1. Valuation Pitfalls
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2. Agenda
Expected Utility
Prospect Theory
Valuation
Individual Decision Making Biases
Debiasing
Group Decision Making
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3. Expected Utility
Are individuals expected utility maximizers?
EU is based upon four main axioms
 Completeness
 Transitivity
 Independence
 Continuity
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4. Expected Utility
Allais paradox
St. Peterberg paradox
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5. Prospect Theory
Prospect Theory: Loss Aversion
 Briefly, people generally prefer to avoid losses
rather than having gains
 Psychologically the losses seem to be 2.5 times
more powerful than the gains
 Kahneman and Tversky demonstrated under
highly controlled experimental setting that
individuals are not expected utility maximizers at
least under certain conditions
 Basically risk seekers in loss domain and risk
averse in the gains domain
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6. Prospect Theory
When undertaking M&A loss aversion may lead to missing
out on ‘good’ deals - think in a strategic broader manner
When a deal seems ‘bad’ if in the loss range one may
undertake more risk – need to cut ones losses
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7. Valuation
For valuation of a target
• Revenues (sales, price per unit)
• Costs (FC,VC, allocation to merger)
• Synergy (ops, distribution, new markets)
• Cost of capital (market estimates – how exact?)
• Industry – cost leadership, product differentiation
(competitive scope, competitive advantage)
• When do you realize the synergies – time for
implementation and integration
Marketing, Sales, Finance, Operations, HR, Strategy
– each functional area could have different goals
within the overarching corporate goal
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8. Valuation
• Each manager brings personal experience and
insights into the process
• While managements interests are considered as
aligned with the corporate strategy, there are
several examples when management has not
acted in an optimal manner
• This is the basis for understanding decision
making process and individual biases that could
hamper negotiations and affect final outcomes
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9. Individual Biases
• Overconfidence bias
• Escalation of commitment
• Sunk cost fallacy
• Confirmation bias (related to overconfidence….)
 Seeing what you want to see
 Selective seeing, listening
In M&A ignore information that does not fit in with
your preconceived notion
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10. Individual Biases
• Assign probabilities to preconceived notions and
certain events
• Winners Curse (the selection bias that arises because
a bidder tends to win more often when his/her value
estimate is too high than when it is too low)
Arises due to uncertainty where
 Bidders have access to different information,
 Bidders interpret the same information differently,
 Valuation of items is a complicated and subjective
process
Example: possibly the purchase of ABN AMRO in 2007
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11. Individual Biases
• Anchoring - initial anchor or reference point
matters
• Decoy pricing
• Affect bias
• Framing bias – positive vs. negative framing
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12. Individual Biases
M&A application
 In relative valuation we base decisions on known
anchors and often do not adjust sufficiently to arrive
at ‘correct’ value
 In uncertain situations, even with fundamental
analysis and valuation we tend to get to a known
value or anchor – underlying influence
 Initial anchors at at times arbitrarily formed
 When valuation is based on readily available
information it may be subject to availability bias,
recency and extreme data
 When decisions are based on intuition watch out for
‘affect’ bias 12

13. Debiasing
 When CEOs take a targeted debiasing approach
to M&A the probability of success increases
 The first step is to identify the cognitive biases and
then steps to overcome those
Three stages
 Preliminary due diligence
 Bidding and deal structuring
 Final Phase
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14. Debiasing
Preliminary due diligence
 Confirmation bias – seek out discomforting
evidence
 Overconfidence – use a reference class of
comparable, past deals to estimate synergy,
consider industry averages, listen, curb
aggressive stance and intuition
 Underestimation of cultural differences
 Planning fallacy
 Conflict of interest
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15. Debiasing
Bidding and deal structuring
 Winner’s curse
Final Phase
 Anchoring – seek new data and uninterested
parties provide new valuation or appropriate
updating
 Sunk cost fallacy – overcome by knowing when
to pull back and fold; have back up plans and
alternate options
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16. Taxicab problem
• There are two taxicab companies operating in a city: Green
cab co. and Blue cab company
• 85% of the cabs in the city are Green Cabs and 15% are
Blue Cabs
• A hit and run accident takes place at night
• A witness identified the cab as Blue Cab
• The court tested the reliability of the witness under the
conditions that existed on the night of the accident
• They concluded that given the actual color, the witness
correctly identified it 80% of the times
Q: Given that the witness identified the cab as Blue Cab,
what is the probability that the cab involved in the accident
was a Blue Cab rather than a Green Cab?
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17. Default Prediction
Consider the following:
• Suppose you know that the aggregate loan default rate is
2%
• Your model predicts 80% of the defaults correctly; given a
default, your model predicts it correctly 80% of the times
• The model also predicts 15% of the defaults incorrectly
(false positive) – the loan will not default but the model
says the loan will
• Suppose you originate a new loan what is the probability
that it will default, given that the model predicts it will
default
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18. Group Decision Making
Debrief
• Round 1 : Vote after seeing private signal
• Round 2: Vote after seeing private and 2 public
signals
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