MUSINGS OF A MATHEMATICIAN ABOUT THE HEDGE FUND

MUSINGS OF A MATHEMATICIAN ABOUT THE HEDGE FUND SPACE California State University Dominguez Hills Mathematics Colloquium By: Ranjan Bhaduri Ph. D CFA CAIA M. Math MBA BSc. (Honours) Chief Research Officer at Sigma Analysis and Management OCTOBER 22 ND, 2014.

Abstract The hedge fund space has grown into a multi-trillion dollar business, and there are several quantitative and systematic hedge funds in existence. In addition, certain mathematical techniques are invoked in the hedge fund industry. This talk gives some insights about the mathematics utilized in the hedge fund world. In addition, it gives some nuggets of wisdom to students (both undergraduate and graduate) looking to have success in the business and finance world. Sigma Analysis & Management Ltd. www. sigmanalysis. com 1

CONTENTS • 1. Introduction – Jim Simons, Renaissance Technologies – Quantitative Trading Strategies / Mathematical Techniques – Some misconceptions about hedge funds • 2. The Mathematics of Liquidity • 3. The Omega Function • 4. Assorted Remarks about Mathematical Techniques in Hedge Funds • 5. Some Nuggets of Wisdom – Success in industry / business world • Appendix – Who is Sigma Analysis & Management? – Bio of Ranjan Bhaduri – Contact details I would have written a shorter letter, but I did not have the time. - Blaise Pascal Sigma Analysis & Management Ltd. www. sigmanalysis. com 2

1. INTRODUCTION Sigma Analysis & Management Ltd. www. sigmanalysis. com 3

Jim Simons – Renaissance Technologies • Jim Simons, Ph. D – Mathematician – World-class Mathematician (Ph. D at 23 from Berkeley, BSc from MIT) – Chairman & Professor of Mathematics at SUNY – A cryptanalyst at the Institute of Defense Analyses in Princeton – He received the American Mathematical Society Veblen Prize in Geometry in 1975 – Chern-Simons Invariants applications in Theoretical Physics – http: //www. nytimes. com/2014/07/08/science/a-billionairemathematicians-life-of-ferocious-curiosity. html? _r=0 – Founder, CEO of Renaissance Technologies (investment firm), Medallian hedge fund (1982). Sigma Analysis & Management Ltd. www. sigmanalysis. com 4

MATHEMATICAL TECHNIQUES / TRADING STRATEGIES - Systematic Trading Strategies - Statistical Arbitrage - Short-term CTAs - Trend-Following CTAs - Quant Equity - Market-Neutral Equity - Most good hedge funds invoke some ideas from mathematical reasoning, either in portfolio construction, risk management or trading methodology. Sigma Analysis & Management Ltd. www. sigmanalysis. com 5

Some Mathematical techniques used in the hedge fund world … - Some Mathematical techniques - Differential Equations - Machine Learning - Kelly Criterion - Gambler’s Ruin - Probability Theory - Game Theory - Matrix Theory / Linear Algebra - Cryptography / Cyber-security - Statistical Pattern Recognition - Bioinformatics - Bayesian Statistics - Fourier Analysis - Numerical Methods - Monte Carlo Analysis - Regression - Statistical Distributions - Chaos Theory - Randomness - Fractals - Signal Processing - Statistical Analysis (Big Data) Sigma Analysis & Management Ltd. www. sigmanalysis. com 6

Some Basic Facts about Hedge Funds • Many Sovereign Wealth Funds, Pensions, Endowments, and Foundations invest in Hedge Funds – Corollary: Hedge Funds can have a positive impact for many people, and is does not just benefit the super-wealthy. • Hedge Fund Managers are entrepreneurs, and in some sense, the “small business owners” in the financial landscape (compared to banks, governments, etc. ) • Hedge Fund Managers – meritocracy • Hedge Funds did not take any money during the bail-out of 2008 -09. • Hedge Funds usually are not harmful to the environment • Hedge Funds help to create jobs and efficiency. Sigma Analysis & Management Ltd. www. sigmanalysis. com 7

