FORMIGA TRADING STRATEGY QUANTITATIVE INVESTMENT

FORMIGA TRADING STRATEGY - QUANTITATIVE INVESTMENT

The team of world class quants.

Creating of trading robots - the tiny working ants of trading performance.

Formiga team of trading robot has been developed from the best possible mathematician and physician talent. From simple trade pattern robot to sophisticated algorithm robot, Formiga runs the gambit of amazing individual teammates.

The story began more than 7 years ago when a group of mathematicians at the university started to create and develop some algorithms in connection with futures market movements. Within the last seven years, the strategy has been further developed and tested under real life cicumstances. The poduct is a knowledge- intensive sc ience-dr iven product, which is the natural result of years long research and investment. Not only time was required to develop this product but also real capital. We are proud to tell you that we invested more than thirty-five million dollars into IT-capacities and human brain to develop the product as we have today. Currently the costs of maintenance of our hardware and software are one of the major costs of the company. Our team of mathematicians, technology officers and software developers with their knowledge and experience are the main company value. This shows our strong commitment to the newest technology and approach to continuously adjust and improve the strategy and portfolio allocation of each futures based on every single market movement. This is what we call systematic trading.

✦ Successful algo trading since 2011✦ USD 35m own capital invested in

product development✦ USD 5m invested in hardware and

software✦ BDO audited results of 40%+ return pa

since 2011, 90% return in 2016, all inUSD terms

✦ Algo trading all automatic, instrumentsin 52 commodity futures, traded in USA,Europe and Asia with market liquidityover 900 bln USD daily

✦ O w n s o f t w a r e , t r a d i n g r o b o tdevelopment/optimization

✦ Private cloud, calculation capacity of6200 Computer Cores

✦ Looking for international capital raisingpartnership

FORMIGA TEAM

✦ Total number of employees - 50 (8PhDs)

✦ Total number of world class quants andprogrammers - 8

✦ To t a l n u m b e r o f s t o c k m a r k e texperience of key people - 150 years

✦ Executives hold EMBA and MBAdegrees from the best business schools

FORMIGA HARDWARE AND SOFTWARE

✦ Total cost of hardware and software -USD 5 mln

✦ Unique multilevel risk-managementsystem (provided by InfoReach)

✦ Volume of data processing annually: 10bln years. Graphics processing unitsincreases normal processing speed in3000 times

✦ Maximum speed of order log messages:3 bln messages a second

FORMIGA TEAM 16% PHDS

THE GAMBIT

Formiga does not pretend to know in which direction the market will move and it does not take any bets on the future market movements. A gambit is an act or remark that is calculated to gain an advantage, especially at the outset of a situation. This term is often used in the chess. Formiga quant team uses it in connection with their individual robots which do act only for themselves according to h istor ica l parameters. Some of the designers like to call it uncorrelated not only within all the other robots but also with the market. Others call it a market neutral strategy.

THE TRADE

Formiga trading universe has been limited to 52 most liquid futures contracts and 15 main world exchanges. The strategy does not invest in any other single instrument, nor in any nontransparent fund. Formiga Strategy is programmed to invest only within these futures. No single human is taking any trade decision, subjectivity is not allowed. However there is a third-party risk monitoring company which is checking each trade in real time based on the overall strategy

logic and the accumulated exposure. Formiga Strategy happens to trade from 2'000 up to 10'000 per day. Please do not however compare it to High Frequency Trading as Formiga algorithms make trade decision based on a 5mn bar. The average duration of a trade is approx. 2.5 days.

THE EXECUTION

Formiga quants have been trading consistently since 2011, mainly for the seed investors. The team has now decided to launch a product which will replicate the trading pattern of the individual robots. This means that Formiga investors act on their risk tolerance an amount of money into this strategy. This will allow them to participate with a relatively low amount of money to the performance of a hedge fund similar strategy, without having to bear the extremely high liquidity costs normally linked to hedge funds.

