The goal of active management is deliver better risk-adjusted returns than passive investing.
The model isn't a crystal ball. Trade signals are based on Probabilities and Statistical methods, producing different trades with differing probability of success. Overall, model is right 2 out of 3 trades.
The model is not a HFT or intraday scalping algorithm, only changing the position about 2-3x a month, on average. The model is a pure directional bet on whether the SP500 will rise or fall, which during bull or bear markets, is able to profit from not only the major trend but also sell into the minor counter-trends. The result is a system designed to beat the SP index by profiting when the market loses during pullbacks.
One could create a hedged strategy where Shorting SP is utilized, which makes profits possible during a market decline. This hedge fund approach is best combined with another long-only portfolio. This Long/Short 100% version of the model is employed by the firm's commodity pool.
Another option is to supplement the long-only strategy, using the model to change equity weighting allocations, making better timed buys and sells. Scaling between 0% long to 200% long at MAX BULL. Selling means taking profits, not shorting the market.
The graph shows 1999-2012 backtesting results of the Long-Only 200% model theory as compared to passive index investing in the SP500.
Underweight SPX Trading Fund, LP
LONG/SHORT ES FUTURES
MAX Drawdown -6.7%
completes 2 years Nov 2017
Hedges Long-only strategies
First 12 month period, fund was above backtested average return of 8% net of fees, as notated in the green box on the graph. In December 2015, Macro Fair Value had started to roll over, and the fund was positioned correctly short ES futures. With only one failed hedge trade in March, the fund had strong monthly profits trading the long side, including up to Election night.
Fund then faded the Trump Election rally to suffer its first multi-month drawdown, as noted in red box on the graph. SP500 Price following the Election had spiked too far ahead of Macro Fair Value, and it took a quarter before Macro caught up to price. After this lag, Macro Fair Value led Price again for most of the rest of 2017.
Since drawdown, Macro Fair Value has mostly led SP500 price, supporting the bull trend. As noted in the blue box, the fund has started to get back to historical win rate with 2 wins for every 1 loss. Lack of volatility in 2017, however, didn't allow the fund's scaling strategy to add to position sizes, resulting in less than average monthly returns. None of the hedging short trades worked in 2017.
A significant strategic advantage of the fund is identifying which SPX prices have the best odds of a defined short-term low during a market decline. The fund favors high cash balances, only scaling to full size when price falls to this MAX BULL support level.
During market volatility, the fund can add short-term profits, sometimes significant. These MAX BULL signals occur during deep discounts between Macro Fair Value price and the actual SP500 price, and often coincide with extremely negative sentiment indicators. Normally these trades only last a few days - a panic selloff that reverses as price bounces back to an improving Macro Fair Value.
These trades allow the fund to use its cash reserves for high risk opportunities, but limiting them to extreme, pre-defined price points. This scaling strategy allows the fund to have less than 50% exposure ("underweight SPX") to the market, on average.
What is a model?
A simple example of a market model is comparing today's price to the historical average of price over, for example, the 50 past days. The rule would be to BUY when today's price is above the past average, and SELL if below. This rule could be easily backtested and other rules could be applied to produce win/loss ratio and profit/loss delta. Other rules could be applied and tested, but not so many that the rules only work for the test data. Known as curve-fitting, too many of today’s rules are created to fit just one set of past data, which will not work for new data. In simple terms, the rules were so stringent that they could only work once.
The next method for a model is to forward test, which measures the results against actual forecasts. The goal is to gather real-time trades, capture their results, and compare against the benchmark backtest. Since 2013, the results were consistent with the historical testing, falling within test parameters including win/loss ratio, average drawdown, average ROR, as measured in 3 mo, 6 mo, 9 mo, and 12 mo periods. The forward test is key in determining if the backtest was uniquely unrepeatable or whether the methods applied will continue to work.
By 2012, Michael’s confidence in his own MarketModel led him to establish a futures account and commit to trading the model signals, testing different position sizes and sharing trades on Twitter. Michael's trading diary moved to private subscription service in 2013-2014 via protected Twitter, where the nightly model signals were posted, along with trading results. After 3 years of successful model performance in real-time, a group of members joined Michael in investing in Underweight SPX Trading Fund, LP to trade the model in a commodity pool.
The model uses SP500 futures instead of the SPX cash because of the use of leverage provided by futures. Use of leverage is typically associated with high risk, however in our case, we are simply choosing to not to tie up $1M is cash buying and selling shares of the SPX. Leverage allows for the scaling from underweight SP500 to up to 1x leveraged (100% long or short).
The model's use of futures allows for scaling into very large account balances. For example, a $1B account could invest alongside the model within the SP futures market easily within the avg volume and open interest of today's market. The investment decisions are almost entirely systematic, based on the model's signals.
Long/Short Hedge Fund: Underweight SPX Trading Fund, LP
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