2. The Mathematics of Liquidity THE MATHEMATICS OF LIQUIDITY Sigma Analysis & Management Ltd. www. sigmanalysis. com 8

BALLS IN THE HAT GAME Balls in the Hat Game "Business is a game. “ - IBM founder Thomas J. Watson Consider the following game (Balls in the Hat Game) – There’s a hat with 6 black balls and 4 white balls. – At each turn you choose whether to draw out a single ball at random, without replacement. – You gain $1 for each white ball drawn, and lose $1 for each black ball drawn. – The game ends when you choose not to remove any further balls or when the hat is empty. Would you want to play this game? Why? If someone were to play this game, would you expect him/her to lose money? Sigma Analysis & Management Ltd. www. sigmanalysis. com 9

The Value of Liquidity Joint work with Dr. Niall Whelan, Scotia Capital. Answer to the Balls in the Hat Game …. – Consider the hat of size six black balls and four white balls. – Intuitively, one might think that it is not worth playing since there are more black balls than white balls. – Surprisingly the expected value of this game is positive; equal to 1/15. – Thus, it makes sense to play the game! – WHY IS IT POSITIVE? ? – The reason is that the ability of being able to stop at any time overcomes this imbalance of black balls to white. There is a value of the player's right to stop. – Liquidity risk arises from not being able to pull one’s money out of an investment instantaneously at “fair” price – Being able to stop playing the game at any time is analogous to liquidity Sigma Analysis & Management Ltd. www. sigmanalysis. com 10

The Power of Liquidity Hat with six black balls and four white balls has a positive expected value of +1/15 Power of liquidity (i. e. being able to stop playing any time) overcomes the negative imbalance of black balls to white balls Whelan and Bhaduri (2008) Sigma Analysis & Management Ltd. www. sigmanalysis. com 11

Solution Template to Balls in the Hat Game Sigma Analysis & Management Ltd. www. sigmanalysis. com 12

Hotel California – “You can check out anytime, but you can never leave…” Sigma Analysis & Management Ltd. www. sigmanalysis. com 13

Statistical Analysis of Investment Choices Liquidity – the Forgotten Dimension in statistical analysis? Consider the following Scenario: – Hedge Fund A has a 2 -year Lock-up, annual redemptions, and trades in illiquid instruments (distressed debt, structured credit, OTC derivatives) – Hedge Fund B has no Lock-up, monthly redemptions, and trades in illiquid instruments – Hedge Fund C, which is quantitative, has no Lock-up, monthly redemptions, and trades in liquid instruments – All three hedge funds have a 5 -year track record. Sigma Analysis & Management Ltd. www. sigmanalysis. com 14

Statistical Analysis of Investment Choices Liquidity – the Forgotten Dimension in statistical analysis? Consider the following Scenario: – Is it fair just to compare the statistics (return, volatility, skew, kurtosis, omega, Sharpe, etc. ) and the risk factors of these three hedge funds? • If so, then liquidity is getting a value of zero. (i. e. the value of liquidity once again being underestimated – But Hedge Fund C has the best liquidity and liquidity has a value! (Fund A has bad liquidity, Fund B has a liquidity mismatch which is a risk) – Mistake: Liquidity ignored in statistical analysis of investment decisions – Liquidity Risk is a composition of both how onerous the lock-up & redemption terms are as well the volume & complexity of the underlying instruments that it trades Sigma Analysis & Management Ltd. www. sigmanalysis. com 15

Model Risk – No Model is Perfect Know Model Risk “Nothing at MIT had ever reminded me of my lab at home. I suddenly realized why Princeton was getting results. They were working with the instrument. They built the instrument; they knew where everything was, they knew how everything worked. ” - Late Nobel Laureate Richard Feynman on why Princeton’s cyclotron was getting better results than MIT’s. Sigma Analysis & Management Ltd. www. sigmanalysis. com 16

