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1 (of 22) FIN 468: Intermediate Corporate Finance Topic 11–Behavioral Corporate Finance Larry Schrenk, Instructor.

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Presentation on theme: "1 (of 22) FIN 468: Intermediate Corporate Finance Topic 11–Behavioral Corporate Finance Larry Schrenk, Instructor."— Presentation transcript:

1 1 (of 22) FIN 468: Intermediate Corporate Finance Topic 11–Behavioral Corporate Finance Larry Schrenk, Instructor

2 Topics Foundations of Behavioral Finance Anomalies and Applications to Corporate Finance

3 A Cautionary Note In my Opinion–but Not Necessarily in the Opinion of my Colleagues in Finance. 1. There is good evidence for the empirical examples I will present. 2. The theoretical explanations are more speculative, and almost every claim in these areas is controversial. 3 (of 70)

4 Foundations of Behavioral Finance

5 Three Areas of Study Anomalies▪ Evolutionary Psychology 5 (of 70) Neuroeconomics▪

6 1. Anomalies Deviations from Classical Economic Behavior  ‘Irrationalities’ Only Relevant if Systematic 6 (of 70)

7 Anomaly Example Framing 'Asian Disease' Experiment  Given an outbreak of the Asian disease, and 600 people who are going to die, which plan should be implemented?  Plan A or Plan B? Same Choice, but Two Alternate Frames 7 (of 70)

8 Frame 1–’Life’ Language Plan A  200 people are saved. Plan B  33% chance that all will be saved  67% chance that none of them will be saved 8 (of 70)

9 Frame 2–’Death’ Language Plan A  400 people die. Plan B  33% chance that all will be saved  67% chance that all will die. 9 (of 70)

10 Results Frame 1–Life Language  A72%Certain Choice  B28%Risky Choice Frame 2–Death Language  A22%Certain Choice  B78%Risky Choice 10 (of 70)

11 2. Evolutionary Psychology Reasoning  Human Organs Evolve in Response to Environment  Brain is a Human Organ  Our Brian has Evolved in Response to Environment encountered during Human Evolution 11 (of 70)

12 Evolutionary Psychology Traditional Brain View  Tabula Rasa–’Blank Slate’  Flexible; Open to Any Programming Evolutionary Psychology (one version)  Modular Brain  Neural circuits designed for problems faced during our evolutionary history.  Question of Universality Domain-Specificity vs. Domain-Generality 12 (of 70)

13 Evolutionary Psychology When Did the Brain Evolve?  Pleistocene Period 1.8M to 10,000 years ago  Human Evolution Homo erectus 1.6M years ago Homo sapiens200,000 years ago Permanent Human Settlements10,000 years ago  Hunter-Gatherer Societies for 99% of Human Development 13 (of 70)

14 Evolutionary Psychology Possible Evolution 10,000 to present  Lactose Intolerance Example Future Evolution Time Scale 14 (of 70)

15 Evolutionary Psychology Roughly phrased… We are using hunter-gatherer modules to confront 21 st century problems, or Our modern skulls house a stone age mind. 15 (of 70)

16 Evolutionary Psychology Examples Evolved Fear-Learning Psychology  Data: While spiders and snakes kill far less than guns, people nonetheless learn to fear spiders and snakes about as easily as they do a pointed gun, and more easily than an unpointed gun, rabbits or flowers.  Explanation: Spiders and snakes were a threat to human ancestors throughout the Pleistocene, whereas guns (and rabbits and flowers) were not. 16 (of 70)

17 Evolutionary Psychology Examples Hunting Hypothesis and Human Coalitions Mating Behavior 17 (of 70)

18 3. Neuroeconomics Study of how the brain functions when making decisions. Neuroimaging, e.g., fMRI, PET Scans Imaging brain activity to infer how the brain works. Issues  Automatic versus Controlled Processes  Emotions 18 (of 70)

19 Neuroeconomics fMRI 19 (of 70)

20 Neuroeconomics 20 (of 70)

21 Neuroeconomics and Framing “Activity in the frontal and parietal cortices suggests that working memory and imagery mechanisms are involved differentially in choosing risky versus sure options” “fMRI results indicate that the certain choice is considerably less costly (in terms of cognitive effort) than the risky one when individuals choose among options framed as gains.” “Since [in negatively framed questions] participants chose the risky option more often than the certain option in response to such problems, our results suggest that people are more willing to accept a computational rather than an emotional cost, Gonzalez, et al. “The Framing Effect and Risky Decisions: Examining Cognitive Functions with fMRI.” (2005) 21 (of 70)

22 Neuroeconomics and Framing 22 (of 70)

23 Automatic versus Controlled Processes 23 (of 70)

24 Anomalies 24 (of 36)

25 The Anomalies 1. Excessive Optimism 2. Overconfidence 3. Confirmation Bias 4. Illusion of Control 5. Representativeness 6. Availability 7. Anchoring 8. Framing 25 (of 70)

26 Excessive Optimism People overestimate favorable and underestimate unfavorable outcomes. 26 (of 70)

27 Excessive Optimism Examples Most people display unrealistically rosy views of their abilities and prospects. Typically, over 90% of those surveyed think they are above average in such domains as driving skill, ability to get along with people and sense of humor. They predict that tasks (such as writing survey papers) will be completed much sooner than they actually are. 27 (of 70)

