Merricks Capital Systematic Commodity Strategy
The Evolution As an evolution of the existing fundamental discretionary trading strategy, the Merricks Capital > Systematic Commodity Strategy provides a more targeted access to the risk premia associated with commodities than a traditional discretionary commodity fund Merricks Capital Soft Commodities Fund has evolved it’s process from a pure discretionary > fundamental investment strategy to a fundamental investment approach that uses a systematic process of filtering, selecting and timing of commodity trades This strategy is superior to the traditional discretionary commodity trading fund for the following reasons: > Generates a broader opportunity set > Has a higher hit rate > Scalable > Weekly Liquidity > More accurately positions for asymmetrical payoff > Enhanced entry and exit timing > Defined hard stop loss on every position > Lower cost > 1
Introduction Merricks Capital Systematic Commodity Strategy provides a more targeted access to the risk premia > associated with commodities than a directional approach traditionally used Merricks Capital has identified the major market drivers of it’s investment universe within defined market > conditions The strategy uses a systematic process using these drivers and defined market conditions to determine > the timing of the application of risk premia to markets. These market elements include: Fund positioning, open interest, volume > Implied market risk premia (volatility, skew and curve shape) > Percentile historical ranking of current pricing > Seasonality (timing of high/low supply, high/low demand and weather events) > Systematically Apply systematic Determine the overlay existing Apply fundamental process to test and Define Universe fundamental market conditions to trade selection refine potential trade catalyst (the drivers) provide entry and process opportunities exit timing Merricks fundamental understanding of why and when this risk premia should exist is a key edge to designing the systematic strategy for the Fund to efficiently capture the risk premia 2
Fundamental Price Drivers MERRICKS HAS IDENTIFIED TWO MAJOR FUNDAMENTAL DRIVERS OF MARKET PRICE ACTION The combination of these independent drivers creates a successful process of selection, timing and sizing of profitable commodity trades SEASONALITY > Due to the 6 month growing cycle of agricultural commodities, commodity markets display identifiable seasonal trends based on definable fundamental factors such as crop cycles, weather risk periods and demand patterns. Understanding these fundamental factors and having a repeatable systematic process of filtering, defined entry and exit points, selection and sizing of trades is the key to profitably exploiting these opportunities The seasonality driver generates trading signals by identifying seasonal trends in outright commodities and > spreads and applying a systematic approach to refine the fundamental opportunity set Aligning the portfolio composition with seasonal trends, by investing during periods with a high probability of risk > events, the strategy is able to participate in fundamental events (e.g weather) which provides the portfolio with an asymmetrical payoff where gains are disproportionate to losses POSITIONING Commodity markets display a strong tendency to react to changes in speculative positioning > The positioning driver uses a systematic process to predict price action using CFTC reported speculative > positioning data, volume and open interest. This process identifies crowdedness of market participants and the timing and velocity of money in and out of markets By aligning the portfolio with money flow, the strategy captures short covering events, aligns the portfolio with > market momentum and predicts the timing of market trend changes 3
Seasonality Driver OBJECTIVE AND METHODOLOGY A proprietary program has been built to refine the fundamental assessment of seasonal > trends The following parameters are defined and observed by the program: > Hit Rating – number of years with positive return, >80% forms a trend > Average Return – average return of the trend in the past 10 years > Volatility – volatility of the daily returns in the past 10 years > Sharpe Ratio – taking into account average returns, volatility and duration > Upside downside Ratio – max cumulated gains over max cumulated loss > Each trade is sized according to historical volatility, historical payoff and hit rating A stop loss is applied at -5% on each individual trade to enhance payoff > > As with all sustainable systematic trades, the key to success is having a fundamental prior or expectation of why each trade should work and a fundamental trade selection process to determine which trades are added to the portfolio 4
