Forecasting Jeff Horon 25 January 2011. About me [& forecasting] BA Econ / Honors thesis in...
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Transcript of Forecasting Jeff Horon 25 January 2011. About me [& forecasting] BA Econ / Honors thesis in...
Forecasting
Jeff Horon25 January 2011
About me [& forecasting]BA Econ / Honors thesis in petroleum price
forecastingMBA [Winter 2011] / Emphases in Finance
& Strategy; Formal training in decision support
Sr. Analyst / Medical SchoolDesign responsibility for econometric and
financial modeling describing the Medical School’s $0.5B research enterprise [Ad hoc and standardized reporting, surveys, dashboards]
Fore ▪ castCasten Fore
-“Contrive”
“Before [the fact]”
We forecast all the timeCool Kids
Personal Space
Studious
Achievers
You’re doing it right now
Not so bad
Exit Strategy
Why forecast?Decision Support! Decision Support! D-
e-c-i-s-i-o-n S-u-p-p-o-r-t!
Backward-looking: “How did we do?”(it pays to correct your mistakes)
In the present: “How are we doing?”(it pays to not make the mistakes in the first place)
Forward-looking: “Are we headed in the right direction?”
(it pays to be proactive, consistent with reasonable expectations)
Continuum of methodsQualitative --> Quantitative
Subjective
Objective
‘Gut feeling’
Casual observation
Extrapolation
Decision trees /Scenario
construction
Prediction IntervalsMonte
Carlo
Cost [Method]
Cost ~ Complexity ~ Time investment(Skills, effort devoted to creation, maintenance,
delivery)
Objectivity
Cost [Scale]
Cost ~ Resources ~ Time and/or capital investment(Skills, effort, capital devoted to implementation and
maintenance)
Scale
Expected Value of Information
Expected Value of Information ~ Quality ~ Scale of Decision
Objectivity; Scale
Decision Framework
Marginal analysis: Target Expected Value of Information = Cost of Information
Objectivity; Scale
EV (Information) >
Cost
EV (Information) <
Cost EV (Info)
Cost
Practical Decision FrameworkHigh Impact
-High per-unit stakes-High volume
Repeated
Low Impact-Low per-unit stakes-Low volume
Not Repeated
High EV (Info)
Low EV (Info)
Practical Implementation
Objectivity; Scale
EV (Information) >
Cost
EV (Info)
Cost
EV (Info)
Cost
‘Back of Envelope’‘Sketching’‘Low-Fi Prototyping’
WorkflowSTAR
TIdentify unmet decision
support need
Create
Improve
Match method to need
Is it feasible?
Share
Sketch
Prototype
Is it practical?
‘Sell idea’
‘Gut check’ results
Standardize
Build into existing reporting
Method – Gut feelingSubjective
“Well, we usually fall within… ”“I’ve got a good feeling about… ”
Based upon experience
Useful for a check if you are ‘in the ballpark’
Method – Casual observationSubjective
“If we stay on the same growth trajectory as the past few years… ”
Based upon experience and data
However, as our minds rush to think ahead…
Often this is fine…
sometimes w
e see beyond
what’s th
ere
…. but sometimes it isn’t…
Analogous to extrapolation outside the ‘relevant range’
Method – ExtrapolationLess subjective; ‘Baked-in’ assumptions
Based upon historical data
Method – ExtrapolationExtremely easy to implement in Excel
=FORECAST(); =TREND(); =GROWTH() orRight-click graphed data series, ‘Add Trendline’
Every investment prospectus: “Past performance does not guarantee future results”
Method – Decision treePotential for ‘guesstimation’ in the
absence of historical data
Typically based upon historical data
Method for calculating an expected value across multiple possible outcomes; Branches can be decisions or random events
Example – Decision tree
EV = -$12k
Invest?
Decision
Random Event
Meets specs?
Result
No saving
sEV =
$0
High Savings
Low SavingsEV =
$10k
EV = $20k
Yes
No
Yes
No
Key:
50%
50%
Example – Decision tree
EV = -$12k
Invest?
Decision
Random Event
Meets specs?
Result
No saving
sEV =
$0
High Savings
Low SavingsEV =
$10k
EV = $20k
Yes
No
Yes
No
Key:
50%
50%
EV = +$3k Invest = Yes
Method – Scenario construction
Some room for subjectivity in assumptions; Helpful to jog memory regarding important variables, events, etc.
Based upon historical observations or future expectations
Flexible approach depending on decision support need, because you create the scenario
Use case – Effects of legislation
Similar to a marketing ‘conversion rate’ calculation
NIH budgeted extra $10B under ARRA
ARRA dictates internal fund distribution similar to ‘regular appropriation’ funds
U-M Med School tends to attain ‘market share’ of 1% of ‘regular appropriation’ funds
ARRA sets aside $1B for medical school facilities
$1B for medical school facilities
$9B proportionally budgeted
U-M Med School tends to attain ‘market share’ of 2.7% of ‘regular appropriation’ funds to medical schools
x 2.7% = $27M x 1% = $90M
Use case – Effects of legislation
Similar to a marketing ‘conversion rate’ calculation
NIH budgeted extra $10B under ARRA
ARRA dictates internal fund distribution similar to ‘regular appropriation’ funds
U-M Med School tends to attain ‘market share’ of 1% of ‘regular appropriation’ funds
ARRA sets aside $1B for medical school facilities
$1B for medical school facilities
$9B proportionally budgeted
U-M Med School tends to attain ‘market share’ of 2.7% of ‘regular appropriation’ funds to medical schools
x 2.7% = $27M x 1% = $90M
Proposals submitted, not funded
$82M
Noted sensitivity to market share %
Use case – Revenue projection
Awards
Fiscal Year
Use case – Revenue projection
Awards
Fiscal Year
Use case – Revenue projectionAwards ($)
Fiscal Year
Current FY
Use case – Revenue projection
Awards
Fiscal Year
Proposals
Use case – Revenue projection
Awards
Fiscal Year
Proposals
Use case – Revenue projectionAwards ($)
Fiscal Year
Current FY
Use case – Revenue projectionAwards ($)
Fiscal Year
Current FY
Method – Prediction intervalsFor unknown population mean and variance, the endpoints of a 100p%
prediction interval for Xn + 1 are:
Sample mean
Sample standard deviation
Observations
100((1 + p)/2)th percentile of Student's t-distribution with n − 1 degrees of freedom
Method – Prediction intervals
Sample mean
Upper Endpoint
Lower Endpoint
Method – Monte Carlo simulation
Use random sampling to work around difficult or impossible deterministic problems
Variable 1
Variable 2
Variable 3
Result
Best Practices‘Gut check’ (Expectations ~ Results?)Litmus testSensitivity analysis
Adjust for inflation
CommunicationAlways communicate uncertainty,
particularly sensitive outcomes
Source: CBO http://www.cbo.gov/ftpdocs/100xx/doc10014/03-20-PresidentBudget.pdf p34