28 Excessive Optimism and Corporate Finance Managerial overconfidence and optimism lead to overinvestment. Foreign exchange companies are more optimistic about how exchange rate moves will affect their firm than how they will affect others. Delayed cost cutting. Stock bubbles 28 (of 70)

29 Overconfidence People are overconfident in their abilities and knowledge. 29 (of 70)

30 Overconfidence Examples Confidence intervals people assign to their estimates of quantities–the level of the Dow in a year, say–are far too narrow. Their 98% confidence intervals, for example, include the true quant. People poorly estimate probabilities:  Events they think are certain to occur actually occur only around 80% of the time, and  Events they deem impossible occur approximately 20% of the time. 30 (of 70)

31 Overconfidence Examples Expertise, too, is often a hindrance rather than a help Experts, armed with their sophisticated models, have been found to exhibit more overconfidence than laymen, particularly when they receive only limited feedback about their predictions. 31 (of 70)

32 Overconfidence and Corporate Finance Research shows that professionals from many fields exhibit overconfidence in their judgments, including investment bankers, engineers, entrepreneurs, lawyers, negotiators, and managers. Overconfidence can lead to investment distortions, predominantly overinvestment. Economic overconfidence, e.g., forecasting business cycles. 32 (of 70)

33 Confirmation Bias People put too much confidence in information that supports their own view. 33 (of 70)

34 Confirmation Bias Examples People are reluctant to search for evidence that contradicts their beliefs. Even if they find such evidence, they treat it with excessive skepticism. Some studies have found an effect whereby people misinterpret evidence that goes against their hypothesis as actually being in their favor. 34 (of 70)

35 Confirmation Bias and Corporate Finance "Investors tend to seek out information that supports their existing point of view while avoiding information that contradicts their opinion.” (Rappaport and Mauboussin, 2001) Many executives of companies, cocooned in their own little worlds and rarely receiving negative feedback, develop their own intransigent views that are impervious to disconfirming evidence. 35 (of 70)

36 Illusion of Control People overestimate their ability to control events. 36 (of 70)

37 Illusion of Control Examples Being in control makes us feel happy. The absence of control produces withdrawal and depression. In a 1975 study Yale University students were asked to predict the results of coin tosses  A significant number of presumably intelligent Yalies believed their performance could improve through practice, and would have been hampered if they’d been distracted. 37 (of 70)

38 Illusion of Control and Corporate Finance Managers tend to overestimate their ability to lead a project to success. Online Traders Excessive belief in the control of risk. 38 (of 70)

39 Representativeness People make decisions based on stereotypes or typical/representative examples. 39 (of 70)

40 Representativeness Examples Description: Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti- nuclear demonstrations. Which is more likely: “Linda is a bank teller” or “Linda is a bank teller and is active in the feminist movement” 40 (of 70)

41 Representativeness and Corporate Finance Gambler's Fallacy: If a quarter lands on heads five times, a person incorrectly believes that the probability tails increases. It may give too much emphasis to the similarities between events (or samples), but not to the probability that they will occur. Representativeness may reduce the importance of variables that are critical in determining the event's probability. 41 (of 70)

42 Availability People put too much confidence in information that is available and more easily understood. 42 (of 70)

43 Availability Examples When judging the probability of an event people often search their memories for relevant information. More recent events and more salient events will weigh more heavily and distort the estimate. 43 (of 70)

44 Availability and Corporate Finance Availability causes us to frequently misread probabilities, and get into investment difficulties as a result. Saliency and emotional events can dominate decision-making in the stock market. The tendency of recent and salient events to move people away from the base-rate or long-term probabilities cannot be exaggerated. 44 (of 70)

45 Anchoring People use an initial value in making an estimation, but do not adjust it sufficiently or use an irrelevant number. 45 (of 70)

46 Take the last three numbers of your Social Security number and add 400. Now answer this question…  Attila and the Huns invaded Europe and penetrated deep into what is now France where they were defeated and forced to return eastward. In what year did Attila’s defeat occur? Anchoring Example

47 Anchor Mean 400-599626 400-599626 600-799660 600-799660 800-999789 800-999789 1000-1199865 1000-1199865 1200-1399988 1200-1399988 Correct Answer: 451 AD Experiment Results: The artificial and irrelevant calculation affects the estimate! Anchoring Example

48 Anchoring and Corporate Finance Some investors invest in the stocks of companies that have fallen considerably in a very short amount of time anchoring on a recent "high" that the stock has achieve. Many time investors will cling to an investment waiting for it to "break even," to get back to what they paid for it. Securities get anchored on their own estimates of a earnings or on last year's earnings.

49 Framing People allow the way a problem is described to influence their decision. Such effects are powerful. There are numerous demonstrations of a 30 to 40% shift in preferences depending on the wording of a problem. 49 (of 70)

50 Framing Examples Asian Flue (above) Suppose that you have flipped a coin five times but you don’t yet know your wins and losses. Would you play the gamble a sixth time?  60% don’t suggesting that some subjects are framing the sixth gamble narrowly, segregating it from the other gambles. 50 (of 70)

51 Framing and Corporate Finance Investments decisions are considered individually rather than in a broader framework. Investors care about annual changes in financial wealth even if they have longer investment horizons. 51 (of 70)

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