Post on 23-Jan-2018
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Discounted Cash Flow (DCF) Modelingfor Credit Loss Reserves and Other Applications
Garver MoorePrincipal - Advisory Services
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This presentation may include statements that constitute “forward-looking statements” relative to publicly available industry data. Forward-looking statements often contain words such as “believe,” “expect,” “plans,” “project,” “target,” “anticipate,” “will,” “should,” “see,” “guidance,” “confident” and similar terms. There can be no assurance that any of the future events discussed will occur as anticipated, if at all, or that actual results on the industry will be as expected. Sageworks is not responsible for the accuracy or validity of this publicly available industry data, or the outcome of the use of this data relative to business or investment decisions made by the recipients of this data. Sageworks disclaims all representations and warranties, express or implied. Risks and uncertainties include risks related to the effect of economic conditions and financial market conditions; fluctuation in commodity prices, interest rates and foreign currency exchange rates. No Sageworks employee is authorized to make recommendations or give advice as to any course of action that should be made as an outcome of this data. The forward-looking statements and data speak only as of the date of this presentation and we undertake no obligation to update or revise this information as of a later date.
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End in Mind
At the conclusion of this discussion, participants should:
• Understand the unique considerations of a discounted cash flow (DCF) analysis
• Understand the limitations of a sampling approach
• Believe DCF can be a solution when life-of-loan data is not available
• Understand the unique timing and component nature of DCF analysis
• Be able to speak to the elections required in a DCF analysis
• Understand the inferential approaches used in producing a DCF analysis
• Understand open areas of uncertainty
• Consider cross-applicability of measurement methodology
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Agenda
• “Generative” v. “Look-Back” models
• Discounted cash flow principles
• Sampling vs. Bottom-Up
• Timing and Component Estimations
• Volatility in Measurement
• Current Conditions and Forecasting
• Individual Analysis
• Policy Considerations
• Other Applications
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Warning: Software Ahead
Computationally Intensive
Data IntensiveMore Less
MoreLess
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Warning: Software Ahead
Computationally Intensive
Data IntensiveMore Less
MoreLess
Closed Pool Analysis
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Warning: Software Ahead
Computationally Intensive
Data IntensiveMore Less
MoreLess
Closed Pool Analysis Vintage Analysis
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Warning: Software Ahead
Computationally Intensive
Data IntensiveMore Less
MoreLess
Closed Pool Analysis Vintage Analysis Markov/Transition
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Warning: Software Ahead
Computationally Intensive
Data IntensiveMore Less
MoreLess
Closed Pool Analysis Vintage Analysis DCFMarkov/Transition
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Generative vs. Look-Back
Computationally Intensive
Data IntensiveMore Less
MoreLess
Closed Pool Analysis Vintage Analysis DCFMarkov
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Generative vs. Look-Back
Computationally Intensive
Data IntensiveMore Less
MoreLess
Closed Pool Analysis Vintage Analysis DCFMarkov
Retrospective Generative
Longer history of data Shorter history of data
Period-driven Parameter-driven
Requires adjusting to current conditions “Snaps” to current conditions
Computationally straightforward Computationally complex
Include/exclude periods to adjust Adjust parameters directly
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DCF Principles I
• Model cashflows in the future on a per-loan (bottom up) basis• Include credit assumptions (defaulted principal, unrecovered principal)• Use periodic, not lifetime parameters in estimation• Parameters can be time-bound
• Vintage/seasoning sensitive• Model date-sensitive
• Key Parameters:• Periodic default tendency• Recovery delay• Loss given default• Prepayment rate (Amortizing)• Curtailment rate (Revolving)
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DCF Principles I
• Model cashflows in the future on a per-loan (bottom up) basis• Include credit assumptions (defaulted principal, unrecovered principal)• Use periodic, not lifetime parameters in estimation• Parameters can be time-bound
• Vintage/seasoning sensitive• Model date sensitive
• Key Parameters:• Periodic default tendency• Recovery delay• Loss given default• Prepayment rate (Amortizing)• Curtailment rate (Revolving)
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DCF Principles I
• Model cashflows in the future on a per-loan (bottom up) basis• Include credit assumptions (defaulted principal, unrecovered principal)• Use periodic, not lifetime parameters in estimation• Parameters can be time-bound
• Vintage/seasoning sensitive• Model date sensitive
• Key Parameters:• Periodic default tendency• Recovery delay• Loss given default• Prepayment rate (Amortizing)• Curtailment rate (Revolving)
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DCF Principles II
• Model takes into account:• Seasoning / blend of loan maturities
• Structure
• Adjustments for current conditions
• Prepayments
• Discount Rate(s):• If available, effective yield
• Coupon rate
• Should not be forecast for variable-rate assets
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DCF Principles III
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Parameter Description Lines/Revolvers Amortizing
Curtailment Rate Periodic tendency of an extended principal dollar to be returned to you.
