Principal Officers Conference Equity Risk: Mystery or Myth

Transcription

1 Principal Officers Conference Equity Risk: Mystery or Myth Patrice Rassou Head of Equities June 2011

2 Myth & Mysteries Mystery : Crossword Puzzle Simple Problem which needs specific information Ambiguity Myth: Plentiful information but no solution Uncertainty due to random factors (risk) I can calculate the motion of heavenly bodies but not the madness of people Sir Isaac Newton

3 Agenda Introduction The Mathematics of Risk The History of Risk: Risk and Uncertainty Equilibrium v Disequilibrium Theories Behavioural Finance: A Framework to understand markets Epidemiology Famous Financial Bubbles: Tulip craze, Japanese Bust, 1987 Crash The Tech Bubble Portfolio Simulation Case Study Conclusion

4 Introduction When men are brought together Many causes come into action..but there is one thing they cannot destroy, the habits they have of Panurge s sheep Henri Poincare

5 Basophobia BASOPHOBIA

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7 Notion of Risk Volatility = How Curved is the Road? But Equity Risk Premium difficult to forecast Tends to be treated as static Equity Risky Premium (ERP) How confident is the driver? Eg If driver confident = greed ERP drops = Equity Prices Rise If driver is a learner & slows = fear ERP rises = Equity Prices Drop

8 Mathematics of Risk Standard Deviation measures investment s volatility: Downside Deviation Impacts negatively on the compounding effect Sharpe Ratio measures risk taken to achieve a certain return Issues: a. No distinction between downside and upside volatility b. Volatility should be viewed also in context of investment strategy c. If its too good to be true, it probably is do your due diligence

9 Standard Deviation as measure of Risk Std Dev. A: 1,52% E A S Y Std Dev. B: 7,24%

10 Which is the most risky asset? Not So Obvious.

11 Traditional Finance No Free Lunch: Higher Return = Higher Risk

12 Two Stocks which differ by a Constant!

13 But with stocks look at downside volatility Timing Drawdown Concept

14 Effect of Time

15 Drawdown: Real Performance from Highs Source: Dimson & Marsh

16 Importance of Dividends Source: Dimson & Marsh

17 Agenda Introduction The Mathematics of Risk The History of Risk: Risk and Uncertainty Equilibrium v Disequilibrium Theories Behavioural Finance: A Framework to understand markets Epidemiology Famous Financial Bubbles: Tulip craze, Japanese Bust, 1987 Crash The Tech Bubble Portfolio Simulation Case Study Conclusion

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19 Early Study of Risk Daniel Ellsberg (1961) Harvard student: Jar A : 50 red / 50 black balls Jar B: 100 balls with ratio unknown Aim: Pick a red ball and win a prize Result: Avoid picking from Jar B Yet: Rational players should be indifferent towards either Jar People act differently also if prize changed to a penalty Behaviour cascades also occur: influence by behaviour of others Risk not the same as Ambiguity

20 Capital Allocation in Theory Source: Dimson, Marsh, Staunton Triumph of the Optimists

21 Efficient Frontier in Practice

22 South Africa : Since 1900 Nominal, Real Returns & SD

23 Risk v Uncertainty Frank Knight: Risk: Known probability of outcomes (have risk loving and risk averse individuals) Uncertainty: Ambiguous event (have optimists and pessimists) E.g. a Twig snaps, the optimistic caveman sleeps while the pessimistic one grabs his spear Knightian uncertainty in finance: Home Turf Bias Downside risk greatest when new opportunities emerge and information is poor. Potential for profit is real but ambiguous!

24 Agenda Introduction The Mathematics of Risk The History of Risk: Risk and Uncertainty Equilibrium v Disequilibrium Theories Behavioural Finance: A Framework to understand markets Epidemiology Famous Financial Bubbles: Tulip craze, Japanese Bust, 1987 Crash The Tech Bubble Portfolio Simulation Case Study Conclusion

25 Rational Economic Agents Adam Smith 1776: Individuals are led by an invisible hand which leads to optimal outcomes Selfish pursuits allocate resources efficiently George Rutledge Gibson (1889) Louis Bachelier Theory of Speculation (1900) Friedrich Hayek (1920) : Market as Omniscient Being Fama and French: EMH

26 What if Markets were Efficient Strong Form EMH: Prices reflect all public and private information No ways to generate outperformance Bubbles do not exist Semi-Strong form EMH: Only public information incorporated Weal form EMH: Prices move in random ways Insider Information useful Analysts need information which is non public Fundamental Analysts is useful Even public information not properly interpreted

27 Keynesian Theory of Investment Liquidity Preference Animal Spirits Entrepreneurs Animal Spirits Savers Invest Hoard Cash Share prices If Demand < Supply no investment unemployment Govt should cut rates/have public works programme Keynesian economics broken by 1970s stagflation Behavioural Economics important!