Seasonality SEASONALITY EXAMPLE – SUPPLY FACTOR Long April MDEX Crude Palm Oil in USD Prior expectation: Palm production is at its low point for the year and stocks are tightest > Trade dates : 29 Jan to 19 Feb > > Back testing Period : 2006 - 2015 Hit Rating : 100% (10/10 years) > Average Return : 5.7% > Sharpe Ratio : 3.5 > Holding Period : 21 Days > 5
Positioning Driver OBJECTIVE AND METHODOLOGY Merricks has developed a systematic process that uses CFTC positioning data, market volume > and open interest to predict price action When market participant expectations become aligned, speculative positioning becomes crowded. > This results in a disproportionate upside/downside ratio. Positioning the portfolio in the right way, in the right market conditions ensures that the portfolio will exploit a change in market sentiment producing an asymmetrical payoff Absolute change and the rate of change of speculative positioning, volume and open interest > provide signals that identify momentum as well as the beginning and end of trends System conditions are based on weekly changes to CFTC non-commercial positioning, changes > in open interest and volume and overall net positioning of non-commercials The following filters are combined to determine the weekly results: > Winning Percentage > Annualized Return > Max Drawdown > Sharpe Ratio – taking into account returns and volatility > Each trade is sized on a score based conviction rating > Portfolio Manager discretion is used to eliminate trades that do not meet the fundamental > selection hurdle 6
Positioning POSITIONING EXAMPLE 2016 trades for buying Soymeal on Positive Change Prior expectations: Soymeal shorts were crowded with record net short non-commercial > positioning on ideal weather condition in South American despite the crop not yet harvested. Flooding in Argentina triggered a massive short covering rally. Number of Trades : 15 > Winning Percentage: 67% > Annualized Return: 7.6% > Sharpe Ratio: 1.56 > > Max Drawdown: -1.6% 7
Performance SYSTEMATIC COMMODITY STRATEGY PERFORMANCE Average Net Average Gross Peak to Days to Year Returns Annualised Vol Sharpe Sortino Exposure Exposure Trough Recover 2014 31.89% -14.5% 254.7% 9.4% 3.37 6.05 -4.45% 7 2015 26.34% -7.0% 246.0% 9.4% 2.80 5.37 -4.56% 46 2016 24.73% 43.2% 263.7% 13.7% 1.78 3.63 -6.98% 17 2017 (YTD) 4.20% 18.0% 232.9% 6.2% 10.27 16.57 -2.81% 21 * Performance numbers include management fees and administrative cost *Backtest for 2014 is compiled using training data from 2004-2013 *Backtest for 2015 is compiled using training data from 2005-2014 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total 2014 1.4% -0.5% -1.7% 0.9% 6.5% -0.3% 3.4% 4.0% 1.4% 7.6% 5.9% 3.3% 31.9% 2015 7.0% 1.1% -1.4% -1.8% 1.6% 5.2% 1.8% -0.2% 2.5% 3.8% 3.3% 3.3% 26.3% 2016 3.8% -4.1% 2.6% 11.0% 10.0% 0.3% -3.1% -0.7% -1.5% -0.1% 4.9% 1.5% 24.7% 2017 -1.0% -0.0% 3.6% 0.6% 1.0% - - - - - - - 4.2% * Performance numbers include management fees and administrative cost 8
Performance PERFORMANCE METRICS 9
Investment Universe INVESTMENT UNIVERSE Commodity Exchange Currency Soft Red Winter Wheat CBOT USD Grains, oilseeds, vegetable oils, > Hard Red Winter Wheat KCBT USD dairy, sugar, cotton, energy & livestock Hard Red Spring Wheat MGEX USD Milling Wheat MATIF EUR Feed Wheat LIFFE GBP INVESTABLE SECTORS Corn CBOT/SAFEX USD/ZAR Trading futures > Soybean CBOT USD Soybean Oil CBOT USD Soybean Meal CBOT USD Canola WCE CAD SPECIALISTS AND ADVANTAGE Rapeseed MATIF EUR Traders have worked for the large > Crude Palm Oil MDEX MYR food companies in Australia Sugar NYB USD White Sugar LIFFE USD Vast industry contacts and > Cotton NYB USD experience across the world Heating Oil NYMEX USD RBOB Gasoline NYMEX USD Regular travel to view farms, > Crude Oil NYMEX/ICE USD bulk handler operations and Ethanol CBOT USD meetings with all market participants Gas Oil ICE USD Livestock CME USD 10
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