1. Behavioural Finance and Investment Beliefs

2. Twin Peaks 1 1

3. Bubbles Traditional belief: Financial Markets are efficient, in that they reflect all available information correctly and quickly - Is there an alternative view about the markets? 2 2

4. Behavioural Finance Relatively new school of thought A marriage of psychology and finance It says psychology plays a role in financial decision making Cognitive errors and biases affect investment beliefs, and hence financial choices Challenges the traditional idea that financial markets are always efficient 3 3

5. Limits to Arbitrage Why isn’t mispricing arbitraged away? If there is a significant number of irrational investors, arbitrage is risky If arbitrageurs are risk-averse, their activities will be limited, due to fundamental risk, implementation costs, and model risk Hence, mispricing can exist, particularly in the short term 4

6. Why Should We Care? To better understand our own investment behaviour, and that of others Set the right incentives for clients, pension plan (DC) design, financial product design CIBC Imperial Service: Investor Psychology 101 4 5

7. Why Should We Care? To better understand asset management companies that base their investment philosophy on behavioural finance. Examples:  The Behavioural Finance group at JPMorgan Asset Management manages ~ $10 billion  “Our behavioural finance funds seek to take advantage of investor irrationality, capitalising on the anomalies created by investor behaviour to pursue consistent capital growth.”  Value and momentum  LSV Asset Management manages ~ $60 billion  Value, long-term contrarian, and short-term momentum 6

8. Common Behavioural Biases Overconfidence Loss aversion Narrow framing Representativeness Regret avoidance Ambiguity aversion Mental accounting Anchoring 6 7

9. Overconfidence Better than average  “I am a better than average driver.”  95% of British drivers believe they are better than average (Sutherland 1992) Illusion of control  “I am unlikely to be involved in a car accident.” 7 8

10. Overconfidence As applied to investments, overconfidence may lead to excessive trading because these investors believe they possess special knowledge others do not have, such as superior predictive power and information  “Trading is hazardous to your wealth” by Barber and Odean (2000a)  Find that portfolio turnover is a good predictor of poor performance: Investors who traded the most had the lowest returns net of transaction costs 8 9

11. Barber and Odean (2000a) 9 10

12. Why Don’t They Learn? Similar results in other studies: Overconfident traders contribute less to desk profits (Fenton-O’Creevy et al. 2007) Why don’t overconfident investors learn from their mistakes?  Self-attribution bias  Attribute successes to their own ability  Blame failures on bad luck 10 11

13. Gain Loss Pleasure Pain +10% -10% Small Pleasure Big Pain Loss Aversion Prospect Theory 11 12

14. Narrow Framing Loss aversion may be a consequence of narrow framing Narrow frame of evaluation  Limited set of metrics in evaluating investments  Obsessive about price changes in a particular stock  Myopic behavior even though investment is long-term  Can lead to over-estimation of risk 12 13

15. Narrow Framing / Loss Aversion Consequence  “Disposition effect”: Tendency to sell winners too soon, and hang on to losers for too long (Shefrin and Statman, 1985, Odean 1998))  Affects design of financial products:  If investor cares more about loss, then products that limit downside risk is more attractive than products that have low volatility For example, rather than comparing Sharpe ratios across portfolios, can use the Roy’s Safety-First (SF) criterion: 13 14

16. Roy’s SF Criterion Two portfolios may provide the same level of utility  For example, if risk aversion coefficient = 2, then can show that utility is the same  However, if there is a threshold return of 5%, then A has a higher Roy’s criterion  Allocation A more likely to meet the threshold return of 5%  Ranking not necessarily the same as the Sharpe Ratio Investor’s Forecasts Asset allocationExpected ReturnStd Deviation A10%20% B7%10% 15

17. Myopic Loss Aversion Example: Currency hedging  Influenced by recent events or stick with long-term view? 15 16

18. Representativeness Making decisions based on recent history, or a small sample size Believe that it is representative of the future, or the full sample May lead to “excessive extrapolation”  Erroneously think that recent performance is representative of longer term prospects 16 17

19. Representativeness Results: Investors chase past winners  Overreacts to glamour stocks (e.g., technology bubble)  Overreacts to bad news which may be temporary (thus creating “value opportunities”)  Creates short-term momentum, but long-term reversal in returns  What quantitative managers look for 17 18

20. Regret Avoidance Leads to procrastination and inertia  Status quo bias  Good intentions but poor follow-through Consequences:  Delayed saving and investment choices  led to growth of target date funds  Limit divergence from peers’ average asset allocation, if sensitive to peer comparison  herding behaviour of asset managers  Herding behaviour will prolong the bubble (e.g., growth of technology mutual funds in the late 1990s) 18 19

21. Ambiguity Aversion Sticking with the familiar  Results in under-diversification  Investors may exhibit home bias, local bias  Bias is more substantial if take into account human capital  From a diversification point of view, DC plans should restrict company share ownership 19 20

22. Mental Accounting Tendency to divide total wealth into separate accounts and buckets  Ignores correlation between assets across portfolio  May result in tax-inefficient allocations Naïve diversification in DC pension plans (Benartzi and Thaler 2001)  1/n is found to be the predominant rule  Authors find that “the proportion invested in stocks depends strongly on the proportion of stock funds in the plan”  Plan sponsor’s menu of options and choices very important 20 21

23. Impact on Committee Decision Making Lack of diversity in membership could pose a problem  Common knowledge syndrome  Less willing to share unique or different information for the sake of social cohesion  It takes 16 similarly-minded committee members to generate the diversity of 4 different-minded members 21 22

24. Final Thoughts Some empirical findings are more respected in the profession than others Stock market returns affected by number of hours of sunshine in NYC…etc.  Point of disagreement More and more asset management companies are using the “behavioural finance” buzz word (mostly value strategies), as well as investment advisors 22 23

25. Final Thoughts Can the two schools of thought co-exist?  How I like to think about it:  Short-term: markets can be inefficient due to investor behaviour  Long term, markets are on average efficient 24

26. The Value Premium Risk-based explanation Relax the assumption in the conventional CAPM that beta and the market risk premium are constant HML has higher beta when market risk premium is high. Translation: value stocks do not do well in down markets, and hence are riskier to investors (Petkova and Zhang 2005) Value firms tend to have greater amounts of tangible assets, and hence less flexibility to adjust capacity during downturns (operating risk) 25