Several characteristics have provided consistent risk-adjusted excess return for country ETFs, including value-, momentum-, quality-, and sentiment-related factors. This article highlights a country ETF that is currently very attractive using an ETF selection model based upon such factors: **iShares MSCI Sweden ETF (EWD)**.

__Country Selection Factors__

A “**factor**” is a mathematical way of measuring how much of a certain characteristic a security has. For example, a stock’s size is typically measured using market capitalization. A stock’s value might be measured with its price/earnings ratio. Its quality might be measured with its debt/equity ratio.

A previous article pointed out that some of the **same factors** (characteristics or attributes) that published academic research has found to be **helpful in selecting individual international stocks** can be **helpful in selecting countries**. These factors can be grouped into the following categories:

**Value****Momentum****Quality****Sentiment**

In this article we will focus on **one factor in each category.** We will illustrate that factor’s use in country selection by showing how **iShare MSCI Sweden ETF** (**EWD)’s** **“factor exposures”** (or **“factor loadings”**) contribute to its expected **risk-adjusted** **return** in Sapient Investment’s country ETF selection model.

We base our selection of factors on **published research**, confirmed with our own **primary research**. We are able to analyze ETFs much like we would analyze stocks because our database (FactSet) aggregates stock-level information on ETF stock holdings up to the ETF level. For example, the P/E of an ETF is based upon the P/Es of the ETF’s stock holdings aggregated up according to their portfolio weights.

__Standardized Factor Exposures__

It may be readily apparent that stocks and ETFs with attractive value, momentum, quality, and sentiment characteristics might make good investments. The difficulty is translating that vague and intuitive sense into something more **concrete and actionable**. What specific factors should be used to measure each of these attractive attributes? How much emphasis should be placed on each? Should the relative weights be fixed, or should they be flexible and vary over time? Quantitative methods are very handy for answering these questions.

In general terms, the task of the quantitative analyst is to construct an investment system that will maximize risk-adjusted return **in the future**. It’s not that hard to construct a system that “works” based on historical data. The problem is that too often, systems too heavily trained on historical data will break down going forward in live use.

Perhaps the most important measure that helps to prevent overfitting of an historical data sample is to **only test factors that have a solid basis in economic theory**. Markets have a strong tendency towards **efficiency**. Prices in efficient markets fully reflect all available information. Exceptions to that tendency must have a good explanation as to why they might exist and persist.

Academics who study markets have identified several “**anomalies**”—exceptions to the general assumption of market efficiency. The books, journal articles, and white papers published on these market anomalies provide ideas for further primary research by the quantitative analyst. The weight of accumulated academic evidence suggests that the strongest factor anomalies are generally related to the four categories highlighted here: **value, momentum, quality, and sentiment**.

The statistical test most often used to measure the “**payoff**” to a factor (the return associated with it) is **regression analysis**. It may be helpful to visualize the test within an Excel spreadsheet. In Column A is the “**dependent variable**” that you are seeking to explain: **monthly risk-adjusted excess returns**. Each row is for a security: in this case, a sector or industry ETF. Column B contains the **independent variable** you believe is associated with excess return: **the factor**. However, rather than the raw factor, it is often transformed into a standardized factor exposure as described next.

The mechanics of regression analysis require that both the dependent and the independent variables be “normally distributed.” That is, they fall along a nice bell-shaped curve like the one shown below:

How much an ETF has of a particular factor can be measured with the “**standardized factor exposure**” using the following formula:

Standardized factor exposure = __(ETF factor exposure – Universe average factor exposure)__ Universe standard deviation of factor exposures

The graph above illustrates the **standard normal distribution**. The line labeled “1σ” is for 1 “sigma” the Greek letter usually used for one standard deviation. Note that in terms of percentile, a one standard deviation factor exposure is **higher than 84.1% of the observations**—only 15.9% are above it. The Excel regression described above calculates how much added risk-adjusted return an ETF with a **one standard deviation factor exposure** achieved within the month being tested.

Of course, what we really want to know is not what factor returns were in the past but what they will be in the future. Fortunately, some factors within the four categories highlighted have both a **positive long-term average payoff** and also to some extent a **trend-following tendency** in the ebb and flow of their payoffs. Consequently, some sort of moving average of recent payoffs can be a reasonably good forecast of future expected payoffs. At Sapient, we use an **exponentially weighted moving average** of past returns to forecast the expected factor return over the next month.

In the sections that follow, we will drill down into one sample factor within each of the four categories, showing its historical payoff pattern within our universe of country ETFs. Currently, there are **80 country ETFs** within our universe, so the sample is fairly broad. In addition, we will note the factor loading of **iShares MSCI Sweden ETF (EWD)** with respect to each factor as of the **most recent month-end (May 31, 2019)**, and cite the expected risk-adjusted return contribution for EWD derived from each factor. The final section will provide our overall expected return for EWD and cite a few fundamental factors affecting its investment attractiveness.