Connection Between Liquidity Risk and Model Risk –Liquidity Risk and Model Risk are entangled –In general, the less liquid the instruments that are traded, the MORE the hidden risk, and the more dangerous model risk becomes. –Example: credit crisis and financial crisis of 2008 –Liquid Hedge Funds tend to have less hidden risks. (Exchange-Traded => no valuation/accounting issues, no smoothing) “The market can stay irrational longer than you can stay solvent. ” - John Maynard Keynes Sigma Analysis & Management Ltd. www. sigmanalysis. com 17

Liquidity Solutions Liquidity and Portfolio Management – Risk and Return are two sides of the same coin – In portfolio construction, one MUST take liquidity of the underlying investments into consideration – Liquidity buckets – Liquid instruments tend to have less “hidden” risks – Liquidity mismatches – Liquidity vultures – Liquidity derivatives? Sigma Analysis & Management Ltd. www. sigmanalysis. com 18

Liquidity Buckets and Liquidity Index Liquidity Buckets furnishes a simple, yet useful way for portfolio managers to assess their portfolio with a liquidity lens. 1. List the investments in ascending order via liquidity 2. Partition the list into liquidity buckets 3. Calculate the average statistics over a common time interval for each of the liquidity buckets Liquidity buckets applied to hedge funds in June 2004 – June 2007 (i. e. before the financial crisis) showed that hedge funds that were less liquid were not statistically better than those that were more liquid. (AIQ – Second Quarter 2008, Bhaduri & Art) Sigma Analysis & Management Ltd. www. sigmanalysis. com 19

LIQUIDITY DURATION • Sigma Analysis & Management Ltd. www. sigmanalysis. com 20

Pattern of Derivatives “A mathematician, like a painter or poet, is a maker of patterns. If his patterns are more permanent than theirs, it is because they are made with ideas. ” ― G. H. Hardy, A Mathematician’s Apology Derivatives – take a risk, isolate it, and redistribute it – Equity Derivatives (equity risk) – Foreign Exchange Derivatives (currency risk) – Interest Rate Derivatives (interest rate risk) – Credit Derivatives (credit risk) – Weather Derivatives (weather risk) – Real Estate Derivatives (real estate risk) Next… – Liquidity Derivatives! (liquidity risk) … this relates somewhat to futurisation Sigma Analysis & Management Ltd. www. sigmanalysis. com 21

Why Quant Strategies are the Liquid Alpha strategies • Statistical Arbitrage, Market-Neutral Equity, Quantitative Equity, Systematic CTAs (aka Managed Futures), and other Quantitative and Statistical strategies whose trading domain is contained in equities, futures, options, and FX – are all liquid. • By trading liquid, exchange traded instruments (or deep FX), these managers are able to utilize data that is clean and true data (not marked in a way that is artificial). • BIG Data, advanced computing power, all lends itself well to a lot of testing, developing, on both the alpha generation, as well as stress testing, scenario testing, and risk management. • Hence, Quantitative strategies tend to have: ü Less model risk (since dealing with liquid instruments) ü More ability to invoke an impressive array of mathematical techniques ü More ability to do testing and development • Do not mistake illiquidity for alpha! (illiquid strategies are fine – just make sure that you are being paid properly for them) Sigma Analysis & Management Ltd. www. sigmanalysis. com 22

Blending Quant Equity Hedge Funds and CTAs is efficient Portfolio Construction Technique: Overlay/Underlay Alternatives Blend “Tradition is a guide, not a jailer. ” – W. Somerset Maugham Sources: Portfolio Construction Technique: Overlay/Underlay Alternatives Blend, Bhaduri & Lobachevskiy, Alternative Investment Quotient, Sep 2011 Kat, Harry. "Managed Futures and Hedge Funds: A Match Made in Heaven“, 2004 Sigma Analysis & Management Ltd. 23 www. sigmanalysis. com

