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Views of Risk. Traditional Economic View Thűnen [1826] – Profit is in part payment for assuming risk Hawley [1907] – Risk-taking essential for an entrepreneur.

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Presentation on theme: "Views of Risk. Traditional Economic View Thűnen [1826] – Profit is in part payment for assuming risk Hawley [1907] – Risk-taking essential for an entrepreneur."— Presentation transcript:

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 17 th Century – South Sea Company – – Mississippi Company – 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 Models Cooper [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 1.A tiger bit Siegfried or Roy – loss about $100 million 2.A contractor suffered in constructing a hotel annex, sued, lost – tried to dynamite casino 3.Casinos required to file with Internal Revenue Service – an employee failed to do that for years – Casino had to pay huge fine (risked license) 4.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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