Operational Risk)

57. © Brammertz Consulting, 200957Date: 24.01.2016 Unified Financial Analysis – The missing links of finance > Part IV: Dynamic Analysis > General Mechanisms > Banks > (Non-Life Insurance) > (Life Insurance) > (Non-Financials) > Part V: Outlook and conclusions > Financial Laboratory (Completness of the System) > Towards a Unified Financial Language