“Shift Happens”

1. “Shift Happens” On a New Paradigm of the Markets as a Complex Adaptive System by Michael J. Mauboussin Lecture Notes for Finance 450 CSULB Dr. Ammermann

2. References: • “Shift Happens” – www.capatcolumbia.com • The Warren Buffett Portfolio, by Robert Hagstrom • Ch. 8, “The Market as a Complex Adaptive System” • Chaos and Order in the Capital Markets, by Edgar Peters

3. 3 Forms of Market Efficiency • Weak form – technical analysis • Supported by studies on autocorrelation • Contradicted by seasonality, subsequent performance of “winners” and “losers” • Semi-strong form – fundamental analysis • Supported by typical (under)performance of fund managers, also by many event studies • Contradicted by P/E and M/B effects, consistent outperformance of some fund managers (e.g., the “Superinvestors of Graham-and-Doddsville”)

4. 3 Forms of Market Efficiency • Strong form – insiders • Contradicted by subsequent performance of stocks after insider share repurchases

5. Areas of Potential Exploitation • Two areas of potential exploitation under traditional theory • Better information • Day traders, arbitrageurs? • Better analysis • Warren Buffett?

6. Areas of Potential Exploitation • Tension exists between academics and practitioners • Academics – rational agents, random walk, efficient markets • Practitioners – outperformance of some fund managers, irrational investors, inefficiency • One model reconciles both, bringing theory together with practice: The Market as a Complex Adaptive System (CAS)

7. Paradigm Shifts • Thomas Kuhn • Steps in process: • Theory laid out • Scientists test theory, but some facts counter it • Original theory stretched (to encompass new facts) • New theory developed to supercede the old

8. Paradigm Shifts • Example: • Aristotle proposes geocentric universe with orbits as circles • Astronomers observe the orbits are elliptical, not circular • Ptolemy introduces “circles-upon-circles” • Copernicus, Kepler, and Galileo introduce heliocentric universe, elliptical orbits, and celestial imperfection

9. 2 Tests for a New Paradigm • Jeremy Bernstein’s test of “Correspondence” • 2 tests: • New idea must explain why the old theory worked • Must also add some predictive (or at least explanatory) power

10. Classical Capital Market Theory • Economics still largely based on equilibrium systems: supply vs. demand, price vs. quantity, risk vs. reward • Stems from the view of economics as a science akin to Newtonian physics – direct cause and effect, with implied predictability • Many statistical tools can only be applied if equilibrium theory holds

11. Classical Theory • Stock market efficiency – prices reflect all relevant information when that info. is cheap & widely disseminated • Purchasing stocks as a zero-NPV proposition • Prices not always “correct,” but not systematically wrong • Random walk – security price changes are independent of each other • Lots of agents – current prices reflect all info. that is collectively known • Normal distribution of stock returns typically assumed in conjunction with this • Rational agents – investors can assess and optimize risk / reward opportunities • Assumptions / prediction – modest trading activity, limited price fluctuations directly attributable to specific news “events”

12. Classical Theory Tested • Stock returns are not normal • “fat tails,” or “Noah Effect” • Similar to “punctuated equilibrium” in biology • Random walk not supported by data • Elements of persistence – “Joseph Effect” • Campbell, Lo, & MacKinley show prices are predictable • Volume higher & price changes greater than predicted • Risk & reward not linked via variance • Fama – firm size and M/B more important, so interpreted as risk factors • Investors are not rational • Systematic judgment errors have been identified • Humans operate inductively, not deductively

13. Theory Stretched • Non-normal distributions • Typically ignored, or “outliers” simply removed from data • Mandelbrot and stable Paretian distributions for financial data • Noise traders (Black, 1986) • “Noise” fills the theory / practice gap • Should not trade • “Noise theories were all derived originally as part of a broad effort to apply the logic of the [CAPM] to … behavior that does not fit conventional notions of optimization.” • Crashes • Fama – “I think the crash in ’87 was a mistake” • Miller – recommends reading Mandelbrot • Behavioral finance • “as if” argument – if many investors whose errors are independent with no systematic biases, aggregate market should appear rational • Dilutes theory

14. New Theory – Market as Complex Adaptive System • Three components of complex adaptive systems: • Decision rules • Lots of agents, each operating with their own decision rules, with the most effective surviving • Provides the “adaptive” part of CAS • Emergence • Emergence = complex, large-scale behaviors resulting from aggregate interaction of less complex agents • e.g., ant colony – individual ants have simple tasks, but combine together to create a very complex colony • e.g., Adam Smith’s “invisible hand” • Market as a “Meta-System”

15. Market as a “Meta-System” • “Meta-System” = market has properties and characteristics distinct from the agents / investors who comprise it • Two key characteristics: • Nonlinearity • Output of the system will not necessarily be proportional to the input • Critical points • Periodically, small-scale stimuli lead to large-scale effects • “the straw that broke the camel’s back” • Sand piles • Leads to booms and crashes in the market

16. Does CAS Conform to Reality? • Adaptive behavior • leads to high trading volumes • Nonlinearity • contributes to fat-tailed return distributions • Trend persistence, but with low levels of autocorrelation, found in many complex adaptive systems • Homogeneous vs. heterogeneous expectations • Heterogeneous expectations may lead to risk / reward inefficiency • Deductive vs. inductive decision making • “El Farol” problem - Independent errors O.K., non-independent errors can lead to self-reinforcing trends

17. Does CAS Conform to Reality? • Portfolio manager performance • Not much predictability with CAS’s • Nonetheless, some investors may be “hard-wired” to be good investors • E.g., Warren Buffett, George Soros • Artificial stock market • Santa Fe Institute • Replicate market activity • Generates realistic market behavior

18. Practical Investor Considerations • Risk & reward link may not be clear • CAPM a possible first approximation, but far from the whole story! • Cause-and-effect thinking is dangerous • Human nature to identify cause and effect • But, with nonlinearity, large-scale changes can come from small-scale inputs (cf. Haugen, “Beast on Wall Street” • Crash of ’87 • Traditional DCF analysis remains valuable • Sets out first principles for stock valuation • Good framework for sorting out key issues in valuation • Helps investors crystallize / quantify expectations • Strategy / micro-economics

19. Why does EMH still win out? • Still hard to beat the market consistently • Implies EMH is a good first approximation • Strategy for Buffett’s “know-nothing” investors • Market efficiency is effectively accurate • Haugen’s counter-argument • Market so IN-efficient that investors cannot count on rational values to resurface • Justification for Buffett’s focus on “margin of protection” over and above “margin of safety” • Near the “wrong 20-yard line” • Cf., Ch. 15, “The Inefficient Stock Market”