A SWISS DESIGNED ASSET MANAGEMENT STRATEGY

The investment strategy is a volatility based Futures Trading strategy. This is made of multiple formal trading rules. After such trading rules are defined, the rule is added to the portfolio in form of a computer algorithm. The way the portfolio of rules is structured distributes the risk across various strategies and sectors so that if some systems were experiencing a draw-down in an unfavorable market environment the remaining portion of the portfolio would compensate such a draw-down, while the principal capital would be preserved.

A S S E T A L L O C A T I O N UNCORRELATED TO THE MARKET AND SYSTEMATIC

The investment strategy is based on the volatility of movements within a defined set of futures instruments. There is an automatic asset-allocation mechanism which allocates less weight to the more volatile instruments and more weight to the less volatile. To perform this asset-rebalancing within the futures, only the historic volatility will be measured. The portfolio and its trading strategies are designed using systematic methodology. Under ly ing indiv idual systems are developed using top-bottom market analysis. The validity of a quantitative market behavior hypothesis is checked by means of applying various mathematical and statistical analyses thereby verifying the hypothesis’ significance.

There are 120 individual core programs (algorithms) in average with 52 futures contracts. The investment strategy cannot predict the future and therefore the system consists of more than 4000 individual algorithms between all futures contracts, which are deployed through each market and through each future contract. There are therefore different algorithms for each single future contract. Each algorithm is working independently from each other

and is taking its decision systematically and within the set risk parameters. As a result of it, the correlation between 90% of the algorithms deployed is less than 7%.

Each algorithm has its own set of parameters to enter and to exit the group of positions invested into. The calibration of the system (fast feedback loop on an ongoing basis) is carried out on a systematic and constant basis. This means that the system is constantly checking which parameters and which weight of the respective future instruments are giving the best result for a trading rule. The trading program is designed to perform in high/mid-range/low volatility environment in any risk assets class included into the trading universe.

FORMIGA STRATEGY OVERVIEW 7-YEAR TRADING RESULTS

FORMIGA STRATEGY DESCRIPTIONSTRATEGY OBJECTIVE To achieve absolute noncorrelated return

significantly exceeding average market return in a short-to-medium term investment horizon

AVERAGE EXPECTED STRATEGY RETURN, % 40-60% p.a.

BENEFITS OF THE STRATEGY High return Global markets Fast response to market movements

INCEPTION 2011 february

TRACK-RECORD Quarterly BDO audit

TYPE Quantitative investing

Year-to-Date (as on the 31 of August) 64,54 %

August 2017 16,33%

Sharpe Ratio 1,19

ADVANTAGES

✤ Liquid Global Markets✤ Diversified Market Risk✤ Uncorrelated Returns✤ Statistical Stress Testing in Robot

Selection✤ Portfolio with Positive Expectation

of Future Returns✤ Fully Automated Execution✤ Multi Level Risk Management✤ Continuous Trading System

Evolution✤ Multi Task Scalable Proprietary

Software & Hardware

OVERVIEW

✤ 52 future contracts✤ 15 markets✤ 24/7 product support✤ 3000 trades per day✤ 2.5 days average holding period

Explanation of the 2013 results: The Quantitative Easing from September 2012 started a "risk on”mode, which drastically reduced the volatility worldwide. The system at that time was calibrated based on the historical performance / volatility from 2006 onwards. In 2012 the system was re-calibrated to take account of the new parameters, however it took some time to be translated again in positive results. Currently the system is having all the phases of volatility and can outperformance the market in low, middle and high-volatility environment.

AVERAGE EXPECTED RETURN: 40-60%

* The Formiga Trading Strategy (the "FTS") has been launched on June 26, 2017. In accordance to the "Presentation of investmentperformance in compliance with the Global Investment Performance Standarts (GIPS®)" (avaliable on the Homepage in the sectionPerformance), the FTS has been included in the Composite. The performance is historical; past performance is not a reliableindicator of future results and investors may not recover the full amount invested. Investments in Futures Markets involves higherelement of risk. The value of your investment can greatly fluctuate as a result of the investment policy and is not guaranteed.