Applicable Not Applicable
Funding Rate Periodic tendency of an undrawn dollar to be drawn
Applicable Not Applicable
Prepayment Speed
Periodic tendency to receive unexpected principal payments
Not Applicable Applicable
Default Rate Periodic tendency of a loan to enter default state.
Applicable Applicable
Loss Given Default
Static loss on a loan, conditional to default event.
Applicable Applicable
Recovery Delay Static time between default event and resolution (recovery or loss)
Applicable Applicable
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DCF Principles III
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Parameter Description Lines/Revolvers Amortizing
Curtailment Rate Periodic tendency of an extended principal dollar to be returned to you.
Applicable Not Applicable
Funding Rate Periodic tendency of an undrawn dollar to be drawn
Applicable Not Applicable
Prepayment Speed
Periodic tendency to receive unexpected principal payments
Not Applicable Applicable
Default Rate Periodic tendency of a loan to enter default state.
Applicable Applicable
Loss Given Default
Static loss on a loan, conditional to default event.
Applicable Applicable
Recovery Delay Static time between default event and resolution (recovery or loss)
Applicable Applicable
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Generative vs. Look Back (Redux)
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We obtain this distribution with 4 quarters of loan detail
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Sampling vs. Bottom-Up
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• Sampling:• Cash-flow some subset (~10%) of randomly sampled loans in a segment
• Churn sample period-to-period
• Arrive at a sampled rate
• Bottom-Up:• Cash-flow portfolio at loan level
• Arrive at population rate
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Sampling vs. Bottom-Up
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• Sampling:• Cash-flow some subset (~10%) of randomly sampled loans in a
segment
• Churn sample period-to-period
• Arrive at a sampled rate
• Bottom-Up:• Cash-flow portfolio at loan level
• Arrive at population rate
Volatility Issues
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Sampling vs. Bottom-Up
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• Sampling:• Cash-flow some subset (~10%) of randomly sampled loans in a segment
• Churn sample period-to-period
• Arrive at a sampled rate
• Bottom-Up:• Cash-flow portfolio at loan level
• Arrive at population rate
Volatility Reflects Portfolio
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Timing and Component Estimation
• Cash from Expected Principal• Contract Payments (Amortizing)
• Curtailment (Lines)
• Cash from Interest• Contract Interest (Amortizing and Lines)
• Unexpected Principal (Prepayments)
• Cash from Recovered Defaults (Principal)
• Defaulted Principal• Defaulted Interest Not Received
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Timing and Component Estimation
DCF analysis produces a time-sensitive projection of component
cashflows
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Volatility in the Allowance
• Depends on: methodology• Look-back versus Look-forward
• Trivially, look-forward more volatile
• Emergent vs. Intrinsic Volatility:• Emergent: Due to mathematical properties of the model
• Intrinsic: Due to mathematical properties of the subject being modeled (credit loss expectations)
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Volatility in the Allowance
• Depends on: methodology• Look-back versus Look-forward
• Trivially, look-forward more volatile
• Emergent vs. Intrinsic Volatility:• Emergent: Due to mathematical properties of the model
• Example: High-impact quarter rolls off analysis
• Intrinsic: Due to mathematical properties of the subject being modeled (credit loss expectations)
• Example: Uptick in prepayment activity
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The Roll-Forward of a NPV Estimation
• We have timed, componentized estimations.
• Immediately sensitive to:• Loan maturities/terms
• Parameter estimates
• These are projections
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The Roll-Forward of a NPV Estimation
• Periodic changes in NPV due to:• Timing
• Credit
• New Loans
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The Roll-Forward of a NPV Estimation
• Periodic changes in NPV due to:• Timing
• May disclose and recognize as interest
income
• Credit• Provision expense
• New Loans• Provision expense
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• When projections are long (short):• Tune parameter?
