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Views of Risk

Views of Risk. Traditional Economic View. Th ű nen [1826] Profit is in part payment for assuming risk Hawley [1907] Risk-taking essential for an entrepreneur Knight [1921] Uncertainty non-quantitative Risk: measurable uncertainty (subjective)

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Views of Risk

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  1. Views of Risk

  2. Traditional Economic View • Thűnen [1826] • Profit is in part payment for assuming risk • Hawley [1907] • Risk-taking essential for an entrepreneur • Knight [1921] • Uncertainty non-quantitative • Risk: measurable uncertainty (subjective) • Profit is due to assuming risk (objective)

  3. Contemporary Economics • Harry Markowitz [1952] • RISK IS VARIANCE • Efficient frontier – tradeoff of risk, return • Correlations – diversify • William Sharpe [1970] • Capital asset pricing model • Evaluate investments in terms of risk & return relative to the market as a whole • The riskier a stock, the greater profit potential • Thus RISK IS OPPORTUNITY • Eugene Fama[1965] • Efficient market theory • market price incorporates perfect information • Random walks in price around equilibrium value

  4. Empirical • BUBBLES • Dutch tulip mania – early 17th Century • South Sea Company – 1711-1720 • Mississippi Company – 1719-1720 • Isaac Newton got burned: “I can calculate the motion of heavenly bodies but not the madness of people.”

  5. Modern Bubbles • London Market Exchange (LMX) spiral • 1983 excess-of-loss reinsurance popular • Syndicates ended up paying themselves to insure themselves against ruin • Viewed risks as independent • WEREN’T: hedging cycle among same pool of insurers • Hurricane Alicia in 1983 stretched the system

  6. Black Monday • October 19, 1987 • Stock Exchange – triple witching hour • Some blamed portfolio insurance • Based on efficient-market theory, computer trading models sought temporary diversions from fundamental value

  7. Long Term Capital Management • Black-Scholes – model pricing derivatives • LTCM formed to take advantage • Heavy cost to participate • Did fabulously well • 1998 invested in Russian banks • Russian banks collapsed • LTCM bailed out by US Fed • LTCM too big to allow to collapse

  8. Information Technology • 1990s very hot profession • Venture capital threw money at Internet ideas • Stock prices skyrocketed • IPOs made many very rich nerds • Most failed • 2002 bubble burst • IT industry still in trouble • ERP, outsourcing

  9. Real Estate • Considered safest investment around • 1981 deregulation • In some places (California) consistent high rates of price inflation • Banks eager to invest in mortgages – created tranches of mortgage portfolios • 2008 – interest rates fell • Soon many risky mortgages cost more than houses worth • SUBPRIME MORTGAGE COLLAPSE • Risk avoidance system so interconnected that most banks at risk

  10. APPROACHES TO THE PROBLEM • MAKE THE MODELS BETTER • The economic theoretical way • But human systems too complex to completely capture • Black-Scholes a good example • PRACTICAL ALTERNATIVES • Buffett • Soros

  11. Better ModelsCooper [2008] • Efficient market hypothesis • Inaccurate description of real markets • disregards bubbles • FAT TAILS • Hyman Minsky [2008] • Financial instability hypothesis • Markets can generate waves of credit expansion, asset inflation, reverse • Positive feedback leads to wild swings • Need central banking control • Mandelbrot & Hudson [2004] • Fractal models • Better description of real market swings

  12. Fat Tails • Investors tend to assume normal distribution • Real investment data bell shaped • Normal distribution well-developed, widely understood • TALEB [2007] • BLACK SWANS • Humans tend to assume if they haven’t seen it, it’s impossible • BUT REAL INVESTMENT DATA OFF AT EXTREMES • Rare events have higher probability of occurring than normal distribution would imply • Power-Log distribution • Student-t • Logistic • Normal

  13. Correlated Investments • EMT assumes independence across investments • DIVERSIFY – invest in countercyclical products • LMX spiral blamed on assuming independence of risk probabilities • LTCM blamed on misunderstanding of investment independence

  14. Human Cognitive Psychology • Kahneman & Tversky [many – c. 1980] • Human decision making fraught with biases • Often lead to irrational choices • FRAMING – biased by recent observations • Risk-averse if winning • Risk-seeking if losing • RARE EVENTS – we overestimate probability of rare events • We fear the next asteroid • Airline security processing

  15. Animal Spirits • Akerlof & Shiller [2009] • Standard economic theory makes too many assumptions • Decision makers consider all available options • Evaluate outcomes of each option • Advantages, probabilities • Optimize expected results • Akerlof & Shiller propose • Consideration of objectives in addition to profit • Altruism - fairness

  16. Warren Buffett • Conservative investment view • There is an underlying worth (value) to each firm • Stock market prices vary from that worth • BUY UNDERPRICED FIRMS • HOLD • At least until your confidence is shaken • ONLY INVEST IN THINGS YOU UNDERSTAND • NOT INCOMPATIBLE WITH EMT

  17. George Soros • Humans fallable • Bubbles examples reflexivity • Human decisions affect data they analyze for future decisions • Human nature to join the band-wagon • Causes bubble • Some shock brings down prices • JUMP ON INITIAL BUBBLE-FORMING INVESTMENT OPPORTUNITIES • Help the bubble along • WHEN NEAR BURSTING, BAIL OUT

  18. Nassim Taleb • Black Swans • Human fallability in cognitive understanding • Investors considered successful in bubble-forming period are headed for disaster • BLOW-Ups • There is no profit in joining the band-wagon • Seek investments where everyone else is wrong • Seek High-payoff on these long shots • Lottery-investment approach • Except the odds in your favor

  19. Taleb Statistical View • Mathematics • Fair coin flips have a 50/50 probability of heads or tails • If you observe 99 heads in succession, probability of heads on next toss = 0.5 • CASINO VIEW • If you observe 99 heads in succession, probably the flipper is crooked • MAKE SURE STATISTICS ARE APPROPRIATE TO DECISION

  20. CASINO RISK • Have game outcomes down to a science • ACTUAL DISASTERS • A tiger bit Siegfried or Roy – loss about $100 million • A contractor suffered in constructing a hotel annex, sued, lost – tried to dynamite casino • Casinos required to file with Internal Revenue Service – an employee failed to do that for years – Casino had to pay huge fine (risked license) • Casino owner’s daughter kidnapped – he violated gambling laws to use casino money to raise ransom

  21. DEALING WITH RISK • Management responsible for ALL risks facing an organization • CANNOT POSSIBLY EXPECT TO ANTICIPATE ALL • AVOID SEEKING OPTIMAL PROFIT THROUGH ARBITRAGE • FOCUS ON CONTINGENCY PLANNING • CONSIDER MULTIPLE CRITERIA • MISTRUST MODELS

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