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Ch05.pdf
1.
Chapter 5: Heuristics
and Biases Powerpoint Slides to accompany Behavioral Finance: Psychology, Decision-making and Markets by Lucy F. Ackert & Richard Deaves ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part. 1
2.
Perception and processing constraints •
Expectations influence perceptions. • People see what they want to see. • People experience cognitive dissonance when they simultaneously hold two thoughts which are psychologically inconsistent. 2 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
3.
Perception and the
frame • Perception is not just seeing what’s there – but it is influenced by the frame: – How tall is that sports announcer? – Halo effects: Someone who likes one outstanding attribute of an individual likes everything about the individual – Primacy vs. recency effects 3 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
4.
Memory tricks • Memory
is not a simple matter of information retrieval: – It is reconstructive – It is variable in intensity… • With emotion playing a role – It is prone to self-serving distortion (hindsight bias) 4 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
5.
Heuristics • Heuristics or
rules-of-thumb: decision-making shortcuts. • Necessary because the world, being a complicated place, must be simplified in order to allow decisions to be made. • Heuristics often make sense but falter when used outside of their natural domain. 5 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
6.
Type 1 &
2 heuristics • Type 1: Autonomic and non-cognitive, conserving on effort. – Used when very quick choice called for – Or when it’s “no big deal” • Type 2: Cognitive & requiring effort. – Used when you have more time to ponder • Type 2 can overrule Type 1. 6 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
7.
Self-preservation heuristics • Hear
a noise with an unknown source? – Move away till you know more • Food tasting off? – Stop eating it • These make good sense. • Other heuristics, which are more cognitive, are related to comfort with the familiar… 7 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
8.
Example: Diversification heuristic •
Observe people at a buffet… –Many people are trying a bit of everything –Nobody wants to miss out on something good • Diversification sometimes comes naturally. 8 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
9.
Example: Ambiguity aversion •
In experiments, people are more willing to bet that a ball drawn at random is blue if they know the bag contains 50 red and 50 blue. – Than if they know a bag contains blue and red balls in unknown proportions • Lesson: people are more comfortable with risk vs. uncertainty (ambiguity). 9 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
10.
Example: Status quo
bias or endowment effect • What you currently have seems better than what you do not have. • Experimental subjects valued something that they possessed (after it was given to them) more than they would have if they had to consciously go out and buy the item. 10 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
11.
Example: Information overload •
Experiment involving tasting jams and jellies in a supermarket. • Treatment 1: Small selection. • Treatment 2: Large selection. • Which attracted more interest? – Treatment 2. • Which lead to more buying? – Treatment 1. 11 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
12.
Representativeness • People judge
probabilities “by the degree to which A is representative of B, that is, by the degree to which A resembles B.” – A can be sample and B a population OR A can be a person and B a group OR A can be an event/effect and B a process/cause • Behaviors associated with representativeness: – Conjunction fallacy – Base rate neglect/underweighting – Hot hand – Gambler’s fallacy – Overestimating probability 12 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
13.
Conjunction fallacy • Which
seems more likely? – a. Jane is a lottery winner. – b. Jane is happy lottery winner. • Many pick b, but a must have a higher probability, as a Venn diagram clearly shows. • Problem: conjunction fallacy. 13 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
14.
Conjunction fallacy: Venn
diagram 14 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
15.
Base rate neglect
and Bayes’ rule • pr(B|A) = pr(B) * [pr(A|B) / pr(A)] • This is a way of updating your probability estimate based on new information. • You have a barometer that predicts weather. • Example: – pr(rain) = pr(R) = 40% – pr(dry) = pr(D) = 60% – pr(rain predicted | rain) = pr(RP|R) = 90% – pr(rain predicted | dry) = pr(RP|D) = 2.5% 15 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
16.
Bayes’ rule cont. •
Best prediction of tomorrow’s weather without looking at barometer is prior (base rate) distribution: you would say 40% chance of rain. • What should you predict when barometer predicts rain? That is, what is probability of rain conditional on rain being predicted? • pr(R|RP) = pr(R) * [pr(RP|R) / pr(RP)] 16 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
17.
Bayes’ rule cont.
ii. • We first need to work out pr(RP). • This equals: pr(RP R) + pr(RP D) • Use conditional probability rule: pr(RP|R) = pr(RP R) / pr(R) • Re-arrange: pr(RP R) = pr(RP|R) * pr(R) = .9 * .4 = .36 17 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
18.