LIQUIDITY IS THE FIRST LINE OF DEFENSE “Liquidity is the first line of Defense. ” - Daniel Mac. Donald, CFA, Portfolio Manager, Alternative Investments Ontario Teachers Pension Plan Sigma Analysis & Management Ltd. www. sigmanalysis. com 24

LIQUIDITY MATTERS – Dangerous to trust one’s “intuition” (i. e. laziness) with regards to the value of liquidity – Behavioral Finance – easy to underestimate the value of liquidity – Mathematical subtleties in liquidity – In illiquid investments, model risk gets magnified. Liquid hedge funds have less hidden risks. – In liquid hedge funds, there is more data (by definition), and the data is more meaningful – Quant strategies dovetail well with liquid instruments (more data, better quality data) – Blending quantitative equity hedge funds with CTAs in an overlay/underlay strategy is an effective portfolio management technique (as always, rigorous due diligence needed) – Proper and prudent portfolio management and risk management gives REAL attention to liquidity Sigma Analysis & Management Ltd. www. sigmanalysis. com 25

3. The Omega Function THE OMEGA FUNCTION Sigma Analysis & Management Ltd. www. sigmanalysis. com 26

The Need for a better Performance Measure • Do not ignore the Non-Normality! • Alternative investments (hedge funds, private equity, commodities, and real estate) typically have non-normal distributions. • Sharpe Ratio = ( µ - rf) / σ Problems: Look at its formula – the Sharpe Ratio only uses mean & variance. This approach totally discards skew, kurtosis, and all of the other higher statistical moments! Thus, it does not capture all of the risk-reward features unless the distribution is normal. • • WHY RELY ON A TECHNIQUE THAT RESTS ON AN ASSUMPTION THAT WE KNOW TO BE FALSE? NO distinction between upside volatility and downside volatility!

Information Ratio - cousin of the Sharpe Ratio Not much info! • If the information ratio is higher does that mean it is better? Fund A beats benchmark by 0. 5% each month. • • Fund B beats benchmark by 0. 8% in half of the months and 1% in • • • half of the months. IF all else equal, which Fund do you prefer? But. . . Fund A has the higher information ratio. Information Ratio = (Ann. Rtn(r 1, . . . , rn) - Ann. Rtn(s 1, . . . , sn)) / Ann. Std. Dev(e 1, . . . , en)

“Modern” Portfolio Theory Using cutting-edge techniques from the 1950 s Why not utilize the computing power available today? – “Modern” Portfolio Theory using Markowitz mean-variance optimization (1952) was based on computing power available in the 1950 s. – It does not distinguish between upside and downside volatility – It does not take into account skew, kurtosis, or any of the higher moments (hence the name “mean-variance optimization) • Oversimplifies the statistical distribution, does not take into account fat tails or skew, and does not distinguish between upside volatility or downside volatility.

• Markowitz - Essentially a re-run of the problems with the Sharpe Ratio • The Omega Function does NOT suffer from any of these problems! • Applying better statistical methods can give a competitive edge. (Moneyball) “Tradition is a guide and not a jailer. ” W. Somerset Maugham Sigma Analysis & Management Ltd. www. sigmanalysis. com 30

What is the Omega Function – Invented by mathematicians Keating & Shadwick in 2002, it can be thought of as the quality of an investment on a return above a given level (threshold) – Essentially a ranking function that captures return, variance, skew, kurtosis, and of the higher statistical moments – without penalizing for upside volatility. (i. e. all the higher moments are encoded in the Omega function) – Has been referred to as “a sharper Sharpe” – Omega does not reward smoothing (unlike Sharpe) • An Omega value of less than 1 implies that the quality of the investment is low with respect to the selected threshold, and that the selected threshold is higher than the mean of the investment’s return series. • An Omega value of greater than 1 implies that the selected threshold is less than the mean of the investment’s return series. • An Omega value equal to 1 implies that the selected threshold is equal to the mean of the investment return series.