• Evaluate market changes since parameter
was estimated
• Evaluate confidence in projection
• Evaluate statistical confidence in
parameter
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The Roll-Forward of a NPV Estimation
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Time Sensitivity II: Forecasting
• Our projection is time-sensitive
• Our model inputs can be time-sensitive, too
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Time Sensitivity II: Forecasting
• Our projection is time-sensitive
• Our model inputs can be time-sensitive, too
• Immediately clarifies how to apply reasonable and supportable forecasts
• Even how to “Revert” for periods beyond our forecastable window
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Time Sensitivity II: Forecasting
• Our projection is time-sensitive
• Our model inputs can be time-sensitive, too
• Immediately clarifies how to apply reasonable and supportable forecasts
• Even how to “Revert” for periods beyond our forecastable window
• We can also include other time-bound elements in our projection, for example:
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Origination Year 1 2 3 4 5 6 Total Loss Remaining Loss
12/31/2010 0.40% 1.10% 0.60% 0.25% 0.01% 0.03% 2.39% 0.00%
12/31/2011 0.25% 0.85% 1.25% 0.40% 0.06% 0.03% 2.84% 0.03%
12/31/2012 1.25% 0.95% 0.75% 0.06% 0.04% 0.03% 3.08% 0.07%
12/31/2013 0.75% 0.65% 0.40% 0.24% 0.04% 0.03% 2.10% 0.30%
12/31/2014 0.20% 0.60% 0.75% 0.24% 0.04% 0.03% 1.85% 1.05%
12/31/2015 0.30% 0.83% 0.75% 0.24% 0.04% 0.03% 2.18% 1.88%
Average 0.53% 0.83% 0.75% 0.24% 0.04% 0.03% 2.41%
Loss Rate – Year Subsequent to Origination
Loss curve
Vintage Analysis
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And source forecasts…
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2010 Federal Reserve Forecast
2016 Federal Reserve Forecast
Adjustment Indicated
Current Conditions?
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Individual Analysis
• Standard does not require specific impairment expectation
• Different credit characteristics:• Past Due Mature / Administrative Past Due• Exotic payment streams
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Considerations
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• “Balloon at maturity” assumptions
• Reliability of P&I expectations from reporting systems• Use of inferential systems to model P&I behavior
• Amortization of fees, etc.
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Considerations
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• “Balloon at maturity” assumptions
• Reliability of P&I expectations from reporting systems• Use of inferential systems to model P&I behavior
• Amortization of fees, etc.
• What to do with all that data?
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Other Applications
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• We have a schedule of cashflows, adjusted for credit and timing behavior
• We are discounting by e.g. coupon in our expected loss analysis.
• What if we discount buildup by• Cost of Funds + ROA/ROE Target?• Opportunity cost?
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Other Applications
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Loan Number GL Balance Cash Flow NPV (CECL) NPV (Pricing) NPV (Exit Price) IRR PD LGD
12137713 250,000 281,292 (4,074) (0) (5,679) 3.87% 3.00% 15.00%
Beginning Balance Principal Interest Prepayment Defaulted Principal Estimated Loss Estimated RecoveryEnd of Month
Balance Fees/(Expenses) Cash Flow
250,000 146,631 34,804 79,955 23,414 3,512 19,902 (342) 281,292
(250,000) 1,700.00 (250,000)
250,000 975 942 2,180 634 95 - 246,212 - 4,097
246,212 989 928 2,147 624 94 - 242,452 - 4,064
242,452 1,003 914 2,114 615 92 - 238,720 - 4,031
238,720 1,017 900 2,082 605 91 - 235,016 - 3,998
235,016 1,031 886 2,049 596 89 - 231,340 - 3,966
231,340 1,045 872 2,017 586 88 - 227,691 - 3,934
227,691 1,059 858 1,985 577 87 - 224,070 - 3,902
224,070 1,072 844 1,954 568 85 - 220,476 - 3,871
220,476 1,086 831 1,922 559 84 - 216,908 - 3,839
216,908 1,099 817 1,891 550 82 - 213,368 - 3,808
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Other Applications
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Loan Number GL Balance Cash Flow NPV (CECL) NPV (Pricing) NPV (Exit Price) IRR PD LGD
12137713 250,000 281,292 (4,074) (0) (5,679) 3.87% 3.00% 15.00%
