Prospect TheoryProspect 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 5
Prospect TheoryWhen undertaking M&A loss aversion may lead to missingout on ‘good’ deals - think in a strategic broader mannerWhen a deal seems ‘bad’ if in the loss range one mayundertake more risk – need to cut ones losses 6
ValuationFor 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 integrationMarketing, Sales, Finance, Operations, HR, Strategy– each functional area could have different goalswithin the overarching corporate goal 7
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 8
Individual Biases• Overconfidence bias• Escalation of commitment• Sunk cost fallacy• Confirmation bias (related to overconfidence….) Seeing what you want to see Selective seeing, listeningIn M&A ignore information that does not fit in withyour preconceived notion 9
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 10
Individual Biases• Anchoring - initial anchor or reference point matters• Decoy pricing• Affect bias• Framing bias – positive vs. negative framing 11
Individual BiasesM&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
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 thoseThree stages Preliminary due diligence Bidding and deal structuring Final Phase 13
DebiasingPreliminary 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 14
DebiasingBidding and deal structuring Winner’s curseFinal 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 15
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 timesQ: Given that the witness identified the cab as Blue Cab,what is the probability that the cab involved in the accidentwas a Blue Cab rather than a Green Cab? 16
Default PredictionConsider 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 17
Group Decision MakingDebrief• Round 1 : Vote after seeing private signal• Round 2: Vote after seeing private and 2 public signals 18
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