PERFORMANCE of the COMPOSITE including the FORMIGA TRADING STRATEGY (*)

COMPOSITE PERFORMANCE

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2017 64.54% 15.01%

2016 87.33% 25.71% 20.63% -5.69% -6.70% 20.93% -1.45% -1.25% 2.67% 2.40% -7.30% 12.96% 8.36%

2015 33.31% -10.13% 4.84% -0.83% -3.14% -5.86% -13.56% 29.82% 1.33% 1.61% 23.19% 10.66% -0.67%

2014 11.49% -6.82% 6.07% -4.41% -3.48% 11.14% 9.79% 5.58% 4.97% 0.47% 0.08% 1.23% -11.17%

2013 -23.85% -0.09% -2.99% -3.67% -1.07% -10.48% 2.37% -0.09% -14.70% -6.10% 0.50% -0.48% 12.40%

2012 35.99% 9.79% -1.25% 0.31% -8.75% 13.65% -6.21% 5.01% -5.25% 12.35% 12.09% 4.03% -1.37%

2011 154.53% 4.96% 24.33% 18.14% 5.83% -7.49% 14.78% 7.30% 27.30% 6.63% -1.99% 2.93%

-7.01% 3.88% -0.85% 12.20% -7.22% 23.35% 16.33%

COMPOSITE PERFORMANCE - CUMULATED

August Qtr YTD Since Inception Since Inception, annualized

Formiga Trading Strategy 16.33% 43.50% 64.54% 1107.67% 46.13%

* The Formiga Trading Strategy (the "FTS") has been launched on June 26, 2017. In accordance to the "Presentation of investmentperformance in compliance with the Global Investment Performance Standarts (GIPS®)" (avaliable on the Homepage in the sectionPerformance), the FTS has been included in the Composite. The performance is historical; past performance is not a reliableindicator of future results and investors may not recover the full amount invested. Investments in Futures Markets involves higherelement of risk. The value of your investment can greatly fluctuate as a result of the investment policy and is not guaranteed.

Composite Performance

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STRATEGY PERFORMANCE FOR 12-MONTH PERIODS

2011 February - 2017 August

The recommended investment period is 1 year. There is no more or less advantageous moment for starting quantitative investing. It is possible to invest and withdraw money any time of the calendar year. The bar chart below shows the most and the least profitable periods of the strategy since its inception. The length of each period is 12 months. The first period: February 2011 – January 2012. The second period: March 2011 – February 2012, etc.

