Advanced Behavioral Finance – Summary
All authors are marked blue :)
Traditional investors
Assumptions
- investors and managers are generally rational
- markets are efficient
- markets include all the information available (= efficient market hypothesis)
- part of the inefficiency (=the irrationalities) can’t be explained
Behavioral investors
--> explains gap between traditional finance and the reality
- some agents are not fully rational
- asset prices deviate from fundamental values (= PV (cash flows))
- impact on corporate finance decisions, investments and asset prices
Rationality
4 criteria
- based on choice maker’s current assets
- based on possible consequences of choice
- agent make choices consistent with expected utility framework
- if agent receives new information their choices are updated
Arbitrage
If an asset is not prices right you would buy/sell the mispriced and get return
Limits to arbitrage = in reality this is not always the case
Market efficiency
= prices are equal to fundamental value (= PV (cash flows))
Arbitrage concepts keep the market efficient because mispricing is corrected immediately
Information incorporated in price:
Strong form = all public + private
Semi-strong form = all public
Weak form = past public
Behavioral finance
- some agents are not fully rational
1 fail to update beliefs correctly (with all available information)
2 make choices that are normatively unacceptable
Limits to arbitrage
1 rational agents can’t correct irrationality of other investors
2 mispricing can have substantial and long impact
Risks and costs allowing mispricing:
1 Fundamental risk
- risk that asset loses value
- finding a perfect hedge is usually impossible
2 noise trader risk (= always in the same direction as mispricing, so is bad for the arbitragers)
- mispricing in short run because of investor sentiment/noise short term information
- arbitragers might liquidate prematurely
Sufficient conditions to limit the arbitrage
Arbitrageurs are risk-averse and have short horizons
- arbitrageurs can’t afford to be patient
- creditors and investors evaluate the arbitrageur based on his returns
- forced closure of a short position
Noise trader risk is systematic
3 implementation costs
- costs that make it less attractive to exploit the mispricing
Transaction costs, short-sale constrains and information costs
Horizon risk and synchronization risk (=when an arbitrager doesn’t know if other arbitrager will exploit an opportunity)
- behavioral biases
1 irrational decisions are not random
, 2 systematic form of irrationality that creates mispricing
3 belief formation: how agents form expectations
4 decision-making: how agents evaluate risky decisions
Finding evidence on limit to arbitrage
You need to know
1 fundamental value
2 joint hypothesis problem (bad-model risk)
Any test of mispricing = joint test of mispricing and of asset pricing model
Twin shares
= assets that represent the same single operating business but trade in two different companies
- separate stock exchange listings
- shares represent claims on exactely the same underlying cash flows
limits to arbitrage
- fundamental risk none
- noise trader risk ?
- implementation cost small
Other examples of limits to arbitrage
1 index inclusion of stocks: when being included in ETF, price increases even though fundamental value does not change
- fundamental risk + noise trader risk
2 close-ended funds: issue a fixed number of shares traded on exchanges
- fund share prices differs from NAV (= net asset value)
- noise trader risk
- Lee, shleifer and Thaler
3 bubbles
- limit short-selling
- housing bubble = short selling is not directly possible
- Graffin, Harris, Shu and Topaloglu
4 equity carve-outs (=carve out piece of company and create subsidiary, then sell minority of shares and rest of shares later)
- Lamont and Thaler
sample selection in the paper:
carve-outs in which the parent retains at least 80% of subsidiary
parent intents to distribute the remaining shares to shareholders
1 equity carve-out (partial public offering)
= IPO for shares, usually minority stake,in a subsidiary company
2 spinoff
= parent company gives remaining shares with all the ownership in subsidiary to parents shareholders , no money changes
Stub
= implied value of a company’s spinoff, in assets and business
Constructing stubs by: s0 = (P0,P – xP0,S) / P0,P = S0 / P0,P
s0 = closing price parent - #shares of S received per share of P * closed price S
s0 = percentage of mispricing
-->3Com and Palm example
- 5% of the shares of Palm were 2vailable
- Negative stub by 77%, because there was low supply of Palm stocks
- Stub remains negative for longer than two moths
