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Views of Risk

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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)

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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

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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.”

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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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