June 23, 2007: Visualizing a market strategy

Topics:
  • Visualization recap
  • A chart of a market strategy.
  • How are we doing?
  • Current ETF rankings.
  • Next column teaser.

    • Visualization recap

    In the last column we looked at simple data visualization and demonstrated its power. If you missed that column, here's a link to it.

    If I had to summarize that column in one paragraph, I would put it this way:

    Data visualization is a powerful tool to help gain insight.
    Insight begets conviction.
    conviction begets action
    And only action can (hopefully) lead to out-performance.

    There's a hidden assumption in there: when making investment decisions, one must focus on raw-data grounded in facts and reality, and ignore herd mentality, prevailing sentiment, rumors, newsbytes , or anything else that might distract from the underlying facts. One should try to examine as many relevant facts (data points) as possible, and nothing but the facts.

    Since the stock market and economics in general, are a source of so much data, I keep wondering why the financial mass media keeps showing the same old simplistic charts: A single company stock price over time, or a single histogram of some economic indicator? Where's the big picture? There must be better ways to look at all the markets in one nice chart showing all sectors, all countries, many asset classes all in one, by adding color and animating ETF movements over time.

    Visualizing a simple market strategy

    Visualizing recent market preferences like we've seen last time, has been useful. We now step-up to visualizing market strategies. Doing this can provide insights into why a strategy is working or not. Perhaps it can even convince us that a strategy is sound and likely to keep working. Again, a chart is worth a 1000 words.

    A few weeks back, I did some strategy back-testing, with Weka (Waikato Environment for Knowledge Analysis).

    Weka is a flightless New Zealand bird. It is also the name a nicely integrated and comprehensive suite of data-mining and machine-learning tools from the University of Waikato, New Zealand. Weka contains a large number of components to import data, filter, clean and transform data, build models, select best features, estimate errors, and even some visualization aids. Weka is free (both as in speech and in beer) and released under the Free Software Foundation's GNU GPL (General Public License), just like Linux.

    Below is a snapshot of Weka visualizing a data-set. This data-set has two new ETF columns (features) I've never shown here before. Together, they represent an investment strategy:

    How can you possibly visualize a ranking-function? You ask. Well, since our ranking ranks ETFs in order of attractiveness, it essentially assigns a numeric attractiveness score to each ETF data point. This attractiveness score is a number that can be plotted along the X axis in a chart.

    Aha, that's obvious now. But how can one plot future performance? Well, one can't, or we wouldn't be here, but one can get close. It is called back-testing. If you go back 3 months, and run the ranking functions on the data as it existed at that point in time, you can also apply hindsight, and look at the future 3-month return, from that point in time till today.

    Of course, there's no guarantee that what worked in the past 3-months will work in the next 3-months, but:

    Moreover, we can repeat this experiment for longer time frames, or start from different points in time, or look at other strategies, for comparison, and so on. In short, we have a very powerful tool in our hands. And this tool can be applied to many, and diverse, future cases.

    Let's take a look at this Weka visualization screenshot:

    Each point represents some ETF at a random point in time in the past. Let's go over the exact coordinates of each point:

    Note that the number of points here is larger than the number of ETFs because the sampling was done for many different points in time to ensure randomness. IOW: each ETFs appears multiple times in the chart representing different points in time. At different times, an ETF can have somewhat different score it was given by the ranking function of the strategy, a different technical risk, and of course, different future 3-month return.

    The most salient point here is that MMVR, by design, is not trying to maximize future returns, but risk-adjusted future returns. This is why it seeks moderate momentum rather than high momentum, and this is why it is trying to avoid volatile ETFs.

    To borrow a concept from artillery. MMVR is seeking long range trajectories, as opposed to high angle (> 45 °) trajectories which gain altitude fast, but then abruptly change direction, and lose altitude just as fast.

    The assumption is that by limiting risk, we limit potentially unpredictable downsides. It makes the product of (Gain x Chances_Of_Gain) more uniform and hopefully higher on average, over many pick instances, in the long term.

    As can be seen in the chart, most of the points with the highest MMVR score are concentrated around a "latitude" of about +5% to +11% next 3-month (future) return. This (very) roughly translates to about 30%/year. Of course, this model, and its data cover a period within a bull market. A +30%/year return should not be considered a long term, sustainable figure. Still, 30%/year, even during pure bull markets, is a very respectable and desirable goal.

    MMVR avoids both:

    Since the chart is representing different points in time, some of the biggest winners happen to be among the biggest losers sometime later. MMVR looks for the most stable and straight lines, which is the source of some of its strength.

