One of my relative strength sector models gave a buy signal for small cap value a few weeks ago. I have been evaluating a product called XTF ETF Ratings Service, and thought I would use it to select the most appropriate small cap value ETF for the model.
The XTF product has a structural integrity analysis that can be used to give a good overview of the trading efficiency of an ETF. The Structural Integrity analysis consists of the following seven factors:
- Tracking error
- Efficiency: daily alpha before expenses
- Market Impact
- Concentration Risk
- Tax Efficiency: Capital Gains
- Expense Ratio
- Bid-Ask Ratio
For my purposes, I am not so concerned about tracking error, concentration risk or tax efficiency (this model runs in a tax-deferred IRA account). So my main focus is on factors b, c, f and g.
These factor values are calculated and subsequently ranked on a percentile basis to rate each ETF with respect to all other ETFs within the same asset class. For every factor, each ETF is ranked by percentile such that the highest-ranking ETF in its class receives a 100 and the lowest-ranking ETF receives a 0. Here are more detailed descriptions of the four factors I focused on:
Factor b): Efficiency: daily alpha + expense ratio: Efficiency measures how well an ETF outperforms its stated benchmark before expenses. Since many ETFs do not hold every security in their benchmark, the ETF manager may use some security selection criteria to determine what to include in the ETF portfolio and how to handle dividends. Efficiency measures the ETF manager’s ability to generate alpha. The main sources of outperformance are: security selection, securities lending, and use of swaps or derivatives to track the index. In the case of swaps or derivatives we refer to this as basket optimization. The higher the efficiency, the better; a high efficiency ranking illustrates the ETF manager’s aptitude for implementing the techniques used to generate alpha. Daily alpha is the average value of the daily error for each ETF. To measure performance before expenses the ETF expense ratio is added to daily alpha.
Factor c): Market Impact: Market Impact (MI) quantifies the liquidity of each ETF. The MI measures the price impact of executing a hypothetical trade of 50,000 ETF shares. We estimate MI by multiplying daily ETF price volatility by the square root of the ratio of 50,000 shares to the average daily volume. The lower the MI, the better for the investor. Low MI means that price sensitivity to trade size is smaller for the ETF therefore its liquidity is higher. It serves as a proxy for trading efficiency: the ability to trade in and out of an ETF without negative performance impact.
e) Expense Ratio (ER): Expense Ratio (ER) is a straightforward annualized measure of an ETF’s expenses paid by shareholders.
f) Bid-Ask Ratio (BA): The BA measures the hidden or implicit transaction cost of an ETF. At any given time, the investor will buy at the asking price and sell at the bid price, incurring a loss equal to the difference between the two prices. The Bid-Ask ratio (BA) is the asking price less the bid price divided by the mid-price of the ETF. Dividing by the mid-price puts the dollar amount in percentage terms so investors can easily relate the measure to returns. We compute the BA as a simple average of all intraday quotes over a one-month trailing period. Our tracking of historical BA spread reveals the marginal change in popularity. As an ETF attracts assets we expect the BA spread to reduce over time reflecting increased trading volume. The BA component complements our MI measure. The BA is directly related to transaction costs and inversely related to liquidity. The lower the BA, the better.
For the sector model I am using, I look to trade in “chunks” of about $100,000, so liquidity is important. But this is not a day trading model and usually has a fairly long holding period (weeks or months, not intraday or just a few days).
Using the overall XTF ratings, the top two ETF’s for small cap value are VBR and IWN, so I decided to limit my analysis to those two. Here is a summary of the key factors I looked at from the XTF system. I have highlighted in boldface the higher rated ETF for each factor.
|Overall XTF Rating||8.6||7.9|
|Avg Bid-Asked Ratio||0.08%||68.30%||0.02%||96.10%|
|Avg Trade Size||295||219|
|Avg Daily Volume||247,386||1,704,614|
For the 8 data points I looked at, there was an even split. VBR was higher on four, and IWN was higher on four. I finally decided to select VBR, mainly because of the longer holding period. If my model was a mean reversion day trading model or I was trading much larger size (say $500,000) I would have selected IWN, since Avg Bid-Asked, Market impact, and Avg Daily volume are critical for short term trading especially with larger size. But since I may be holding the ETF for several months, the expense ratio and efficiency are important factors. Even though VBR has a smaller average daily volume, the average trade size is larger, so there may be less competition from automated trading programs.
Full Disclosure: Long VBR