-40% 0% 40% 80% 120% 160% 200%

07/2015 — 06/2016

02/2011 — 01/2012

03/2011 — 02/2012

06/2015 — 05/2016

10/2015 — 09/2016

09/2015 — 08/2016

08/2015 — 07/2016

03/2015 — 02/2016

04/2011 — 03/2012

04/2015 — 03/2016

05/2015 — 04/2016

01/2016 — 12/2016

02/2015 — 01/2016

09/2016 — 08/2017

07/2011 — 06/2012

06/2011 — 05/2012

12/2015 — 11/2016

02/2016 — 01/2017

11/2015 — 10/2016

08/2016 — 07/2017

05/2011 — 04/2012

08/2011 — 07/2012

05/2016 — 04/2017

04/2016 — 03/2017

09/2011 — 08/2012

06/2016 — 05/2017

12/2011 — 11/2012

12/2013 — 11/2014

10/2013 — 09/2014

11/2013 — 10/2014

01/2012 — 12/2012

07/2016 — 06/2017

11/2011 — 10/2012

01/2015 — 12/2015

03/2016 — 02/2017

09/2013 — 08/2014

10/2011 — 09/2012

05/2012 — 04/2013

02/2012 — 01/2013

03/2012 — 02/2013

12/2014 — 11/2015

04/2012 — 03/2013

01/2014 — 12/2014

05/2014 — 04/2015

04/2014 — 03/2015

11/2014 — 10/2015

07/2012 — 06/2013

02/2014 — 01/2015

03/2014 — 02/2015

08/2013 — 07/2014

08/2012 — 07/2013

07/2013 — 06/2014

06/2012 — 05/2013

06/2014 — 05/2015

06/2013 — 05/2014

09/2012 — 08/2013

08/2014 — 07/2015

10/2014 — 09/2015

09/2014 — 08/2015

10/2012 — 09/2013

03/2013 — 02/2014

04/2013 — 03/2014

01/2013 — 12/2013

05/2013 — 04/2014

07/2014 — 06/2015

02/2013 — 01/2014

11/2012 — 10/2013

12/2012 — 11/2013

STATISTICS & RATIOSReturn

Average RoR: 3,66%

Win Months, % 50,91%

5% quantile annual return 12,9%

95% quantile annual return 82,1%

Risk 

Volatility 38,81%

Lose Months, % 49,09%

Average Drawdown Depth (out of top 5) 24,59%

Reward/Risk

Sharpe Ratio 1,19

Sortino Ratio 1,60

Sterling Ratio 1,32

Skewness 0,91

Kurtosis 5,49

STATISTICS & RATIOSDepth Length (Days) Recovery (Days) Start End

39.86% 1068 115 11/19/2012 10/23/2015

27.51% 83 59 09/23/2016 12/15/2016

22.93% 119 48 03/08/2016 07/05/2016

16.82% 14 11 05/02/2011 05/16/2011

15.83% 211 127 02/09/2012 09/07/2012

STATISTICS & RATIOS

1 Month 3 Month 6 Month 12 Month

Best period 34,65% 77,35% 132,85% 193,04%

Average period return 3,50% 10,01% 20,51% 38,51%

Worse period -27,37% -27,80% -30,10% -36,17%

FORMIGA STRATEGY PERFORMANCE BEFORE FEES Past performance does not guarantee or indicate future results

FORMIGA STRATEGY PERFORMANCE COMPARED TO BARCSYST INDEX AND TOP HEDGE FUNDS’ RETURNS*

* The index’ and funds’ monthly returns are adjusted to the corresponding FORMIGA STRATEGY volatilityby multiplying the respective index’ and funds’ monthly returns by the respective K coefficient.

Transtrend Crabel Winton Bridgewater TwoSigma QIM BARCSYST CTAtrend CTAnon FORMIGA

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Transtrend Crabel Winton Bridgewater TwoSigma QIM BARCSYST CTAtrend CTAnon Formiga

K* 3,0 2,7 4,2 5,3 4,0 3,9 6,2 3,3 5,8 1,0

Sharp 0,1 0,3 0,4 0,3 1,5 0,4 -0,1 0,1 0,2 1,0

2011 -29.2% -6.1%  - 126.6% -1.5% 14.3% -25.3% -24.9% -3.0% 154.5%

2012 -1.9% 73.6% -0.1% 2.0% 15.3% 11.5% -23.0% -14.7% -15.6% 36.0%

2013 -3.5% -1.4% 26.2% 17.7% 63.0% -3.7% -8.3% 6.3% -8.8% -23.8%

2014 59.0% 38.9% 66.1% 10.5% 136.5% -41.6% 74.4% 72.8% 83.5% 11.5%

2015 -14.4% -8.0% -3.5% 8.4% 55.7% 95.9% -20.8% -4.4% -6.6% 33.3%

2016 19.0% 16.4% -1.6% 3.3% 46.1% 73.2% -13.4% -22.5% -7.5% 87.3%

2017 -8,8% -18,2% 3,8% -7,6% -16,3% 8,4% -7,9% -8,4% 1,3% 64,5%

August 2017 15,0 % -1,4% 10,7% 1,4% 0,4% -11,5% 3,8% 8,4% 8,8% 16,3%

SI -0,9 % 95,5 % 106,3 % 211,3 % 733,6 % 163,4 % -41,9 % -20,3 % 20,2 % 1107,7 %

FORMIGA STRATEGY - INTERNATIONAL MARKETS Number of traded instruments – 52 Number of exchanges – 15

TRADED SYNTHETIC FUTURES Number of traded synthetic futures pairs – 30

GLOBEX CBOT EUREX NYMEX ICE USE-mini NASDAQ 100 Futures Mini Sized Dow Jones

Industrial Average $5Dow Jones Euro STOXX50

Futures Light Sweet Crude Oil Futures NYBOT US Dollar FX Futures

E-mini S&P 500 Futures 2 Year US Treasury Note DAX 30 Index Futures Natural Gas Futures Cotton No. 2 Futures