28 Risk & Efficient Markets 1. Efficient Market still involves Risk Even if Price = Pv (Div0 t) Prices will adjust to reflect new info immediately; changes in discount rates; changes in growth rate 2. Stocks follow a random walk around an upward drifting trend Higher expected return for higher risk Problem: Is higher return = higher risk or the result of a bubble?

29 Against Rational Markets The Stock Market as a Beauty Contest (Keynes 1936) Cass & Shell Model based on Sunspots if enough investors believe sunspots cause prices to go up, then craze will drive prices up until self fulfilling loops close Blanchard & Watson (MIT 1983): Rational Bubble if you can show that a new generation of suckers is born everyday Greater Fool Theory of Stock Market Boom-Bust would be consistent with Rational Participants Excess volatility observed in markets (Shiller) but Marsh & Merton have shown that this could be compatible with rising dividends

30 Equity Premium Puzzle Discovered the Equity offered a rich premium of 4-8% over safe assets Source of problem: Assume returns are normally distributed Reality is that equity returns are more volatile than Gaussian distribution. Hence need higher risk compensation Households across the world have different appetite for risk and therefore have different strategic asset allocation preferences

31 Equilibrium Theory 1997 Nobel Prize Winners in Economics: Robert Merton and Myron Scholes Believe that rational, efficient pricing of assets Also helped manage LTCM: Aug/Sep 1998: Losses $500m during Asian financial crisis Likelihood of such events per models: 1 in 50 million!

32 Disequilibrium Theory Karl Popper: Problem of Induction The fundamental problem in science is that it cannot prove anything One piece of evidence can falsify a supposed fact Irving Fisher : 1929 stock prices have reached what looks like a permanently high plateau George Soros: Theory of Reflexivity The participants views and state of affairs enter into a process of dynamic disequilibrium At times, higher prices generate more demand not more supply in asset markets (and vice versa) Andrew Lo Nicholas Taleb: Black Swans

33 Disequilibrium in Our Era Debt and Leverage as a magnifier Hyman Minsky: Financial Instability Hypothesis After long periods of economic stability, destabilising forces in the economy develop.using leverage to become more dangerous. Procyclical tendency of credit to grow during good times (Minsky Migration) Minsky Moment: Speculative and Ponzi schemes implode and entire economy wobbles

34 Agenda Introduction The Mathematics of Risk The History of Risk: Risk and Uncertainty Equilibrium v Disequilibrium Theories Behavioural Finance: A Framework to understand markets Epidemiology Famous Financial Bubbles: Tulip craze, Japanese Bust, 1987 Crash The Tech Bubble Portfolio Simulation Case Study Conclusion

35 Behavioural Flaws: Imitation

36 Gambler s fallacy Mr and Mrs Thomas have three children, all girls They are now expecting a fourth child. What are the odds in favour of having another girl: A: 6.25% (1 in 16) odds of having 4 girls in a row B: 50% (1 in 2) C: somewhere in between

37 Framing Kahneman & Tversky on prospect theory. Example Outbreak of unusual disease expected to kill 600 people. Choice of 2 remedies A: 200 people will be saved B: 1/3 probability that all 600 people will be saved and 2/3 noone will be saved Which do you choose?

38 Or Programme 1 : 400 people will die Programme 2 : 1/3 probability nobody will die and 2/3 probability that 600 people will die

39 Risk Aversion Risk Aversion means that choices not always rational

40 Epidemiology Study of Infection Rate Tipping Point (Malcom Gladwel) Word of mouth key for propagation of speculative bubbles Swarms: How group logic is different from individual logic

41 Trading Psychology

42 Conventional Wisdom Mkt is Efficient Buy & Hold Speculative Bubble Mkt Overvalued Prices Soar

43 Agenda Introduction The Mathematics of Risk The History of Risk: Risk and Uncertainty Equilibrium v Disequilibrium Theories Behavioural Finance: A Framework to understand markets Epidemiology Famous Financial Bubbles: Tulip craze, Japanese Bust, 1987 Crash The Tech Bubble Portfolio Simulation Case Study Conclusion

