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Risk Management

Market Reversal In 2014 And How Alpha Theory Helps Part 2

Following up from the previous article, Cameron Hight and Benn Dunn discuss and demonstrate how Alpha Theory helps with market reversal.

This article was co-written by Benn Dunn, President of Alpha Theory Advisors, and Cameron Hight, CEO of Alpha Theory.

After our last post about the fast market reversals in March and April and Alpha Theory’s palliative help, we were curious if we could create a proof of Alpha Theory’s impact. Below is that proof!

As in our previous post, we began by choosing the Morgan Stanley New Tech and Old Tech Baskets as a proxy for the market run-up and unwind that most impacted hedge funds. To simulate an Alpha Theory vs non-Alpha Theory portfolio, we bought and sold equal amounts of New Tech and Old Tech respectively on April 1st, 2013. Through April 28th, 2014, the New Tech basket is up 57% and the Old Tech basket is up 29%. Through February 2014, the New Tech basket dramatically outperformed the Old Tech basket and that trend reversed through the end of April (see the -28% drop in the chart below).


The Simulation – Trial #1

To simulate Alpha Theory and non-Alpha Theory portfolios, we invested half of the portfolio in New Tech and shorted and equal amount of Old Tech. At time 0, both funds were 100% Gross / 0% Net. For both funds, we instituted a max position size of 50% and trimmed if the position grew above 50%. To simulate a typical portfolio, we ran a Buy & Hold strategy. Alpha Theory position sizing is driven by intrinsic value, so to simulate Alpha Theory, we set Price Targets for both New and Old Tech that would keep us from shorting New Tech and going long Old Tech over the course of the year. That equated to $55 for New Tech (was trading at $23.85) and $15 for Old Tech ($28.08). This represents a 130% and -47% return, respectively (forecasting 130% price appreciation seems extreme, but the buy and hold had to have a price target above the peak value of $50.42 to maintain full exposure). After running the simulation, we found that Alpha Theory dramatically outperformed Buy & Hold on just about every metric except Peak Return (see below). The advantage that Buy & Hold showed for Peak Returns comes at a crippling cost of very large drawdowns.

The Results – Trial #1

Alpha Theory returns were 16.2% vs. 12.9% for Buy & Hold with a lower volatility. Peak Return was lower than for Buy & Hold but being fully exposed at the peak caused the Max Drawdown to be significantly higher for Buy & Hold. The 18.7% drawdown is the pain that many investors are feeling in their portfolios currently. Trimming into the face of a massive run-up is incredibly difficult because every psychological indicator is telling you to hold on, but trimming assets as they approach intrinsic value reduces volatility, drawdowns, and, if there is a reversal, improves returns.

How quickly one sells in the face of the run-up is an important variable. In the Alpha Theory portfolio, we’ve assumed a minimum return of 0% and a 45% return to hold a full 50% position (45% is a pretty typical return hurdle for Alpha Theory clients). What that means, is when the expected return between the current New Tech basket and our price target of $55 dropped below 45%, we started to sell and when it dropped below 0%, we were out completely (see the exposure comparison below – Alpha Theory gross drops to 60%).

You can see Alpha Theory’s exposure oscillation caused by expected return fluctuation and decay as the New Tech basket approaches its target versus the relatively flat line associated with Buy & Hold (The Old Tech basket isn’t really a factor because Alpha Theory and Buy & Hold end up with similar exposures throughout the experiment).

After looking at the data, we realized that our price target assumption of $55 (lowest price target that would allow Buy & Hold to maintain a full position in New Tech) had a large bearing on returns, standard deviation, etc. We decided to perform a sensitivity analysis on price targets assuming a range of $45 to $75. Again, our assumption is that Buy & Hold must have an intrinsic value of at least $55 for New Tech to hold on until it hit $50.42 on March 5th which creates a price target floor somewhere near $55. For conservatism, we show a range going down to $45. On the upper end, we use $75. At $75 and above, Alpha Theory results do not change because Alpha Theory essentially becomes Buy & Hold (maintains 50% exposure the entire time).

The resulting analysis was the most compelling result. With price targets of $62 and below, Alpha Theory dramatically outperforms on an ROIC and Volatility basis and outperforms on returns except for $47 and below (lower returns, but with lower exposure and volatility). And with price targets of $62 and above, Alpha Theory basically became Buy & Hold. THE NET RESULT IS THAT, BASED ON YOUR PRICE TARGET, YOU WOULD HAVE EITHER DRAMATICALLY OUTPERFORMED OR BEEN ROUGHLY IN LINE. This is the essence of positive asymmetric returns.

Here is the same data in a tabular format with a few more metrics. You can see that over the entire spectrum (averages are based on the entire price series, not just the 7 points below), Alpha Theory significantly outperforms in terms of return, volatility, max drawdown, and Return on Invested Capital.

The Simulation – Trial #2

While running this analysis, we were surprised to see that Alpha Theory was slightly underperforming Buy & Hold at all at the higher price targets. We realized that this was related to the mechanics of the Buy & Hold strategy we assumed. Our mechanism was to never allow the position to go above 50%, when in reality, the Buy & Hold strategy would have purchased a set number of shares at the beginning and that position would have risen in value and then fallen in value. We suspected that the total return for Buy & Hold using a fixed number of shares should be much higher and, after running the analysis, we found out it was. But at the expense of dramatically higher volatility and drawdown. See the same three charts from above, but with an allowance for max position to grow beyond 50% (max gross for Buy & Hold rises to 124% - Alpha Theory model is not changed).

The Results – Trial #2

The return rises, but the volatility skyrockets (from 11.5% to 16.1%) and causes the Sharpe to dip below 1.0. Even more alarming is the Max Drawdown of 29.5%! Note that Alpha Theory has slightly better overall returns and substantially better volatility, drawdown, and ROIC measures. Notice the positive asymmetry persists in Trial #2, the return is 0.5% better on average. The highest returns are associated with price targets in the $50-$60 range which isn’t surprising given that they are closest to the ultimate high of $50.42, but the return is only a piece of the equation. The ROIC is superior in most all cases and the Volatility is 4.5% better at its smallest gap. Finally, the Max Drawdown is better in every single instance by a wide margin.

The Conclusion

The Alpha Theory impact is profound and validates trading around intrinsic value. We recognize that our analysis is assumption laden, but we tried to stress and honestly assess the comparison. Alpha Theory benefited from volatility on the way up and down, as price fluctuations allowed for alpha generating trades along the way. If the track would have been straight up, straight down, Alpha Theory’s returns would have been reduced. But we’ve never seen a market that goes straight anywhere.

We also recognize that the ideal strategy would have been to hold a full position until the peak and unload. We suspect that there were some investors that did just that, but market timing is not a repeatable process given the unpredictable pendulum swings around intrinsic value. The only thing a smart investor can do is reduce the exposure to low expected return assets and increase their exposure to high expected return assets. In doing so, they will create a return stream with lower highs and higher lows, lower volatility, and improved returns…the Alpha Theory way.

Risk Management