Yen denominated Nikkei 225 Index Futures

5 Year US Treasury Note Swiss Market Index Futures Gasoline RBOB Futures Cocoa NYBOT Futures

AUD/USD Futures 10 Year US Treasury Note Euro Schatz (2 year Bond) Platinum Futures Coffee "C" Futures

GBP/USD Futures 30 Year US Treasury Bond Euro Bobl (5 Year Bond) Palladium Futures Sugar No. 11 Futures

CAD/USD Futures Soybean Futures Euro Bund (10 Year Bond)

EUR/USD Futures Euro Buxl (15-30 Year Bond)

JPY/USD Futures

COMEX SGX HKFE EURONEXT CDE

Gold Futures CNX Nifty Index Futures Hang Seng Stock Index Futures 10 Year Government ofCanada Bodns

Silver Futures MSCI Taiwan Index Futures Hang Seng China Futures

CBOE IDEM MEFF ICE EU OMXVolatility Index Futures FTSE MIB Index Futures IBEX 35 Index Futures FTSE 100 Index Futures OMXS30 Index Futures

Corn Futures

NZD/USD Futures

MXP/USD Futures

Live Cattle Futures

Feeder Cattle Futures

Copper Futures

Gold Silver Oil Equity Indices

Gold Futures AUD/USD FuturesSilver Index

FuturesAUD/USD Futures

Light Sweet Crude Oil Futures

AUD/USD Futures E-mini S&P 500 FuturesJPY/USD Futures

Gold Futures GBP/USD FuturesSilver Index

FuturesGBP/USD Futures

Light Sweet Crude Oil Futures

CAD/USD FuturesMini Sized Dow Jones Industrial Average $5

JPY/USD Futures

Gold Futures CAD/USD FuturesSilver Index

FuturesCAD/USD Futures

Light Sweet Crude Oil Futures

EUR/USD FuturesE-mini NASDAQ 100

FuturesJPY/USD Futures

Gold Futures EUR/USD FuturesSilver Index

FuturesEUR/USD Futures

Light Sweet Crude Oil Futures

Euro Bobl (5 Year Bond)

Gold Futures JPY/USD FuturesSilver Index

FuturesJPY/USD Futures

Light Sweet Crude Oil Futures

Euro Bund (10 Year Bond)

Gold FuturesEuro Bobl

(5 Year Bond)Silver Index

FuturesEuro Bobl

(5 Year Bond)Light Sweet Crude

Oil Futures5 Year US

Treasury Note

Gold FuturesEuro Bund

(10 Year Bond)Silver Index

FuturesEuro Bund

(10 Year Bond)Light Sweet Crude

Oil Futures10 Year US

Treasury Note

Gold Futures5 Year

US Treasury NoteSilver Index

Futures5 Year US

Treasury Note

Gold Futures10 Year

US Treasury NoteSilver Index

Futures10 Year US

Treasury Note

Gold FuturesLight Sweet Crude

Oil FuturesSilver Index

FuturesLight Sweet Crude

Oil Futures

CAC 40 Index Futures

AMS EOE Index Futures

BE QUANTITATIVE OR FADE AWAY

For decades, investors imagined a time when data-driven traders w o u l d d o m i n a t e financial markets. That day has arrived.

Up and down Wall Street, algorithmic-driven trading and the quants who use sophisticated statistical models to find attractive trades are taking over the investment world.

On many trading floors, quants are gaining respect, clout and money as investment firms scramble to hire mathematicians and scientists. Traditional trading strategies, such as sifting through balance sheets and talking to companies’ customers, are falling down the pecking order.

…

Algorithmic trading has been around for a long time but was tiny. An article in The Wall Street Journal in 1974 featured quant pioneer Ed Thorp. In 1988, the Journal profiled a little-known Chicago options-trading firm that had a secret computer system. Journal reporter Scott Patterson wrote a best-selling book in 2010 about the rise of quants.

Prognosticators imagined a time when data-driven traders who live by algorithms rather than instincts would become the kings of Wall Street.

That day has arrived. In just one sign of their power, quantitative hedge funds are now responsible for 27% of all U.S. stock trades by investors, up from 14% in 2013, according to the Tabb Group, a research and consulting firm in New York.