Mathematical Definition of Omega • Where F is the cumulative • distribution of returns, and r is the • threshold chosen by the investor.

Omega – the Finance Intuition • R is the threshold value (and the strike) • C(R) and P(R) are prices of one period • European call and put prices; • The underlying is the security’s RETURN, • not the security’s price. • Numerator = E [ max (x – R, 0)] • Denominator = E [ max (R – x, 0)] • Can be thought of as the quality of an investment on a return above a given level (threshold); • “quality” is upside versus downside – – Kazemi, Schneeweis, and Gupta proved that the mathematical definition of Omega is equivalent to the finance definition above. Math-Finance Duality

Mathematical Proof that Omega at the Mean is One

Mathematical Proof that Omega at the Mean is One

Mathematical Proof that Omega at the Mean is One

Mathematical Proof that Omega at the Mean is One

Mathematical Proof that Omega at the Mean is One

Omega Graphs – Geometry of Risk?

Using Sharpe Ratio leaves Investor vulnerable to smoothing by Illiquid Hedge Funds • • • Let σReported = λ*σTrue , where 0 < λ ≤ 1 (if λ=1 then no smoothing is being done) Then the Sharpe. Reported = (E(r) – rf)/ σReported = (E(r) – rf)/ λ*σTrue = (1/ λ) * Sharpe. True Thus, the reported Sharpe increases by a factor of (1/ λ), which can be fairly substantial in some cases. The effect of smoothing on an Omega function is mixed. Smoothing will increase the Omega value for lower thresholds, but decrease the Omega value for higher thresholds. This is intuitive from the mathematical definition of Omega. Essentially the Omega graph will have a higher y-intercept but will have a steeper slope, and thus a lower robustness coefficient [e(dΩ /dr)].

How Can the Omega Function and Omega Graphs be used? – Performance Review – Peer Analysis – Comparative Analysis – Quantitative Due Diligence – Risk Monitoring – Quantitative Leverage-setting Tool – Robustness of Portfolio = e(dΩ /dr) – Fine-tuning the tail – Portfolio optimization – Three-dimensional Omega – the geometry of risk?

4. Assorted Remarks about Mathematical Techniques in Hedge Funds Sigma Analysis & Management Ltd. www. sigmanalysis. com 42

Nuggets of Wisdom … No Model is Perfect … understand the limitations, input and output – be able to explain it Know Model Risk “Nothing at MIT had ever reminded me of my lab at home. I suddenly realized why Princeton was getting results. They were working with the instrument. They built the instrument; they knew where everything was, they knew how everything worked. ” - Late Nobel Laureate Richard Feynman on why Princeton’s cyclotron was getting better results than MIT’s. 102309 CA 43

Model Risk – Anscombe’s Quartet Don’t regress too much! Each data set has the same Mean, Variance, t-stat, etc. , and leads to the same regression line!

Some Nuggets of Insights – The Viciousness of Percentages • This table helps to demonstrate the importance of cash preservation, and the risk involved in trying to make an investment that “swings for the fences”. • The formula used to generate the above is that a loss of k, requires a gain of k/(1 -k). • Living by the sword can lead to dying by the sword. It becomes increasingly difficult of recovering from a large loss. • The above table does not take the time value of money into consideration (i. e. that a dollar two years from now, is worth less than a dollar today), and consequently is conservative in its assessment of the increase required in order to come back. Percent Loss Percent Gain Needed to Get Back to Even 5% 10% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95% 96% 97% 98% 99% 5. 26% 11. 11% 25% 33. 33% 42. 86% 53. 85% 66. 67% 81. 82% 100% 122. 22% 150% 185. 71% 233. 33% 300% 400% 566. 67% 900% 1900% 2400% 3233. 33% 4900% 9900% 45

The Gretzky Rule: Don’t Chase Returns!

