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Weekly Leveraged ETF News 7

August 30th, 2009 Kevin Comments off
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Historical Data Randomization Using the Frequency Domain (Preview)

August 24th, 2009 Kevin 2 comments

Identifying a Strategy’s Risks

Recently I decided to try to stress test one of my own personal strategies (it is a 100% mechanical mean reversion strategy). I wanted to see how this strategy might perform in a variety of market conditions, such as a random but similar market crash of 2008. Ultimately, I wanted control over randomizing historical data such that I can perform tests against infinite sets of data such as:

1.  Crashes similar to 2008
2.  Crashes 5% worse than 2008
3.  Crashes 10% worse than 2008 (etc.)
4.  Bull markets
5.  Bear markets
6.  Sideways markets
7.  Volatile and nonvolatile

Unfortunately, historical ETF data only goes back about a decade or so. Hence the data is somewhat limited for finding a variety of such scenarios. In some discussions with a friend (PhD in electronics), it was brought up that I should take a look at performing randomization in the frequency domain rather than the time domain. Since we are both more familiar with digital signal processing rather than statistics, it was only natural for us. I will explain the details of the technique in the next post covering this research.

Charting the Randomized Data

I was not expecting much from the results, but a few tests and tweaks gave me data that was exactly what I was looking for. Here are some demonstrations. (green is original data, red is a random data set)

Crash similar/worse than 2008


Crash much worse than 2008


Strong bull market (followed by a severe crash)


Volatile market

 

 

Performing frequency modifications gives control over how the data is manipulated. By adding randomness to the low frequencies, steep bear and bull markets can be created. By adding randomness to high frequencies, volatile markets can be created. It is much like how an equalizer for a stereo system controls the amount of bass (bull/bear markets) and treble (volatility).

Purpose of Frequency Domain Randomization

The reason I am interested in this research is not to optimize a strategy against a variety of random historical data sets, but rather to identify risks. For example, my personal strategy has a weakness of fully scaling in too early during strongly trending markets. By changing some parameters to my strategy, I was able to not only maintain the original performance on the original data set, but also significantly reduce drawdowns during significant trends in the random data sets (like a 2008 bear market that drops by 75%).

I will provide more details of the research as it develops.  If the technique proves useful, I can add it to QLeverageSim or just create a standalone utility for generating the random data.

Weekly Leveraged ETF News 6

August 23rd, 2009 Kevin Comments off

8/21/2009 – Wells Fargo Advisors Alters Leveraged ETF Sales Policy (CNNMoney.com)
8/20/2009 – Regulatory Storm Brews for ETFs (TheStreet.com)
8/20/2009 – Clearing Up Misconceptions About Leveraged ETFs (Seeking Alpha)
8/19/2009 – Interest in Leveraged ETFs Waning? (Seeking Alpha)
8/18/2009 – A Leveraged ETF Warning Again. Yawn (TheStreet.com)
8/18/2009 – SEC Warns Investors on Leveraged ETF Holdings (CNBC)

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Leveraged ETF Myths 1 – Shorting Both Bull and Bear

August 17th, 2009 Kevin 2 comments

The Myth

In light of the incredible decay seen in leveraged ETFs in the past year, many have suggested shorting both the bull and bear leveraged ETF to profit from the decay (such as FAS/FAZ). The belief is that over enough time, both ETFs will either decay toward 0 or do reverse splits. Hence, the myth is that the pair shorting strategy is a ‘can’t lose’ strategy and a one way ticket to profits.

The Flawed Assumption

Unfortunately, this myth is based on the assumption that leveraged ETFs always decay more than they grow over long periods. In the period from October 2008 to March 2009, this was most certainly true as the markets saw such incredible volatility that caused leveraged ETFs to decay up to 20 to 40 times more than normal. This period has ‘colored’ people’s thinking, making them believe that leveraged ETFs will always behave this way and that any long term investor will most certainly lose money. The belief that leveraged ETFs always decay more than they can grow is simply not true. All it takes is some simple simulations to prove otherwise. Simulations show that there periods where the decay is greater than the growth and that there are also periods where the growth is greater than the decay.

The Risks

Like any other strategy, shorting both ETF has its risks. In order for it to work, it needs certain conditions to be met. Just like ‘going long’ requires the market to go up in order to profit, the paired shorting strategy requires the market to be horizontal or exceptionally volatile in order to profit from the decay. When these conditions are not met, the shorting technique can result in significant losses.

Example #1 – A Recent Trend

An excellent example that has probably blown out anyone recently attempting the ’short both’ strategy is the recent gains of many leveraged ETFs since the March bottom. Case in point, TNA:

 

A 240% increase implies a massive loss for anyone holding a short through this period. Even though there was 15% decay during this period, the trend was too strong. A chart showing the paired shorting profit through this period is shown below.

 

At one point the strategy is down 98%. This is certainly damaging to one’s confidence in this strategy.

Example #2 – A Bull Market

Advocates may say that the previous example did not give the strategy enough time to work itself out. Well, let’s run a simulation to see how it holds up in a bull market. By applying a 3x multiple on IWM’s data from 2003 to 2007, we can get a simulated version of both TNA and TZA during the last bull market. Running the strategy yields the following results.

 

A 400% loss is definitely not a one way ticket to profits.

Example #3 – S&P 500 (1950 – 2009)

We can take this strategy to the extreme by seeing how it would work over the course of almost 50 years of S&P 500 data. Again, a 3x multiple is applied to simulate a 3x bull and 3x bear ETF (such as UPRO and SPXU). Unfortunately, running the strategy over this timeframe results in such a drastic loss, plotting a chart shows astronomically high losses that make the chart difficult to read. Only a partial chart is displayed to make it somewhat readable.

 

Coming in at over a 100,000% loss, this strategy clearly did not work over this long period.

Myth Result: Busted

While there are periods where the pair shorting strategy works (like volatile bear markets), there is ample data that proves it fails to work during a multitude of conditions and timeframes. Hence, this myth is busted. For more coverage describing the challenges of this strategy, read this:

Why your brilliant plan to short a pair of 3x ETFS will not work.

Articles describing the pair shorting strategy:

Triple Leveraged Arbitrage
A Winning 2X and 3X ETF Long Term Strategy
The Equal Short Bull-Bear As The Ultimate Negative Correlator?

Shorting 3x levered bull and bear ETFs: Possibly a very cool strategy
Shorting Leveraged ETFs – Low Risk High Gain Potential?

Disclaimer: Results do not take into account any borrowing costs, transaction costs, or leveraged ETF costs.

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Weekly Leveraged ETF News 5

August 16th, 2009 Kevin Comments off

8/14/2009 – 3 Big Changes ETF Investors Have Been Making (Seeking Alpha)
8/13/2009 – Leveraged ETFs Might Go to Court (planadvisor)
8/13/2009 – SRS: The Leveraged ETF Witch Hunt Continues (Seeking Alpha)
8/13/2009 – Crucifying Inverse Leveraged ETFs: Why Now? (Seeking Alpha) [Top Pick]

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