- Then after that the price was corrected because after two months the rest of the 95% shares were available so the price
of Palm decreased relatively, and therefore the price of 3Com increased
- Palm option prices
No put-call parity
Very high shorting costs
Options and equity market are segmented (can be caused by limits to arbitrage): investors hold on the to stock
even though there exists portfolio with higher return
Investment strategy in stubs
Long in the parent and short in the subsidiary
= want parent zal gaan stijgen (die is relatief underpriced) en de subsidiary die is overpriced (die zal dalen want er komt meer
aanbod)
,Short sale constraints
Shortage of supply = you cant get the assets easily
Lender fee = you have to pay interest if you want to borrow a stock
Short interest = if a lot of people see potential arbitrage with shorting --> short interest increases
Synthetic short with options
= use options to simulate the payoff of a short stock option
Put call parity for options = law of one price = c + Ke-rT = p + S0
- Buy at the money puts and sell at the money calls = practically same as shorting
- Borrow present value of the strike price
- Synthetic short in value see graphic
Representativeness
Argents determine whether and event of sample is representative based on
- Similarity of sample to the parent population
- Reflection of randomness
= Kahneman and Tversky definition
2 bias generation by representativeness
1 Sample size neglect = believe in law of small numbers
You think that a small sample is a good representation of the actual population --> you take too little data points
People see trends to quickly in random data, FE:
Stock market fluctuations = the P/E ratio of the mark market fluctuates
significantly over time but without reasons --> see picture
Momentum and return reversals
Return-chasing behavior among mutual fund investors (Franzzini and
Lamond, Goyal and Wahal)
Formation of asset bubbles
2 Base rate neglect = you forget the basic rate that occurs and think a effect to be bigger than it actually is
Volatility puzzle
Volatility of returns is higher than the volatility of earnings growth
Variation in price-earnings ratio can come from:
1 variation expected earnings growth = g
Shiller rejects the idea that P/E ratio predicts cash-flow growth
2 variations in discount rate = r by
- variation in expected future risk-free rate
- changing forecast of risk
- changing risk aversion
Campbell and Cochrane habit model
3 belief-based behavioral models
extrapolate trends, FE: if stock market has done well/bad in the past, investors think it will keep doing well/bad
, Biases
1 Representativeness
2 overconfidence
3 other biases
Overconfidence
1 Overoptimism and wishful thinking
- people tend to overestimate their own abilities, they think they’re above average on various dimensions (80% of people)
- systematic planning fallacy = we tend to underestimate the time (and costs) needed to complete a task
Malmendier and Tate
2 Over precision (miscalibration)
- people are too confident in the accuracy of their beliefs
- 90% of confidence intervals contain the correct answer less than 50% of the time
Ben-David Graham and Harvey
3 Illusion of control
- Thinking you’re in the control of a situation
4 Self-attribution bias
- You think you created the good results, but the bad result are the cause of something external
5 Hindsight bias
- You think you could have predicted something
6 Applications to be discussed (session 3)
Other behavioral biases
1 belief perseverance
= people hold to opinions too tighlt and for too long
2 confirmation bias
= people tend to search for evidence that confirm their belief
3 anchoring
= people relate to an irrelevant value as a reference point and make insufficient adjustments afterwards
Baker, Pan and Wurgler
4 Availability bias
= when judging for probability of an event, people search their memories for relevant information, but memories are not all equally
available so it is estimated wrong
Normative vs Descriptive
Expected monetary value ∑pixi
Expected utility theory ∑piu(xi)
Prospect theory ∑π(pi)v(xi)
Expected utility theory
- Normative model of rational choice = how should we behave?
- Consider different possible future outcomes
- Describe how good and bad something would be, weight each outcome by its probability and sum up
1 certainty effect
Even though B has smaller expected value, people choose for
certainty
Apparently when they are both uncertain or it differs not too much
people are okay choosing uncertainty
2 reflection effect
You take risk with high possible losses, but want more certainty when gaining
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