Heuristics and Biases

1. Heuristics and Biases

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.

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

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)

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.

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.

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…

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.

9. 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. 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. 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

12. 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.

13. 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.

14. 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.

15. 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

16. 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!

17. 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…

18. 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

19. 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.

20. 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

21. 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

22. 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

23. Lottery Stock Stock that is similar to lottery ticket: low price, low chance of winning, but offers high reward when win. Some investors prefer to invest in lottery stocks. Gamblers prefer a lottery stock even its odds is unfavourable. They prefer a lottery stock because of its positively skewed payoff: the payoff can either take a large positive value with a small probability or a small positive value with a large probability.

24. An Example: Chinese Warrants Bubbles Xiong and Yu (2011)

25. In 2005–2008, 18 Chinese companies issued put warrants with long maturities ranging from six months to two years. These warrants give their holders the right to sell the issuing companies’ stocks at predetermined strike prices during a prespecified exercise period.

26. For each warrant, billions of yuan was traded with an average daily turnover rate over 300 percent, and at substantially inflated prices. Several features make these warrants particularly appealing for analyzing bubbles: First, we can reliably measure the warrants’ fundamental values to be close to zero by using the underlying stock prices; second, the publicly observable stock prices also make the warrant fundamentals observable to all market participants; and third, these warrants have predetermined finite maturities.

27. Explanations: • The restrictive legal ban on short-selling financial securities (including warrants) in China • investors’ heterogeneous beliefs • Investors see warrants as gambles

28. Self Control Empirical studies on measuring self-control problems among individuals have found a negative relationship between measured self-control and the accumulation of wealth (Ameriks et al., 2003, Ameriks et al., 2007). Lack of self-control can be linked to overspending.

29. Time Preference • Q1. Choose between A and B in each of the following: • (1)A：$35 today -------- B：$40in one month • (2)   A：$30 today -------- B：$40in one month • (3)   A： $25 today -------- B：$40in one month • (4)   A： $20 today -------- B：$40in one month • (5)   A： $15 today -------- B：$40in one month • (6)   A： $10 today -------- B：$40in one month

30. Time Preference • Q2. Choose between A and B in each of the following: • (1)A：$35 in one month --- B：$40元 in two month • (2)   A：$30 in one month --- B：$40元 in two month • (3)   A： $25 in one month --- B：$40元 in two month • (4)   A： $20 in one month --- B：$40元 in two month • (5)   A： $15 in one month --- B：$40元 in two month • (6)   A： $10 in one month --- B：$40元 in two month

31. Time Preference An individual is said to have present bias if he/she is less willing to wait when he/she can receive the money today in Q1. e.g., always choose B in Q2, and always choose A in Q1. Answers for the above two Qs can be used as an measure of self-control problem.