Black-Scholes-Merton Equation • S is the price of the stock • V (S, t)is the price of a derivative as a function of time and stock price. • σ the standard deviation of the stock's returns; this is the square root of the quadratic variation of the stock's log price process. • r the annualized risk free interest rate, continuously compounded. Sigma Analysis & Management Ltd. www. sigmanalysis. com 47

Cryptography • Focused Research • • Centre for Applied Cryptographic Research (CACR): A joint project between the University of Waterloo, the Federal Government of Canada, and a number of corporations. (www. cacr. math. uwaterloo. ca) University Cryptography Departments • • • University of Waterloo: Cry. SP (crysp. uwaterloo. ca) Mc. Gill University: CQIL (crypto. cs. mcgill. ca) University of Calgary: CISa. C (cisac. ucalgary. ca) Columbia University: Crypto. Lab (www. cs. columbia. edu/crypto) Mathematicians (Number Theory, Computer Science, Cryptography) • • Alex Stanoyevitch, California State University Dominguez Hills Claude Levesque, Université Laval Neal Koblitz, University of Washington Alfred Menezes, University of Waterloo Sigma Analysis & Management Ltd. www. sigmanalysis. com 48

5. Some Nuggets of Wisdom – Success in finance industry / business world Sigma Analysis & Management Ltd. www. sigmanalysis. com 49

Some Nuggets of Wisdom for the Business / Finance World 1. (M) In the math world, one has to be precise, and not much BS, since one has to prove any statement (a proof is forever). (B/F) In the Business world / finance world – people (more frequently) make incorrect statements, and sometimes do so with a lot of confidence 2. (M) In the math world, substance trumps everything. (B /F) In the business / finance world, style matters (substance is still the most important, but style is important). 3. (M) In the math world, intellectual curiosity is a very key ingredient (B/F) In the business/finance world, while this is still a very useful trait to have, one needs to remember that most people only care about two things: – How much money does it make me? – How does it make my life easier? Sigma Analysis & Management Ltd. www. sigmanalysis. com 50

Some Nuggets of Wisdom for the Business / Finance World • • MOST IMPORTANT – Good integrity & ethics – Right thing to do, plus good for business (where is Enron today? ) – Your reputation is important Work hard (competitive world) – Care about the quality of your work – Be reliable and responsible – Be dependable Communicate well (TA / Teach / Tutor) Someone above you should be a supporter Do not do “bad” politics Do not be afraid to rap a little Network / keep in touch Keep learning Sigma Analysis & Management Ltd. www. sigmanalysis. com 51

Some Nuggets of Wisdom for the Business / Finance World • Surviving Success (it’s one thing to have success, it’s another to survive it) – “You’re only as good as your last shift” – old hockey axiom – Go into the corners (or in the slot) – Respect the markets (no crystal ball – Amaranth example) • Research is absolutely critical (firms that do not invest sincerely in research, don’t or won’t last) • Diversification – the only free lunch in finance (if you don’t invest in hedge funds, then you should be forced to justify why in writing) • The Human Element • Act in the light of intelligence, guided by experience with reason. Sigma Analysis & Management Ltd. www. sigmanalysis. com 52

Acknowledgements THANKS! • Dr. Alex Stanoyevitch, California State University Dominguez Hills, Department of Mathematics • Wai Yan Pong, California State University Dominguez Hills, Department of Mathematics • All of you! Sigma Analysis & Management Ltd. www. sigmanalysis. com 53

