INVESTOR BEHAVIOUR AND BENCHMARKS Presentation to Finansmarkedsfondet Executive Board Sari Carp Norwegian School of Management (BI) 8 December 2005

PART I: Background on Behavioural Finance

Isaac Newton (losing investor in the South Sea Bubble): “I can calculate the motions of the heavenly bodies, but not the madness of people.”

Three Viewpoints on Market Efficiency (borrowed from Richard Thaler) 1)Efficient Market Zealot (vanishing breed)  Security prices are always equal to intrinsic value.  Price movements are random, and hence unpredictable. 2)Behavioural Finance Zealot (figment of EMZs’ imaginations)  Prices depend only on market psychology.  It is easy to predict price movements. 3)Sensible Middle Ground (nearly everyone)  Prices are highly correlated with intrinsic value, but sometimes diverge significantly.  It is sometimes possible to predict prices, but generally not with great precision.

The Development of Behavioural Finance Research  Documentation of anomalies  Theory building: Economics + Psychology  Testing of theories  Experimental  Empirical

“Bounded” Rationality  Decision Making Rules = Heuristics  Biases  Market Imperfections = Anomalies

PART II: Investor Behaviour and Benchmarks

Psychology of Benchmarking  Fundamental human desire to evaluate one’s own abilities  Cannot assess abilities directly, so evaluate the result of abilities; i.e., performance  Even if performance can be measured unambiguously (e.g. time to run a race), how do we know if it’s good?  compare with others’ times  If an objective, non-social criterion exists, then evaluation relative to others is not used  Focus on a reference point, or goal, decreases as the difference between it and one’s own ability increases

Antecedents  Prospect Theory (Kahneman and Tversky, 1979)  March and Shapira, 1992  two reference points: aspiration and survival  decision makers tolerate high risk when resources are below reference point, lower risk when resources are above reference point  decision makers focus only on one of two reference points at a time  ascribes value to gains and losses, not total wealth  decision makers are risk averse in gains, risk seeking in losses  pain of loss is sharper than pleasure of gain

Model of Investor Risk Behaviour  Performance (return) evaluated relative to two benchmarks:  Success (S)  Exit (X)  In each period, investors choose portfolio risk according to:  benchmark(s) focused on  distance from focal benchmark(s)

time 0 portfolios homog. in value, variance; heterog. in assets time t evaluation period ends; investors performing below Exit are eliminated from the market; all others may invest again time 1 portfolio values change according to random walk; investors choose risk strategies based on own returns relative to Success and Exit levels time 2 portfolio values change again; Success and Exit benchmark returns also change; investors restrategize based on new values Timeline

Risk Taken Relative to Performance Benchmarks Success Focus Exit Focus Mixed Focus Risk Performance (Cumulative Return) X S

Risk Taken Relative to Performance Benchmarks Risk Performance (Cumulative Return) X S

Mutual Fund Manager Data  actively managed (no index funds)  single country focus Offshore  787 funds (Datastream)  25 countries  all equity  U.S.  7,606 funds (CRSP)  equity and bond 

Results from Fund Manager Data  Results support model; highly statistically significant  Model holds across economic, political and legal contexts  But…how can we know if benchmarking is driven by behavioural or compensation based factors??  By comparing individual and institutional investors, we can “factor out” the compensation based element

VPS Data  Unique in the world  “Gold mine” for behavioural research!!!  Some behavioural research has been done on similar databases from other Scandinavian countries…but, these databases are incomplete  Very famous behavioural research has been done on an individual investor database from a U.S. brokerage firm…but, this database is both incomplete and biased

VPS Data  Complete database of ownership in Norwegian stock market  10 years of monthly portfolios for each investor  Investors categorized as individual, financial institution, non-financial corporation, government or foreign investor  Tracks each investor by ID over time, allowing comparisons of investment decisions under same conditions  Eliminates sample bias

Predicted Results on VPS Data  Both professionals and individuals will take increased risk as their performance improves above the Success or Exit points; this result will be more pronounced for professionals  Both groups will take increased risk as performance deteriorated below Success or Exit points; this result will also be more pronounced for professionals  So, the pattern will be the same, but more extreme for professionals