__Value Factor: TSR Fwd E/P__

**Time-Series Relative (TSR) Forward Earnings Per Share / Price **compares the current forward earnings yield (EPS/price) to its 36-month median. A higher number means that value is better than in the recent past. This value ratio has been particularly powerful over the past year.

A comment regarding the use of time-series relative factors may be in order. A **TSR** factor can greatly **improve the comparability** of a factor across ETFs that are not very homogeneous. International accounting standards can very among countries, for example. Also, analyst coverage can vary a great deal across the globe. Comparing the current company’s factor to its historical median or average helps control for these differences.

The graph above is a “**factor graph**.” It shows the **cumulative return** from a **portfolio** that is **neutral in all respects but has a one standard deviation above average exposure to the factor**. TSR Fwd EP has had a cumulative return of about 28% since 2008, which equates to **about 2.4% per year**. 2.4% does not appear very impressive on the surface. However, note that it is **not cumulative total return** that is being measured

**but the log of cumulative**.

__residual__return**The distinction is vital.**The vast majority of return for country ETFs is systematic return, most especially return derived from an ETF’s market beta or sensitivity to the market.

**Residual return is the return that is left unexplained by an ETF’s systematic risk factor sensitivities.**Over the long-term,

**the average residual return for all of the ETFs in the international universe is**

**essentially zero**! That’s right. There is

**no alpha**. On average, over the long term, ETFs earn return only from their systematic risk sensitivities, especially their sensitivity to the market (or market beta).

As of May 31, 2019, iShares MSCI Sweden ETF (EWD) had a **standardized factor exposure** to TSR Fwd EP that of **.38**, meaning that it was .38 standard deviations above the average. Based upon that factor’s exponentially weighted moving average payoff, we expected that a one standard deviation factor loading would add **.41%** (or 4.92% per year) to risk-adjusted return over the following **month**. We simply multiply EWD’s factor exposure (.38) by the expected payoff within the country ETF universe (.41%) to get the expected risk-adjusted return contribution of **.16% **(or 1.87% per year).

__Momentum Factor: EPS Estimate Revision Diffusion__

The **changes that analysts make in their forecast of future EPS** reflect momentum in the earnings growth of the companies within a country index. **“Diffusion”** is calculated as **(#up - #down) / (#up + #down)**, which is a way of calculating %up-%down. “# up” means the number of upward analyst revisions of EPS in the last month. At Sapient Investments, we use a fairly complex way of incorporating changes in the EPS forecasts that analysts make for the underlying constituents of an ETF, combining data for the next two fiscal years.

Because it is based on changes in the estimate of future earnings, EPS estimate revision diffusion tends to be the most **forward-looking** factor, providing an early warning if the outlook for a country is changing. It has achieved an annualized average residual return of 1.9%, although it has been fairly flat over the past few years. Consequently, our ETF selection model currently forecasts a lower than average factor payoff.

As of May 31, 2019, iShares MSCI Sweden ETF (EWD) had a **standardized factor exposure** to EPS Estimate Revision Diffusion of **3.40**, meaning that it was 3.40 standard deviations above the average. Based upon that factor’s exponentially weighted moving average payoff, we expected that a one standard deviation factor loading would add **.05%** (or .60% per year) to risk-adjusted return over the following **month**. EWD had a factor exposure of 3.40, so it’s added expected risk-adjusted monthly return from its exposure to EPS Estimate Revision Diffusion was **.17% **(or 2.04% per year).

__Quality Factor: TSR EBITDA Margin __

**Time-Series Relative EBITDA Margin **compares the (Earnings Before Taxes+Depreciation+Amortization) / Total Revenues (or EBITDA Margin) to its 36-month median. A higher number means that a company is more profitable than in the recent past.** **EBITDA margin helps to put different kinds of companies more on the same footing than would net income margin for example, because it is not as cyclical and not as sensitive to whether capital is sourced from debt or equity. TSR EBITDA margin rewards companies with improving profitability.

Although it has weakened in the last few years, TSR EBITDA Margin has proven very powerful over the long run, with an average residual return of **2.4% per year** since 2008 as shown in the factor graph above. Because our ETF selection model dynamically adjusts to changing payoffs, our current forecast of the payoff to TSR EBITDA Margin is much lower than its long-term average.

As of May 31, 2019, iShares MSCI Sweden ETF (EWD) had a **standardized factor exposure** to TSR EBITDA Margin of **.05**, meaning that it was .05 standard deviations above the average. Based upon that factor’s exponentially weighted moving average payoff, we expected that a one standard deviation factor loading would add **.06%** (or .72% per year) to risk-adjusted return over the following month. Since EWD had a factor exposure of .05, it’s added expected risk-adjusted monthly return from its exposure to TSR EBITDA Margin was **.003% **(or .04% per year). Obviously, this factor was not an important contributor to the residual return forecast for EWD.