    What would have happened if we picked a strategy that is willing to take more risk and buy more high-flyers? Or maybe some crash-and-burners, perhaps after they crashed? What if we picked more components or less components to make up our portfolio mix? What are the error rates of the model when trying to use it as a predictor on data that was not used to build it? These are all excellent questions which I will try to partially answer in future columns.

    How are we doing?

    Our model portfolio SZDM continues to outperform the 3 major US indexes since its inception, although the gap has narrowed due to record performance of the Dow and the S&P500 last spring. In the past couple of weeks, including in the recent down week, we are back outperforming both the S&P500 and the DJI. Earlier in June we've also hit a milestone of being up +10% since our inception less than 5 months ago (January 28, 2007). The recent down week has reduced our total gain to +8.47%. Our annualized (extrapolated) returns stood at 28.32% on June 15th. Some recent figures can be found here.

    Current ETF rankings

    Here are last Friday's (June 22, 2007) rankings (top 40 ETFs among over 400): 
    
	Using mmvr ranking method on 20070622
	1	 3.0973	EWY	iShares MSCI South Korea Index
	2	 3.0742	TTH	Telecom HOLDRs
	3	 3.0014	VAW	Vanguard Materials VIPERs
	4	 2.8606	EWG	iShares MSCI Germany Index
	5	 2.8470	ADRU	BLDRS Europe 100 ADR Index
	6	 2.7763	PRFE	PowerShares FTSE RAFI Energy
	7	 2.6968	ADRD	BLDRS Developed Markets 100 ADR Index
	8	 2.6138	RPV	Rydex S&P 500 Pure Value
	9	 2.6126	FEZ	streetTRACKS Dow Jones Euro STOXX 50
	10	 2.5769	EZU	iShares MSCI EMU Index
	11	 2.5693	PKB	PowerShares Dynamic Building & Construction
	12	 2.5430	PXE	PowerShares Dynamic Energy Exploration
	13	 2.5411	RZV	Rydex S&P Smallcap 600 Pure Value
	14	 2.5397	RYE	Rydex S&P EqWght Energy
	15	 2.5167	XLE	Energy Select Sector SPDR
	16	 2.5163	PWP	PowerShares Dynamic Mid Cap Value
	17	 2.4888	DLS	WisdomTree Intl SmallCap Dividend
	18	 2.4810	EWT	iShares MSCI Taiwan Index
	19	 2.4678	DKA	WisdomTree Intl Energy
	20	 2.4631	DEW	WisdomTree Europe High-Yielding Equity
	21	 2.4392	DEB	WisdomTree Europe Total Dividend
	22	 2.4339	DFE	WisdomTree Europe SmallCap Dividend
	23	 2.4171	VOX	Vanguard Telecom Services VIPERs
	24	 2.4121	IEO	iShares Dow Jones US Oil & Gas Ex Index
	25	 2.3862	IXP	iShares S&P Global Telecommunications
	26	 2.3791	DBN	WisdomTree Intl Basic Materials
	27	 2.3601	EWQ	iShares MSCI France Index
	28	 2.3445	RFV	Rydex S&P Midcap 400 Pure Value
	29	 2.3424	IEV	iShares S&P Europe 350 Index
	30	 2.3344	IYZ	iShares Dow Jones US Telecom
	31	 2.3050	EWK	iShares MSCI Belgium Index
	32	 2.2910	VDE	Vanguard Energy VIPERs
	33	 2.2765	PPA	PowerShares Aerospace & Defense
	34	 2.2705	EWN	iShares MSCI Netherlands Index
	35	 2.2540	PRF	PowerShares FTSE RAFI US 1000
	36	 2.2306	IXC	iShares S&P Global Energy Sector
	37	 2.2248	EWC	iShares MSCI Canada Index
	38	 2.2035	PTE	PowerShares Dynamic Telecom & Wireless
	39	 2.1966	DIM	WisdomTree Intl MidCap Dividend
	40	 2.1961	XOP	SPDR Oil & Gas Exploration & Production

    Next column: ETF strategies Battle Royale

    Now that we saw one strategy "in action", we're ready to look at a large number of strategies. In the next column, I'll present an experiment I did by back-testing a large number of different simple investing strategies to pick ETFs. Which one did best? By which criteria? Should we employ more than one strategy in different circumstances? Can we do better than MMVR based on more data and models?

    Some surprising and exciting results are coming. Stay tuned.

    As always, I hope you found this column useful. Feedback, good or bad, is always more than welcome.

    -- ariel