44 Not a New Concept

45 Bubbles Are Common

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47 Tulip Price Index

48 Tulip Bubble: 1630 Netherlands Seed based reproduction takes 7-12 years to produce a bulb Contracts to trade bulb throughout the year(futures on margin) Price of rare bulbs went up 10x in weeks Money supply in Netherlands shot up Economic optimism after end of Spanish war and uncertainty of life due to rampant bubonic plague

49 Japan: Drivers of Boom BoJ lowered rates to 2,5% in 1986 Deregulation of Banking Sector = increased loans guaranteed by property Conformist Thinking 150% CGT on land sales within 2 yrs of purchase led to artificial scarcity

50 Japanese Land Prices Source: Merrill Lynch

51 Topix Index TOPIX Index Source: Inet

52 Japanese Bust Shintoism & Confucian ideas: consensus seeking Belief that Government should guide enterprise High growth led to booming property market: scarcity, feudal tradition and government policies.

53 Japanese Market Valuation Source: Merrill Lynch

54 Behaviour Men think in herds, go mad in herds, but only recover their senses one by one Charles Mackay Extraordinary Popular Delusions and Madness of Crowds

55 Sign of Excesses: Lessons from Tulips and Japan! Change in Credit Criteria / Financial Innovation Growth in Credit and Collateral Value / Cheap Money Policy Driven Distortions Hot Money Inflows / Moral Hazard Overconfidence / New Era Thinking

56 Sotherby s Stock Price as Bubble Indicator 1988 Japan, 1999 Dotcom, 2007 Hedge funds/gem,2010 China Nasdaq China Hedge funds/ GEM Japan Source: Bloomberg

57 Pricking of a Bubble: Black October Source: Robert Shiller

58 Probability Theory: Standard Deviations 1987 Crash = 22 SD Event!

59 1987 Crash The efficient market hypothesis is the most remarkable error in the history of economic theory. This is just another nail in its coffin Robert Shiller (after the 500 pt drop, -23%, in Dow on Oct ) Probability of such a drop 1 in NYSE was 170 yrs old so very unlucky to see a 1 in 2bn yr event! Some causes: Portfolio Insurance Futures prices dropping further than underlying, causing arbitrageurs to drop physical stock Soln: Fed pumped liquidity to avoid repeat of 1920s

60 Agenda Introduction The Mathematics of Risk The History of Risk: Risk and Uncertainty Equilibrium v Disequilibrium Theories Behavioural Finance: A Framework to understand markets Epidemiology Famous Financial Bubbles: Tulip craze, Japanese Bust, 1987 Crash The Tech Bubble Portfolio Simulation Case Study Conclusion

61 Nasdaq: Surely History Cannot Repeat Itself? NASDAQ 100 Composite Index Source: Inet

62 IT Bubble: Some Similarities Source: Robert Shiller

63 Bubbles: History does repeat itself! Source: Robert Shiller

64 Agenda Introduction The Mathematics of Risk The History of Risk: Risk and Uncertainty Equilibrium v Disequilibrium Theories Behavioural Finance: A Framework to understand markets Epidemiology Famous Financial Bubbles: Tulip craze, Japanese Bust, 1987 Crash The Tech Bubble Portfolio Simulation Case Study Conclusion

65 Portfolio Simulation: What would you have done?

66 Risk Limits..would have allowed you to Invest BIG!

67 And yet..some common sense is best safety net

68 Valuation based investing sidesteps mistakes

69 But remember...human nature never changes!

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71 The Problem with Equity Risk Models Risk is not the same as Volatility Many models equate historically volatile shares to risky shares This led to binomial models being used to model stock prices We think risk = permanent loss of capital Eg: New venture difficult to evaluate New Technology

72 Mitigating Risk Identifying Alpha Source is Key Understanding Risk relative to benchmark is key both at a portfolio and stock specific level Scenario analysis is to model more extreme downside scenarios Focus on risk of capital loss not volatility Rely on facts rather than forecasts Avoid investing in bubbles and following the crowd

73 Conclusion Is the market rational? No Can you outsmart it easily? No Because most human beings are not rational either! In an efficient market, the actions of the many competing participants should cause the actual price of a security to wander randomly about its intrinsic value - Eugene Fama (1965) Offsetting actions by informed investors do not typically suffice to cause the price effects of erroneous beliefs to disappear with the passage of time - Eugene Fama & Kenneth French (1997)

74 THANK YOU FOR ATTENDING Patrice Rassou Sanlam Investment Management