my life in a hedge fund - université de fribourg - universität
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My Life in a Hedge Fund
Marc Potters
Capital Fund Management
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What is a Hedge Fund?
• Traditional Asset ManagementLong Only – Equity (Stocks)– Bonds
• Alternative Investments– Real Estate– Private Equity– Hedge Funds
• Hedge FundsAt least one of the
following:– Leverage– Short Positions– Derivatives– Profit from Trading
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Hedge Fund Industry
• Number of Hedge Funds– Total : thousands– Assets > 1 billion$: ~100– Assets > 10 billion$: ~20
• Total Amount in Hedge Funds– 1 – 1.5 trillion$ (1012)
• Worldwide Stock Market Capitalization– ~40 trillion$
• Short Lifetime– Typical lifetime ~ 3y– Survival bias
• Known Quantitative Funds– Simmons’ Medallion– Prediction Company
• Farmer and Packard
– D.E. Shaw– Citadel
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Types of Hedge Funds
• Long/Short Equity– Stock Picking + Short
• Event Driven– Merger– Distress– Shareholder Activism
• Convertible Bonds• Bond/Yield Arbitrage
e.g. LTCM
• Global Macro
• Others– Complex Derivatives
• Systematic Quantitative– Futures Trading (CTA)– Equity Statistical
Arbitrage– Volatility Arbitragee.g. CFM
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Short Selling
• Buy Shares: Long Position– Make money if price goes up
• Short Selling: Short Position– Borrow shares from someone else– Sell them on market– Need to buy them back at some future time– Make money if price goes down
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Futures Market
• Market for Finance Professionals– Contract size = 50-100k $
• Many Asset Classes– Stock Indices– Government Bonds– Short Term Interest Rates– Currencies– Commodities
• Oil Products• Grains + Livestock• Metals
• Symmetric Long/Short• Small Deposit
– 3-5 % of value
• Marked-to-Market Everyday
• Anonymous• No Counterparty Risk
– Exchange between every transaction
• Electronic Almost Everywhere
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Call Option
• Right to buy an asset at a fixed price X (Strike) at a later time T (Maturity) for a payment upfront C (Premium).
• Premium depends on fluctuations (Volatility)• Similar to insurance policy• Black-Scholes Theory
–Option premium and hedge under continuous-time multiplicative Browninan motion–Unrealistic assumptions–Used as an effective theory to translate market prices in volatility
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Statistical Trading
• Casino “House”– Odd/Even 19/37=51.3%– Many tables, many
players, many days
• Hedge Fund– Diversify in strategies– Diversify in “space”
• Trade many uncorrelated assets (residuals)
• Avoid global factors– Market mode– Global volatility
– Diversify in time• Short term trading
• Law of Large Numbers– Average ~ N– Fluctuations ~ √N
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We’ve got computers…
• Short Term Trading Opportunities– ~100 ms
• Monitor Large Number of Assets Simultaneously– 2000 Stocks– 30000 Options
• Implement Sophisticated Pricing Models
• Patient– Can wait hours to save
a few dollars
• Record Everything They Do– Audit– Impact Analysis
• Anonymous• No Emotion• No Stress
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Ingredients for a Successful Hedge Fund
• Investment Strategy– Profit generating
strategies• Predict future prices
better than chance
– Trade selection• Risk control• Transaction cost
analysis
– Trade ExecutionConstant Innovation
• Other Departments– IT infrastructure– Marketing– Back-Office– Compliance
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Predictable Random Walk
• How can a random walk be predictable?• Bias (not equal to risk free rate)
– Carry trade: buy currency that pays large interest rate
• Positive Auto-correlation– Trend following
• Negative Auto-correlation– Mean reverting
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Trend Following
• Strategy– Always Long or Short– Go long if R(t)>R0, go
short if R(t)<-R0, otherwise stay
• Make money on average if random walk is persistent
• Trade statistics is skewed– Winning trades are longer
than loosing trades– Loosing trades are more
frequent• Daily return statistics is
unskewed– gauge transformation
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Risk Control
• Fat tails– Non-Gaussian statistics
• Leverage Effect– Price drops induce
volatility
• Volatility fluctuations– Long range
autocorrelations– Non-trivial kurtosis– Dynamical leverage
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Distribution of Stock Returns
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Risk Control
• Empirical Correlation Matrices– N assets, T time intervals– Q=N/T ~ O(1)– Marcenko-Pastur distribution of eigenvalues– Small eigenvalues are dangerous for Markowitz
• w ~ C-1 r
– Large eigenvalues may be signal• Cut-Off cleaning• M-P dressing of ansatz distribution
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Empirical Eigenvalue Distribution
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Applications of Correlation Matrices
• Generalized Pair Trading– Pair trading: compare a stock with a peer– Create a “super-peer” using all correlated stocks
• Uncorrelated Residuals– To get √N diversification need uncorrelated bets
– Invest uncorrelated portfolios with wa=1
• Measure Your Risk– Need reliable matrix “out-of-sample” if you
optimize
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Transaction Costs
• Broker – Clearer – Exchange fees– Order of 0.1 to 1 bp (10-5 - 10-4)
• Bid-Ask Spread– Order of 1 to 10 bp (10-4 - 10-3)– Can do better with smart execution
• Price Impact– Strongly depends on size– Dominant cost for large funds
• Financing cost– 0.5 to 1% per year per unit of Leverage
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Trade Execution
• Order Book Dynamics• Short Term Predictive
Models• Price Impact• Market Reaction• Experimental Physics
vs Cosmology
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Quant Crunch 1998 and 2007
• Aug. 1998• LTCM Strategies
– Emerging Market Yield– European Convergence– Long Term Index Vol– On-the-run Off-the-run
• Trigger: Russian Dept Crisis
• Aug. 2007• Quant Strategies
– High Yield Bonds (credit risk)
– Equity Quantitative– Equity Stat Arb
• Trigger: Sub-Prime Loan Crisis
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Investment Advices
• Beware of fees!• Don’t buy complicated
products• Diversify
– Large portfolio or indices NOT a few stocks
• Include dividends in your performance analysis
• Investment managers don’t beat the index
• Buy Index Funds or ETFs
• Reasonable long term targets– RFR + 3-5% is very good
• Consider impact of taxes– Retirement accounts,
life insurance etc.
• Don’t invest in Hedge Funds– Unless you know that
• They are smart• They are not crooks
• Beware of fees!