__Sentiment Factor: TSR Buy%-Sell% __

Sell side analysts usually provide buy/sell/hold recommendations on the stocks that they follow. The TSR Buy%-Sell% factor subtracts sell, underweight, and hold recommendations from buy and overweight recommendations. Furthermore, it compares the current level of this indicator to its 36-month median. As such, it favors companies with **improving analyst sentiment**.

TSR Buy%-Sell% has been particularly powerful recently, boosting our forecast of its near-term payoff. Since 2008, its average annualized residual return has been 1.5%.

As of May 31, 2019, iShares MSCI Sweden ETF (EWD) had a **standardized factor exposure** to TSR Buy%-Sell% of **-.60**, meaning that it was .60 standard deviations *below* the average. (Few ETFs are above average in *all* respects.) Based upon that factor’s exponentially weighted moving average payoff, we expected that a one standard deviation factor loading would add **.21%** (or 2.52% per year) to risk-adjusted return over the following **month**. EWD had a factor exposure of -.60, so it’s added expected risk-adjusted monthly return from its exposure to TSR Buy%-Sell% was **-.13% **(or -1.51% per year).

__Putting It All Together__

The four factors analyzed above represent a subset of the factors that we currently use in our international ETF selection model at Sapient Investments, but they are some of the most important ones. The **overall sum of our factor-based risk-adjusted monthly return forecasts for EWD** on May 31, 2019 was **1.28%**. This was among highest risk-adjusted monthly return forecast within our international ETF universe.

In addition to the factors we use to forecast risk-adjusted return, we also forecast **risk-related return**. For equity ETFs, this forecast is primarily driven by our return expectation for the overall stock market and the ETF’s stock market sensitivity or “**beta**.” We use the S&P 500 to represent the stock market, since our clients are U.S. investors whose equity exposures are reflected in that index. Our proprietary four-factor risk model indicated that EWD had a **stock market beta of .74** on May 31, 2019. At that time, we were forecasting a **stock market return **of 9.12% per year, or** .76% per month**, so the one-month stock market return forecast for EWD was **.56% **(.74 x .76%).

The other significant factor exposure for EWD was its non-dollar risk. We use the U.S. Dollar Index to measure this risk. EWD had a **dollar sensitivity** (or beta) of **-1.14** on May 31, 2019. We were forecasting a return in the U.S. Dollar Index of **.09%** (1.08% per year), so the one-month dollar return forecast for EWD was **-.10% **(-1.14 x .09%).

Adding the risk-adjusted and risk-related forecasts together resulted in a **total monthly return forecast of 1.74%** for EWD on May 31, 2019. When selecting ETFs, their expected return forecast is the primary driver, although we apply a 50% haircut to the return forecast to reflect its high level of uncertainty. We also consider the costs of trading and owning an ETF, as well as the volatility of its returns. EWD had a relatively modest expense ratio of .49% and a very low bid-ask spread of .04%. Its level of historical volatility was about average. All of these combined into an overall attractiveness rating (or “utility”).

__A Bit of Country Analysis__

At Sapient, our quantitative models drive all of our investing decisions. We do not make subjective, emotional, seat-of-the-pants moves. However, we do make sure that the conclusions of our models pass a “reality check” level of due diligence. That is, we want to make sure that our models are not missing major contraindicating considerations.

Happily, in the case of Sweden, if anything our models may understate the investment case. Sweden is a fairly **small and prosperou**s country with a government that functions extremely well, especially compared to the U.S. With about 10 million people, its per capita income is 11% higher than the U.S. Sweden combines a **generous welfare system** with an **extremely open and competitive free market economy**. Trade is vital to Sweden’s **export-oriented economy**. A strong rule of law, a high level of fiscal responsibility, and a cooperative attitude among competing political parties provide a background of stability.

Some of the top holdings in EWD include Atlas Copco (industrials, 8.5%), Ericsson (telecom, 8.1%), Volvo (autos, 6.6%), Nordea Bank (banking, 6.4%), Investor AB (venture capital, 5.7%), Assa Abloy (security services, 5.6%), and Sandvik (mining equipment, 5.1%).

__Conclusion__

- The same factors that have provided excess risk-adjusted return in international stock selection can also be used to select countries
- The most powerful and consistent factors fall into these four categories:
- Value
- Momentum
- Quality
- Sentiment

- iShares MSCI Sweden ETF (EWD) has very attractive characteristics:
- Higher than median forward EPS/price
- Upward revisions of earnings estimates
- High and improving profitability margins

- We expect that EWD will be one of the top performing country funds over the next month and maybe longer