Appendix – Who is Sigma Analysis? • Sigma Analysis & Management Ltd. is a Toronto-based firm with operational excellence, world-class hedge fund research with an elite quant group, that delivers high-touch, value-add customized service in the hedge fund space. • Sigma Analysis was founded in 1999 by Dave Rudd and Dr. Luis Seco to help service institutional investors in its hedge fund portfolio construction and risk monitoring. Dr. Luis Seco has Ph. D in Mathematics from Princeton University and is a Professor of Mathematics at the University of Toronto. • Professionals on Sigma Analysis have global experience from well known firms including: Goldman Sachs, Morgan Stanley, State Street, Citco Fund Services, Ernst & Young, and the Montreal Exchange. • Sigma Analysis services include: • – Customized Managed Account & Transparency services ü operations, technology, risk monitoring, smart risk analytics, reporting, & research – Customized Research & Advisory services ü due diligence support, hedge fund origination, asset allocation analysis, structural alpha analysis, portfolio construction, quantitative analytics, and mathematical and statistical analysis, & commissioned research studies on specialized topics in hedge funds, portfolio management, and risk management – Customized Education & Training services ü Educational workshops and/or Customized Seminars on a variety of topics (including due diligence, hedge fund strategies, structural alpha, portfolio construction, risk management, managed accounts, operations, quantitative finance, risk management, governance, ethics & integrity); Training of Analysts (due diligence, hedge fund operations, risk management, & hedge fund research). Sigma Analysis is a pioneer in hedge fund research, smart risk analytics, transparency services, and managed accounts. Sigma Analysis & Management Ltd. www. sigmanalysis. com 54

Appendix - Sigma Analysis - Transparency • Transparency requires Technology – Sigma Analysis has built proprietary software which helps to empower the client – Sigma Analysis continues to invest in research & technology – Protect against fraud, strategy drift, style drift, concentration risk – Gain structural alpha • Sigma Analysis will furnish truly customized solution • Sigma Analysis is client-focused with excellent service Sigma Analysis & Management Ltd. www. sigmanalysis. com FOR EDUCATIONAL AND DISCUSSION PURPOSES ONLY 55

Bio: Dr. Ranjan Bhaduri is the Chief Research Officer. Prior to joining Sigma in 2014, he was the Chief Research Officer and the Head of Product Development at Alpha. Metrix Alternative Investment Advisors. At Alpha. Metrix, Dr. Bhaduri designed and led an institutional due diligence and research program. Dr. Bhaduri worked closely with institutional clients on portfolio matters. Prior to joining Alpha. Metrix, he was a Vice President and on an Investment Committee at Morgan Stanley where he conducted due diligence and helped design customized portfolios of Alternatives. Earlier, he was at a Canadian Fund of Funds, and at a multi-billion dollar capital management firm where he was involved in all aspects of its fund of hedge funds and structured finance business. He has also worked with two major Canadian investment banks in the Financial Strategy Consulting Group and in Global Risk Management & Control, respectively. Dr. Bhaduri has held advisory roles at the East-West Center, a leading think tank on the Asia-Pacific region, and at Class. Mouse, an early stage software company. He has taught finance and mathematics at several universities and lectured on Derivatives for the Montreal Exchange. Dr. Bhaduri has published papers on, and been invited to speak worldwide regarding hedge fund issues, and advanced portfolio and risk management techniques. Dr. Bhaduri has a M. Math in Combinatorics & Optimization from the University of Waterloo (1992), a Ph. D in Mathematics from the University of Hawaii (1999) and an MBA from Mc. Master University (2002). Dr. Bhaduri holds both the CFA and CAIA charters. He is a member of the American Mathematical Society, the Mathematical Association of America, the Toronto CFA Society, and the Global Association of Risk Professionals (GARP). Dr. Bhaduri has previously served as a member of the All About Alpha Editorial Board, and as a CAIA Chicago Chapter Executive.

Contact Info. Ranjan Bhaduri Ph. D, CFA, CAIA, MMath, MBA, BSc (Honours) Chief Research Officer Sigma Analysis & Management, Ltd. Ma. RS Discovery District 101 College Street Suite 345 Toronto ON M 5 G 1 L 7 Canada. Email: ranjan@sigmanalysis. com Phone: +1 -416 -716 -0341 Sigma Analysis & Management Ltd. www. sigmanalysis. com 57

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