On the eve of the June Fed meeting the deflationary pressures in the global economy are growing. There are three things the Fed must begin to realize:
On Monday, June 13th we bought $Yen at 106.40. Currently, we are long US 10yr notes and $Yen and short the S&P and German Dax and are 100% invested.
In 2012 modeled performance (7 ˝ mo.) net of all fees was +12.46% with a 10% Hurdle rate
In 2013, modeled performance net of all fees was +19.73% with a 10% Hurdle rate
In 2014, modeled performance net of all fees was +56.42% with a 10% Hurdle rate
In 2015, modeled performance net of all fees is +72.68% with an 8% Hurdle rate
In 2016, modeled performance net of all fees is +41.89% with a Graduated 10% Hurdle Rate
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Disclaimer:
The Unicorn Macro Fund, LP (“Fund”) operates under the SEC rules of 506(c) of Regulation D. This rule allows general solicitation as long as all purchasers of the Fund are accredited investors and the Fund takes reasonable steps to verify that purchasers are accredited investors. The 506(c) rule benefits funds that perform better than their peers, because for the first time, Regulation D funds can post their results publicly.
The Fund trades both long and short positions in a variety of global markets and its performance is not correlated to any one market. Performance of the model of the Fund is measured by Net Asset Value (NAV) which is net of all fees, is unaudited, and may include the use of estimates. Individual results will vary based on the timing of an investment and past performance is no guarantee of future results and there is a possibility of loss.
The modeled results are based only on capital appreciation from macro style trades. The results do not include dividend reinvestment or any other form of cash flow and are taxed as ordinary income. All trades have a risk/reward objective of at least 3 to 1 and each full position risks no more than 2% of assets. There will be times when market conditions may alter these objectives. Since the inception of the model our trading of the methodology has become more precise.