Beginning Balance Principal Interest Prepayment Defaulted Principal Estimated Loss Estimated Recovery End of Month Balance Fees/(Expenses) Cash Flow
250,000 146,631 34,804 79,955 23,414 3,512 19,902 (342) 281,292
(250,000) 1,700.00 (250,000)
250,000 975 942 2,180 634 95 - 246,212 - 4,097
246,212 989 928 2,147 624 94 - 242,452 - 4,064
242,452 1,003 914 2,114 615 92 - 238,720 - 4,031
238,720 1,017 900 2,082 605 91 - 235,016 - 3,998
235,016 1,031 886 2,049 596 89 - 231,340 - 3,966
231,340 1,045 872 2,017 586 88 - 227,691 - 3,934
227,691 1,059 858 1,985 577 87 - 224,070 - 3,902
224,070 1,072 844 1,954 568 85 - 220,476 - 3,871
220,476 1,086 831 1,922 559 84 - 216,908 - 3,839
216,908 1,099 817 1,891 550 82 - 213,368 - 3,808
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Example – NPV (Discounted Cash Flow)
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Loan Number GL Balance Cash Flow NPV (CECL) NPV (Pricing) NPV (Exit Price) IRR PD LGD
12137713 250,000 281,292 (4,074) (0) (5,679) 3.87% 3.00% 15.00%
Loan Number GL Balance Cash Flow NPV (CECL) NPV (Pricing) NPV (Exit Price) IRR PD LGD
12137713 250,000 283,118 (4,300) (122) (6,113) 3.86% 3.00% 15.00%
• Prepayment behavior or expectation can have a significant impact on the profitability of a loan
• In the above example, a simple reduction in prepayment behavior from 10% CPR to 8% CPR would result in under-performance relative to institutional targets
• This relationship is not linear. Interest rates exceeding x% will experience an improvement in performance as prepayments slow, while interest rates below x% will experience declining profitability
• There is a certain period of time that is required in order to recover origination costs and overhead allocation
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Example – NPV (Discounted Cash Flow)
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Loan Number GL Balance Cash Flow NPV (CECL) NPV (Pricing) NPV (Exit Price) IRR PD LGD
12137713 250,000 281,292 (4,074) (0) (5,679) 3.87% 3.00% 15.00%
• Although both scenarios include a 45bps loss rate, the relationship between default (lost interest) and realized loss certainly matters from a pricing decision under an NPV approach.
Loan Number GL Balance Cash Flow NPV (CECL) NPV (Pricing) NPV (Exit Price) IRR PD LGD
12137713 250,000 279,562 (4,565) (536) (6,034) 3.80% 5.00% 9.00%
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Segment GL Balance PV Reserve Reserve % NPV (Effective Yield) NPV (Pricing) NPV (Exit Price)
Real Estate Other 365 - 365 - Fixed 6,764,709 6,718,898 45,811 0.68% (45,600) 43,155 (89,417)
RE 1-4 Family (Amort) Fixed 2,522,876 2,508,479 14,397 0.57% (14,336) (53,747) (162,600)
TOTAL 9,287,585 9,227,377 60,208 0.65% (59,936) (10,592) (252,017)
Example – NPV (DCF) – Portfolio Level
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• Portfolio level analysis contains valuable insight into the profitability of various product lines under current market conditions.
• .65% Reserve (CECL)
• -$10M below ROA objective given all assumptions; cost of funds, PD, LGD, CPR, fees, and expenses
• 2% FMV discount
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(1,000,000)
-
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
7,000,000
8,000,000
Real Estate Other 365 - 365 - Fixed RE 1-4 Family (Amort) Fixed
(200,000)
(150,000)
(100,000)
(50,000)
-
50,000
100,000
NPV (EffectiveYield)
NPV (Pricing) NPV (Exit Price)
Real Estate Other 365 - 365 - Fixed RE 1-4 Family (Amort) Fixed
Example – NPV (DCF) – Portfolio Level
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IMP
LEM
ENT
NPV (DCF): Cross Application + CECL + Exit Price
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Classification and Measurement
IMP
LEM
ENT
Classification and Measurement
CECL
CECL
SEC Filer
Non-SEC Filer
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IMP
LEM
ENT
NPV (DCF): Cross Application + CECL + Exit Price + Pricing
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Classification and Measurement
IMP
LEM
ENT
Classification and Measurement
CECL
CECL
IMP
LEM
ENT
IMP
LEM
ENT
Valuation, Day 2, and Pricing
Valuation, Day 2, and Pricing
SEC Filer
Non-SEC Filer
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Question & Answer
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Garver.Moore@Sageworks.com
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