Morningstar Magazine - October/November 2016 - 43

All major currencies have trended downward on average, because of the strengthening of the U.S. dollar. The most significant pattern is the drawdown and bounce back of the Japanese yen. Post-Brexit saw the strengthening of the yen and subsequent decline due to the Bank of Japan's stimulus strategy. An appreciating yen hurts Japan's powerful export economy, yielding the negative premium. Likewise, the depreciation of Great Britain's pound sterling translated into a positive return premium, possibly attributable to expected export strength. We further examined the region factors and found no insightful patterns, so we do not show them here. Taking everything together, we have formed an image of which factors (for example, volatility, communication services, the yen) drove EXHIBIT 5 Risk: Financial Services After the Brexit vote, funds highly exposed to the financialservices sector saw significant increases in variance and skewness, paired with significant decreases in value at risk and conditional value at risk. Period 1M Period 1M 3M 6M 1Y Median Delta (%) 3M 6M 1Y 50 Median Delta (%) 25 50 250 -250 -25 -50 Variance Skewness Kurtosis Value at Risk Source: Morningstar. Skewness Kurtosis Value at Risk Variance Conditional Value at Risk Conditional Value at Risk -75 -50 -75 EXHIBIT 6 Risk: Developed Americas Meanwhile, the model showed the reverse for funds in the developed Americas region factor, which saw a decrease in expected variance, Period but increases in value at risk and conditional value at risk after the Brexit vote. 1M Period 1M 3M 6M 1Y Median Delta (%) 3M 6M 1Y 75 Median Delta (%) 50 75 25 50 250 -250 -50 -25 Variance Skewness Kurtosis Value at Risk Variance Skewness Kurtosis Value at Risk Source: Morningstar. Conditional Value at Risk Conditional Value at Risk -75 -50 -75 varying equity returns over the Brexit timeline and how they made an impact. Forecast Risk Each day, the risk model also produces a forecast of several risk statistics: variance, skewness, kurtosis, value at risk, and conditional value at risk. We examine how these forecasts change from before and after Brexit. One of the risk model's valuable features is the ability to screen for funds along each factor dimension. In the following analysis, we isolate the top decile of U.S.-domiciled funds exposed to each factor. We then calculate the median percentage change in the risk statistics from before and after Brexit. The following exhibits highlight selected findings. E X H I B I T 5 illustrates the changes in various risk statistics for funds highly exposed to the financial-services sector. We notice significant increases in variance and skewness, paired with significant decreases in value at risk, or VAR, and conditional value at risk, or CVAR. The magnitude of the change increases with the forecast time horizon. Overall, the risk model suggests this sector, having just underwent a shock, carries increasingly positive expected returns but also substantially more risk. Broadly speaking, E X H I B I T 5 represents the most common paradigm observed across the various factors. For example, funds highly exposed to developed Europe, the yen, and real estate factors also revealed the same pattern. Investors seeking to optimize their portfolio on these statistics (for example, CVAR) should be aware of these increases. E X H I B I T 6 highlights a reverse pattern for the developed Americas region factor. Expected variance decreases, while VAR and CVAR increase sharply after Brexit. That is, funds highly exposed to developed Americas experienced declines in both expected return and risk, implying a concentrated, negative shift in the forecast return distribution. E X H I B I T 7 shows the changes in risk statistics before and after Brexit for the economic moat global.morningstar.com/Morningstarmagazine Period 43 http://global.morningstar.com/Morningstarmagazine

Table of Contents for the Digital Edition of Morningstar Magazine - October/November 2016