Quants have almost caught up to individual investors, which outnumber quants and collectively have 29% of all stock-trading volume.

At the end of the first quarter, quant-focused hedge funds held $932 billion of investments, or more than 30% of all hedge-fund assets, estimates HFR Inc. In 2009, quant funds held $408 billion, or 25% of all hedge-fund assets.

Quants got $4.6 billion of net new investments in the first quarter, while the ove ra l l hedge- fund bus ines s saw withdrawals of $5.5 billion.

THE QUANTS RUN WALL STREET NOW THE WALL STREET JOURNAL

QUANTITATIVE INVESTING - one of the most effective methods of asset management. Facts and quotes below are taken from main financial media.

THE QUANTS: MEET THE NEW KINGS OF WALL STREET THE WALL STREET JOURNAL

For the first time in history, there is such a thing as human-free investing. Using high-powered computers to construct abstruse m o d e l s , q u a n t i t a t i v e t r a d e r s instantaneously analyze an amount of data that is too vast for any one human mind. They siphon and sort through trillions of gigabytes of data. And now, they are training the

On many trading floors, quants are gaining respect, clout and money as investment firms scramble to hire mathematicians and scientists. Traditional trading strategies, such as sifting through balance sheets and talking to companies’ customers, are falling down the pecking order.

BLACKROCK SIGNALS ACTIVE OVERHAUL WITH SHIFT TO QUANT FINANCIAL TIMES ADVISER

Fund giant BlackRock is to overhaul its active equity strategies in the US with a greater focus on quantitative and non-human approaches, signalling a possible direction of travel for active managers in the UK and elsewhere.

…

The changes will see around dozens of fund strategies overhauled to use more quantitative methods, affecting some $30bn (£24.2bn) of assets. It also extends beyond BlackRock's suite of US equity strategies - the most affected by a shift to quantitative and passive strategies.

The $5trn fund group said it will now focus on "harnessing the power of human and machine to efficiently and consistently deliver investment performance", with a greater onus on data analysis and other quantitative methods.

…

"We really need to move to get the product set ready for the next period."

INSIDE A MONEYMAKING MACHINE LIKE NO OTHER BLOOMBERG

The Medallion Fund, an employees-only offering for the quants at Renaissance Technologies, is the blackest box in all of finance.

The fabled fund, known for its intense secrecy, has produced about $55 billion in profit over the last 28 years, according to data compiled by Bloomberg, making it about $10 billion more profitable than funds run by billionaires Ray Dalio and George Soros. What’s more, it did so in a shorter time and with fewer assets under management. The fund almost never loses money. Its biggest drawdown in one five-year period was half a percent.

For outsiders, the mystery of mysteries is how Medallion has managed to pump out annualized returns of almost 80 percent a year, before fees.

“Even after all these years they’ve managed to fend off copycats,” says Philippe Bonnefoy, a former Medallion investor who later co-founded Eleuthera Capital, a Switzerland-based quantitative macro firm. Competitors have identified some likely reasons for the fund’s success, though. Renaissance’s computers are some of the world’s most powerful, for one. Its employees have more—and better—data. They’ve found more signals on which to base their predictions and have better

models for allocating capital. They also pay close attention to the cost of trades and to how their own trading moves the markets.

Quants seem like saviors to investors disappointed with how mere mortals have managed their money of late.

I n 2 0 1 6 c l i e n t s plugged $21 billion i n t o q u a n t h e d g e funds, while pulling $60 billion from those that do everything else.

One noteworthy quant shop, Two Sigma, managed just $5 billion during the financial crisis and has seen assets jump to $37 billion. Even old-fashioned traders such as Paul Tudor Jones and Steve Cohen are adding to their computer scientist ranks in hopes of boosting returns.

…

The goal of quant trading is similar: to build models that find signals hidden in the noise of the markets. Often they’re just whispers, yet they’ll help predict how the price of a stock or a bond or a barrel of oil might move. The problem is complex. Price movements depend on fundamentals and flows and the sometimes irrational behavior of people who are doing the buying and selling.