Bayes’ rule cont.
iii. • Next work out pr(RP D). • Begin with conditional probability: pr(RP|D) = pr(RP D) / pr(D) • Re-arrange: pr(RP D) = pr(RP|D) * pr(D) = .025 * .6 = .015 • Therefore pr(RP) = .36 + .015 = .375 • Note that the barometer (conservatively) predicts rain less than it actually rains. 18 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
19.
Using Bayes’ rule •
Best prediction of tomorrow’s weather without looking at barometer is prior (base rate) distribution: you would say 40% chance of rain. • What should you predict when barometer predicts rain? pr(R|RP) = pr(R) * [pr(RP|R) / pr(RP)] = .4 * (.9 / .375) = .96 • Base rate underweighting would imply that you believe there is a higher than .96 chance of rain conditional on rain being predicted. 19 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
20.
Hot hand phenomenon •
Sometimes people feel that distribution/population should look like sample, but sometimes they feel sample should look like distribution/population. – Former is especially true if people aren’t sure about nature of distribution/population. – As in hot hand phenomenon in sport: • In basketball, it is erroneously thought that you should give ball to hot player 20 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
21.
Gambler’s fallacy • Gambler’s
fallacy may apply if people are fairly sure about nature of population. – They think even small samples should always look like population. • So if you flip coin 9 times getting 6 heads and 3 tails, these people would say that a tail is more likely to come next… • “We are due for heads.” – Winning lottery numbers are avoided based on mistaken view that they are not likely to come up again for a while. 21 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
22.
Overestimating predictability • Tendency
to underestimate regression to mean – amounts to exaggerating predictability. • GPA example: subjects were asked to predict GPA in college from high school GPA of entrants to the college. – High school average GPAs: 3.44 (sd = 0.36); GPA achieved at college was 3.08 (sd = 0.40). – One student was chosen: high school GPA of 2.2. – People underestimated mean regression for this low- achiever. 22 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
23.
Biases related to representativeness •
Recency: – Recent evidence is more compelling. • Salience: – Dramatic evidence is more compelling. • Availability: – Freely available, easily processed information is more compelling. 23 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
24.
Anchoring • People are
initially anchored on their prior belief. • Quickly multiply these eight numbers: 1 * 2 * 3 * 4 * 5 * 6 * 7 * 8 – Most people will come up with a low estimate: anchored on product of first 4 or 5. – A bit better (but still too low) with: 8 * 7 * 6 * 5 * 4 * 3 * 2 * 1 24 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
25.
Anchoring bias: Example
of anchoring to irrelevant info • Wheel with numbers 1-100 was spun. – Subjects were asked: • 1. Is the number of African nations in the UN more or less than wheel number? • 2. How many African nations are there in the UN? – Answers were highly influenced by wheel: • Median answer was 25 for those seeing 10 from wheel. • Median answer was 45 for those seeing 65 from wheel. – Grasping at straws! 25 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
26.
Anchoring vs. representativeness •
Anchoring says new information is discounted. • Representativeness (base rate neglect variety) says people are too influenced by latest information. • Potential conflict between anchoring and representativeness in how people deal with new evidence. • Which is right? – Perhaps both depending on situation… 26 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
27.
Anchoring vs. representativeness
ii. • It is argued that people are “coarsely calibrated.” • Suppose morning forecast is for sun. Day starts sunny. You go on a picnic. – Some dark clouds start to move in – You are anchored to prior view and discount clouds – More dark clouds: the same thing 27 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
28.
Anchoring vs. representativeness
iii. – Even more dark clouds. – Now you coarsely transition – thinking that “it’s going to rain for sure!” – What is reality? Never 0% or 100%. New information should alter probabilities but a flip- flop doesn’t make sense. • Coarse calibration has been used to explain tendency for prices to trend and eventually reverse. 28 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
29.
Preview of financial
errors from heuristics and biases • Expectations influence perceptions: – If most people are saying good/bad things about company, you will “find” good/bad things • It has been argued that cognitive dissonance can: – Explain why people don’t exit poorly-performing mutual funds 29 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
30.
Preview of financial
errors from heuristics and biases ii. • Diversification heuristic – Stock-bond menu influences risk taking in DC plans • Ambiguity aversion – Under-diversification • Information overload – Lower participation rates for DC plans with more investment choices 30 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
31.
Preview of financial
errors from heuristics and biases iii. • Representativeness (and halo effects) – “Good companies are good stocks” thinking may lead to value advantage • Recency – May explain chasing winners • Anchoring and slow adjustment coupled with representativeness – May explain momentum and price reversal 31 ©2010 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly available website, in whole or in part.
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