ETF Performance Analysis for S&P -2.0% Day -- April 27, 2010

Apr 28, 2010 in S&P 500 | Screener | Volatility

Volatility spiked on April 27th, lets take a closer look.

First note that while the S&P 500 dropped -2.0%, the higher volatility ETF's are in steeper drawdowns:

What performed well? The usual suspects --- those with negative correlations to equities, including the VIX ETF's, the inverse ETFs from ProShares, long duration US treasuries, precious metals, the U.S. Dollar and the Japanese Yen.


The last chart goes back to focus on U.S. Sector SPDR ETF's. Note again that those with higher volatilities led to largest drawdowns.

Follow us on Twitter:


Simple Strategy Using The Relative Strength Themes of our 'Screener'

Apr 26, 2010 in Screener | Strategy

While it may not be particularly interesting from a global perspective -- the relative strength model in our Screener has been quite clear for past few months. Long US Equity and Negative on Europe. Here is an example using the relative strength model as of the last day of February to create a subsequent long-short portfolio that is representative of the broad themes in the marketplace:

Follow us on Twitter:


New Relative Strength Backtest 'App' Added To Site. Check it out.

Apr 26, 2010 in Backtest | Relative Strength

We have added an innovative new relative strength backtest application to the site.

Click Here: ETF Relative Strength Backtest App



For a video tutorial on this 'app' click here: Relative Strength Tutorial"

Follow us on Twitter:


Credit Spreads and S&P 500 Review Chart

Apr 24, 2010 in Credit Spreads | S&P 500

A look at corporate bonds vs treasuries with S&P 500 overlay.

Note that this relationship can only be viewed if your database calculates total return (re-calculating the data series to adjust for dividends and distributions). No other websites do this but


Follow us on Twitter:


The Higher The Volatility, The Higher The Drawdowns

Apr 22, 2010 in Volatility | Drawdown


Lately I have read in a few places that "investors too often equate risk with volatility." The people who say these kinds of things rarely go on to present an argument based in statistical fact. This blog post is not to say anything is absolute --- but I will show some simple recent data that hardly refutes the statement put forth on the first page of Chapter 3 in ‘the bible’ of quantitative finance ‘Active Portfolio Management’ (Grinold & Kahn, 1999): – it could not be much clearer: “Risk is the standard deviation of return.”

Below is data from the past bear market for 5 of the largest ETF’s in the world. I have chosen to use the standard deviation of the period PRIOR to 2008, Q4 2007. I then show the subsequent drawdown in 2008. Note how in each case of higher standard deviation, the drawdown was larger in the NEXT period.


While the above is just a sample --- I can show this over many, many more ETF's. Thinking about your portfolio from the viewpoint of standard deviation can help you understand at least in some small way about how your portfolio might drawdown relative to some common benchmarks. This chart shows volatilities across these same 5 ETF's over time. Note that each ETF has held its relative position for the past 3 years -- zero change. While you cannot know with precision what the future holds -- you can to some extent understand your relative drawdown given S&P volatility of XX.





Follow us on Twitter:


Video: Find Relative Strength Ideas -- Then Integrate Ideas Into A Portfolio

Apr 20, 2010 in Backtest | Screener | Video


4 minute video going over a few recent examples:



Follow us on Twitter:


2010 Regional Equity Themes

Apr 20, 2010 in Regions

Starting late 2009 and re-inforcing in early 2010: Emerging markets have just tracked the world index --- while US equities have enjoyed large money flows IN and European equities have seen large money flows OUT. Note that the structure of our ETF screener demands stronger relative strength if volatility is higher. Thus, given identical relative strength across 2 timeframes, it will favor the lower volatility ETF. US equities continue to enjoy the potent combination of high relative strength and low relative volatility and this continues to be the primary theme in the marketplace.


Follow us on Twitter:


Moving Average Backtest Page Added To Site, Have A Look

Apr 13, 2010 in Backtest | moving average

New application added. This simple application generates a report for any ETF in our database based on the user-defined rules regarding a moving average cross.



Moving Average Backtest Page

Comments and feedback appreciated.



Hedge Funds and ETF's

Apr 12, 2010 in Hedge Funds | Volatility

In the world of hedge funds, it is standard practice to list a ‘volatility target’ within a presentation to potential investors. You never see this listed in any mutual fund or investment advisor type of presentation. Nevertheless, whether it’s in the presentation or not, it’s a topic that is important to post about as often as possible.

First, volatility is a good way to think about ‘drawdown potential.’ High volatility means that the high to low intermediate moves are likely to be large – and since you can never be quite sure what the future will bring, you should generally avoid the highest volatility ETFs unless you feel especially confident in a high return expectation. The relationship between your general return expectation and the underlying volatility of the ETF is an important one.

What hedge funds state in their presentations is a ‘volatility range’ to expect – that is, what the hedge fund manager believes their strategy equates to in a bottom-line percentage, always stated as an annual figure. Many presentations then try to target a ratio of returns relative to that volatility figure. Two such examples are listed here:


Target: <15% Volatility with >15% Return



Target: 8-12% Volatility with 1.5x+ Return




Both of the above examples from actual hedge-fund marketing books are stated within the same structure: namely, the Sharpe Ratio. Return and volatility of return are both used as quantitative targets.

