Quantitative Portfolio Management · ©2014 Murray R. Cantor Applying this approach ! Getting the...
Transcript of Quantitative Portfolio Management · ©2014 Murray R. Cantor Applying this approach ! Getting the...
©2014 Murray R. Cantor
Quantitative Portfolio Management Murray Cantor Cutter Senior Consultant
©2014 Murray R. Cantor
Some things to consider n Are you managing your projects to
get the most value from your organization? • Do you know how much you should
invest in a given effort? • Are you addressing uncertainty in your
business cases to balance your portfolio between innovative efforts and low hanging fruit?
n How do you know when you are ready to deploy? • Do you know how much financial risk
you are assuming when you deploy the offering?
• Do you know when it makes economic sense to deploy the offering (when the expected benefits outweigh the expected expenses?
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Strategic alignment may not answer the questions n The staff with the strongest
opinions will prevail in getting their pet projects funded • “My project is clearly more strategic
than yours.”
n Difficult conversations with stakeholders such as the CFO who wants to know if the money is being spent wisely. • CFO: “Costs are real money, benefits
are soft.”
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Taking an economic perspective helps the stakeholders reason together
n What is the value of our project portfolio?
n How can I get the most value from our finite resources? • Money • Staff time • Calendar time
Key question: “What is the value of an (incomplete) software or IT project?”
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A conventional answer: $0 till shipped
n The conventional wisdom: • Fails to acknowledge value of work already done • Provides no opportunity for ongoing value management
n Can only quantify cost, not value
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0 6 Ship date
Valu
e
If all unshipped efforts are worthless, the only discussion available is some form of “strategic alignment”
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Another approach: Things are worth what someone (maybe you) might pay for them
n Imagine (if you will) you could buy or sell incomplete development programs
n The buyer would spend money now to obtain the option to invest in completing the program to receive its benefits
n How would one reason about the fair price? • The buyer, reasoning like an investor, to compute fair price needs
– The costs to complete, C – The benefits to be received, B
• With these probabilities, one can reason about value of V=B-C
The economists call this “incomplete market reasoning”
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For example: Consider the Airbus A350 program n They have delivered no planes, but
they have 750 orders @ ~ $350M each (comes to ~ $260B).
n If they were to sell the program to Boeing, what would be a fair price? • Certainly not $0!
n The buyer would get the right to complete the effort to get the future benefits (a kind of option).
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n Need to estimate: • C, the costs of completing development and bringing to market • B, the benefits: revenue, after delivery maintenance, etc. for the
current and future orders
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The two challenges:
1. The future costs and benefits are uncertain
2. The benefits may be intangible
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Dealing with Uncertainty
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Capturing uncertain quantities
n Sometimes you do not (or cannot) know a quantity you need to make a decision or carry out a calculation.
n Even so, you are not completely ignorant. • For example, I know there is
zero probability that GM will sell a trillion cars next year.
n One can use probability distributions is to describe what we know or believe the quantity to be.
0 2 4 6 8 10
0.0
50.1
00.1
50.2
0uncertain quantity
Pro
babili
ty
The height shows the probability of the quantity being near a value. In this case, it is most probable value is near 5.
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Capturing uncertain values probabilistically n For an uncertain quantity, Q, one can elicit from subject matter experts:
• High value, H (there is little chance that Q > H) • Expected value E, (of all possible values Q = E is the most likely) • Low value, L (there is little chance that Q < L)
n The inputs are captured in a triangular probability distribution
L
E
H
This is common “best case, worse case, likely case” elicitation
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One can do arithmetic on uncertain quantities
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The result is also given probabilistically
Statistics of Sum Mean: 14.667 Median: 14.495 SD: 2.2239 Variance: 4.9459 Lower Percentile: 25.0 (13.061) Upper Percentile: 75.0 (16.191)
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Example Software Project Template
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Example Software Project
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B - C
C
B
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Example of a benefits model: Using models from flight safety engineers and aviation economists
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This example shows the investment value is close to break even
n What is this telling us? • The most likely value is about $285k • There is a 25% chance this effort would lose more than $390k • There is a 25% chance that that this effort would yield more than $960k
n What to explore: • How does this investment compare to others? • Could I make this a better investment by changing project plan?
– Earlier market window? – Invest more in quality, less after market costs?
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The investment value distribution gives a view of the value and risk (uncertainty) of the investment
n The mean of the distribution is its fair value
n The standard deviation is a measure of its risk.
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This provides the basis for a portfolio view
The mean
Normalized SD
A program
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Applying this approach n Getting the most value:
• Ongoing portfolio management: Use emerging information to get early indication of failing efforts to “fail fast.”
• Multiple scenarios: e.g., balance deploy date against feature set • Instrument Lean Startup: Decide when ready to pivot
n Manage ready to deploy: • For example, to balance further investment in quality against less after
market expense
n Provide economics of efforts to finance, business management (expected NPV, ROI, with amount of uncertainty)
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Intangibles
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Dealing with Intangibles n A government agency, mission-based
organization, or not-for-profit is faced with how best to invest to further in its mission
n To deal with this challenge, one starts with the questions:
• What exactly are our goals? • How would we know they are being
achieved?
n With the answers, one can determine a useful measure of benefit from the investments.
• Examples may include lives saved, failures avoided, forested acres preserved
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Sometimes one can work with economists to ascribe an economic value to the benefit, sometimes not.
©2014 Murray R. Cantor
Modeling with non-monetary benefits
n Can’t compute B-C, different ratios
n But can compute B/C (essentially a ROI)
n Examples: • (lives saved)/$ • (acres preserved)/$
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To adopt
n Initial Workshop • Agree on level of detail needed (simple decision to full financial model) • Identify first one or two investments to be modeled • For each investment
– Agree on cost and benefit streams – Identify subject matter experts – Elicit
> Domain business models > Initial estimates
• Build integrated value • Sanity check output and revise if necessary
n Expand to more investments • Build portfolio view
n Over time, revise models with updated information • Track trend of program values (well managed programs accrue value over
time)
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Strengths of this approach
n Integrates the knowledge, beliefs, and concerns of all the subject matter experts: • Surfaces the assumptions • Provides a framework for identifying and addressing risks
n Enables delivering best case, worse case scenarios for finance
n Provides a dispassionate framework for decision making
n Provides an opportunity for incorporating new information to improve models as data rolls in • Track value creation (or not).
n Can be elaborated to include financial measures such as probabilities of NPV, RoI(s), etc.
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