AVOIDING BEHAVIORAL TRAPS. San Diego County Employees Retirement Association. August SEATTLE LOS ANGELES

Transcription

1 AVOIDING BEHAVIORAL TRAPS San Diego County Employees Retirement Association August 2013 SEATTLE LOS ANGELES

2 UNDERSTANDING INVESTOR BEHAVIOR 1

3 SHAKESPEARE OR SIMPSON? What a piece of work is a man, how noble in reason, how infinite in faculties, in form and moving how express and admirable, in action how like an angel, in apprehension how like a god! William Shakespeare, Hamlet, Act II, Scene II

4 OR SIMPSON? 3

5 FOREST FOR THE TREES 4

6 THE POWER OF ILLUSION: BRAIN TWISTER YELLOW BLACK PURPLE ORANGE BLUE GREEN BLUE ORANGE RED GREEN YELLOW RED GREEN RED BLUE BLACK PURPLE ORANGE 5

7 THE POWER OF ILLUSION: WORDPLAY I LOVE PARIS IN THE THE SPRINGTIME 6

8 THE POWER OF ILLUSION: MORE WORDPLAY FINISHED FILES ARE THE RESULT OF YEARS OF SCIENTIFIC STUDY COMBINED WITH THE EXPERIENCE OF YEARS 7

9 TRADITIONAL VS. BEHAVIORAL FINANCE Traditional financial modeling assumes all investors make rational investment decisions based on well-established notions of risk and reward. Behavioral finance recognizes that investors base investment decisions on factors that may not be validated by traditional finance concepts Traditional Assumptions (MPT) Risk Aversion Rational Expectations Asset Integration Behavioral Assumptions Loss Aversion Investors prefer larger uncertain losses to smaller certain losses Biased Expectations Investors place too much confidence in their ability to forecast the future Asset Segregation Investors tend to focus on individual asset classes rather than the entire portfolio 8

10 WHY DOES THIS MATTER? Biases lead to bubbles Bubbles lead to drawdowns Drawdowns bring serious consequences Awareness allows avoidance 9

11 BUBBLES IN HISTORY Dutch Tulips: South Sea Company: 1720 California Gold Rush: American Railroads: Bicycles: Radios: 1920 s Another gold rush: Japan: Tech: Real Estate: Fed Bubble: ??? 10

12 BEHAVIORAL BIASES TO WATCH OUT FOR Representativeness Over-confidence Anchoring Familiarity Loss Aversion Regret Minimization 11

13 REPRESENTATIVENESS BIAS Definition Making judgments based on stereotypes (failure to appreciate mean reversion) Demonstration Sarah loves to listen to New Age music and faithfully reads her horoscope each day. In her spare time, she enjoys aromatherapy and attending a local spirituality group. Which series of coin flips is more likely to occur: Behavioral Trap HHTTHTHTTH HHHHHHHHHH Not appreciating randomness of independent outcomes Assuming tomorrow s market will be like today s 12

14 OVERCONFIDENCE BIAS Definition Believing one s ability to forecast the future is better than it is Demonstration How many rivets are there on the Golden Gate Bridge? How many man hole covers in NYC? 90% Confidence Interval Best Guess Low End High End Behavioral Trap Certainty about the future leads to greater risk-taking (collapse of LTCM) 13

15 ANCHORING BIAS Definition Inability to adjust views in response based on new information (i.e., stuck in the past) Demonstration Height of the tallest redwood Behavioral Trap Hanging on to what has worked in the past, even though the regime has shifted (Apple) Once bit; twice shy 14

16 FAMILIARITY BIAS Definition A propensity to make decisions based on familiarity or past experience Demonstration Temperatures around the world Behavioral Trap Lack of appropriate diversification Concentrated risk 15

17 LOSS AVERSION BIAS Definition The tendency for investors to prefer avoiding losses over acquiring gains Pleasure Loss Gain Demonstration Pain Investment A $100 Investment B $100 50% 50% 50% 50% Behavioral Traps $120 $150 $50 $250 Poor risk/reward decisions Ex-ante risk avoidance Propensity to hold a losing position too long (and even to double down) 16

18 REGRET MINIMIZATION BIAS Definition The tendency to make decisions based on the desire to avoid feeling regret in the future Demonstration Behavioral Trap Choice Blue Yellow Red A $50 $100 $0 B $0 $50 $100 Propensity to sell a winning investment too soon 17

19 THE IMPORTANCE OF PATIENCE 18

20 CASE STUDY Using data from Morningstar, Wurts & Associates conducted a study to assess to what extent good managers underperform along the way. The study observed 47, 96, and 32 Large Cap Blend, Growth, and Value mutual funds, respectively, who outperformed their benchmarks over the10-years ending March % 3.00% Excess Returns of Outperforming Managers (Ten Years Ending March 2008) Large Cap Blend 2.96% Large Cap Growth 2.50% Large Cap Value 2.40% Percentage 2.00% 1.50% 2.06% 1.48% 2.01% 1.34% 1.00% 0.50% 0.00% Average 10 Year Excess Return Median 10 Year Excess Return 19

21 SHORT- TERM UNDERPERFORMANCE SHOULD BE EXPECTED The funds were then examined to find what percentage of these funds underperformed their benchmarks on any annualized three year rolling period Despite long-term outperformance, over a three-year period a large percentage of the managers surveyed underperformed their benchmarks Over all three-year periods, the mean batting average for blend and growth managers was approximately 75%, while value managers underperformed more often Overall, over half of all managers underperformed their indices by at least 2% during a three-year stretch and about quarter underperformed by at least 5% Active Management over Rolling 3-Year Periods Large Cap Blend Large Cap Growth Large Cap Value Percentage of Funds Who Underperformed at least One 3-Year Period 90.4% 91.4% 100.0% Average Rolling 3-Year Batting Average 75.3% 73.6% 61.1% Highest Batting Average 100.0% 100.0% 94.1% Lowest Batting Average 31.8% 21.2% 27.1% Percentage of Funds which Trailed for Three Years by at least 2% Annualized 63.8% 64.6% 84.4% Percentage of Funds which Trailed for Three Years by at least 5% Annualized 25.5% 24.0% 28.1% 20

22 HIRED VS. FIRED MANAGERS The Cost of Market Timing Managers for Plan Sponsors Performance 3 years Prior to and After Manager Change Return (%) Fired Firms Hired Firms 0 Prior to After Number of Years Before Firing Number of Years After Firing -3 to 0-2 to 0-1 to 0 0 to 1 0 to 2 0 to 3 Fired Firms Hired Firms Return Differential (Hired - Fired) Source: Goyal, and Wahal. The Selection and Termination of Investment Management Firms by Plan Sponsors. The Journal of Finance. August, Table X, page