THE QUANTS ARE TAKING OVER WALL STREET FORBES

Paul Tudor Jones helped define the rich hedge fund industry by placing big contrarian trades that made him a billionaire, like anticipating the 1987 stock market crash. But in 2001 he quietly made one of his best trades ever as the key investor backing the launch of a data-driven quantitative hedge fund called Two Sigma Investments. Jones knew one of Two Sigma’s co-founders, David Siegel, who had briefly worked for Jones’ Tudor Investment Corp. Tudor even provided the s ta r t -up space for Two S igma at Manhattan’s One Liberty Plaza.

Today, Two Sigma is the fastest growing hedge fund on Wall Street. It manages some $35 billion and made its two founders, Siegel and John Overdeck, billionaires themselves. Quant funds like

those managed by Two Sigma and Renaissance Technologies have been consistently posting solid returns in recent years while most other hedge fund strategies centered around the trading decisions of human beings have struggled mightily.

The most successful hedge fund launch of recent years has been PDT Partners, the quantitative firm run by Pete Muller, a quantitative specialist who is well-respected by Jones. Last year, Siegel went to an investment conference and said, “eventually the time will come that no human investment manager will be able to beat the computer.”

The godfather - Ed Thorp

In 1962, Ed Thorp became every gambler's favorite mathematician when he published the first mathematically proven method for beating the dealer at blackjack.

Thorp's work revolutionized the game. But he went further: In 1967, Thorp devised a system that uses math and computers to predict the future of the stock market. His hedge funds and his personal portfolio have been profitable ever since.

Thorp and the people who use such systems have come to be known as "quants" — it's a reference to the quantitative-analysis techniques they employ.

Ed Thorp’s Princeton Newport Partners fund, set up in 1969, is recognised as the

first quant hedge fund (one that uses algorithms). Over 18 years it turned $1.4m into $273m, compounding at more than double the rate of the S&P 500 without suffering so much as one quarter with a loss. Thorp’s then revolutionary use of mathematics, options-pricing and computers gave him a huge advantage.

His escapades, as well as his moneymaking abilities, have made Thorp the “godfather” of many of today’s greatest investors. Warren Buffett, a bridge partner, advised investors in his first hedge fund to move to Thorp; Bill Gross, who founded Pimco and built it into the world’s largest bond fund manager, spent time at blackjack tables, then set up as an investor using Thorp’s 1967 book Beat the Market; Ken Griffin, now the world’s highest-paid hedge fund manager, set up his fund Citadel in 1990 using documents that Thorp gave him from Princeton Newport.

QUANTITATIVE INVESTING THE HISTORY OF WALL STREET WINNERS

QUANTITATIVE INVESTING - science-driven asset management strategies. Quantitative strategy is composed of thousands and dozed of thousands trading algorithms. Algorithms are created by math researchers (mainly PhDs). Algorithms are built on laws of mathematics: the law of large numbers, probability theory, statistics, etc.

The books that started it all

✦ 1 9 0 0 - « T h e o r y o fSpeculation» (dissertation) - LouisBachelier

✦ 1951 - «Non Cooperative Games(dissertation, the Game Theory) - JohnNash (Nobel Prize in EconomicSciences, Abel Prize)

✦ 1964 - «The Random Character ofStock Market Prices» - Paul Cootner

✦ 1967 - «Beat the Matket»1977 - «Elementary Probability» - EdThorp

ED THORP «BEAT THE MARKET»

DISCLAIMER

Information and opinions contained in this presentation are gathered and derived from sources which we believe to be reliable. However, we can offer no undertaking, representation or guarantee, either expressly or implicitly, as to the reliability, completeness or correctness of these sources and the information provided.

Interested investors are strongly advised to consult with their Investment Adviser prior to taking any investment decision on the basis of the information and opinion herein and in order to discuss and take into account their investment goals, financial situation, individual needs and constraints, risk profile and other criteria.

Information and opinions contained in this presentation shall not be construed as an offer, recommendation or solicitation to acquire or dispose of any investment instrument or to engage in any transaction.

The investment strategy expressed in this website and related documents are the opinions of Formiga team.

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