How does this apply to ETF’s? Each ETF has its own same characteristics. Viewing total return and annualized volatility for each ETF is a nice breakdown of the major components of the Sharpe Ratio. Moreover, you can COMBINE ETF's into portfolios that suit your own risk tolerance.




One major issue for all investors is that volatility is not static. Large changes in market volatility complicate the discussion of absolute targets of volatility. But what we can observe from actual experience is that RELATIVE volatility across different types of ETFs have been quite consistent. Below is an example of a few different types of ETFs. You can see that while the LEVEL has swung around significantly, if we were to rank each of these ETF’s – you would see that each ETF has maintained its exact ranking for every period for the last few years. Emerging markets have maintained high relative volatility vs the S&P 500, which has been more volatile than the defensive Consumer Staples Sector SPDR which in turn has been more volatile than the aggregate bond market.



What this says is that the risk of drawdown among these different ETF’s is skewed. We cannot precisely say what kind of risk there is --- but we can think that if we entered a long position in bonds and mis-timed the entry, the punishment for being wrong would not be as great as if we did the same thing in Emerging Markets.

It is professional to think in terms of volatility and risk-adjusted returns. But you do not need to be a ‘long-short’ hedge fund manager to maintain an efficient (risk-adjusted) portfolio. Overall portfolio volatility can be diluted through exposure to shorter-term fixed income. Indeed, rotating between high returning ETF segments (high relative strength) and low-volatility investments is a strategy that generally leads to the same place hedge funds are ultimately targeting: a high Sharpe Ratio.

Note: The Sharpe Ratio measures reward per unit of risk. It is calculated as the annualized average daily excess return divided by the standard deviation of daily excess return.




Global Asset Class Rotation

Mar 30, 2010 in Backtest | Relative Strength | Screener

This is to highlight some specific thoughts on the important topic of global money flows and its implications for investors.   ETF’s have caused a major shift as they allow cheap ways to access new markets.  It is our belief that this innovation will cause the discussion to increasingly become ‘which MARKETS should I own?’ – and less about ‘which stocks should I own?’

Below are some performances of a few of the major ETFs over the past three years.  I am starting at a very high level here and then working towards my ultimate point of coming up with a process for interpreting global money flows.  (Note that all returns posted here make the proper dividend and distribution adjustments as total return is absolutely essential in any professional discussion of performance)

The point of this slide is to simply highlight that even if trading long-only, a well-executed high-level rotational strategy could have produced strong, consistent returns.

The next image highlights how a combination of 80% bonds and 20% emerging markets has performed over the past 3 years.  Most investors think myopically about returns.   The discussion of returns, without the context of risk, is meaningless to professional investors.  

Note how despite a massive volatility spike in the overall marketplace, the standard deviation of daily returns for our 80/20% portfolio was quite low.   This assumes no rebalancing.  Value could have easily been added over this return with a relatively simple re-balancing rule.

This brings us to the Sharpe Ratio. The important aspect the Sharpe Ratio framework brings is to factor in ‘drawdown potential.’   If you own securities with higher relative volatility, the high to low moves will be larger and cumulative negative returns become increasingly difficult to overcome.  By thinking in terms of a sharpe ratio rather than returns-only, you will inherently adjust for the ever-present possibility of drawdown.  You cannot fully know the risk that awaits -- but you can at least think in terms of reward/risk.

To make this practical and to keep this from becoming a dissertation, I will just simply show one way to de-compose the Sharpe Ratio into a multi-factor model that allocates toward the segments of the market that show strong combinations of high relative strength and lower relative volatility.  This model uses two timeframes to calculate return and one part volatility.   It is considered a statistical model, a subset of the APT framework – and can more easily be thought of as ‘risk-adjusted relative strength.’

Below is an image of the model as it stood on June 30, 2007.  I can 'roll back' the model to any historical date through the calendar control in the top right corner.  Global asset classes are color-coded for ease of understanding what is showing strong relative strength.  International equities and select US equity segments were leading the market on a risk-adjusted basis during the middle of 2007.  Though not shown in the image, you can go to the site and view what was lagging (hint: housing and financial segments).

Fast-forward 1-year through setting the calendar control to June 30, 2008.  At this point, the screen is dominated with fixed-income ETFs. One way to interpret this is that global money is flowing INTO bonds.  These kinds of shifts are likely not whipsaws as you might find with the US-based Sector SPDRs --- which can have violent rotations even within a secular bull move.

This was one example of a global asset flow shift.  I could run this ETF Screener 50 times, run the results through our Backtest ETFs page and then post all of those images here.  But to truly understand this, it is better to interact with the data yourself.   It takes time to understand complex financial relationships and it is nearly impossible to see this in just one-dimensional snapshots in time through some images.   Change the model inputs, vary the timeframes.  This tool is meant to enable users to interact with each other at a fundamentally higher (risk-adjusted) level of conversation.


Follow ETFreplay