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

Q & A

Jeff Horonjhoron@umich.edu

http://www.umich.edu/~jhoron/