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9 Active vs. Passive Asset Management: An Update In nearly any asset class, these less costly systematic factor-based strategies can be used to decouple rewarded risk factors from more expensive true manager alpha. Because investors now have choice in how they access factor returns, they are no longer tied to paying an active manager for them. A combination of systematic factor-based strategies and skilled traditional active strategies can lead to more cost-effective excess return. The onus is on active managers to earn their higher fee by contributing unique skill over and above factor premia, and the way to measure this true skill is by looking at multifactor-adjusted alpha. 4.2 | FACTOR ADJUSTMENTS Almost as soon as the one-factor CAPM was proposed, researchers began amassing evidence to extend it to multiple factors, recognizing that market exposure alone did not fully explain return. The academic research of the last half century or so has produced solid evidence that the value, momentum, quality, small size and low volatility factors have been rewarded with excess risk-adjusted return over time, and incorporating these factors into modeling exercises has proven more accurate in forecasting risk than the one factor market model. Recognizing that these systematic sources of excess return exist, we can write a regression equation to account for them and paint a clearer picture of true idiosyncratic alpha as a measure of manager skill. A regression approach allows us to bucket total return into systematic factors, like beta and style factors, and alpha, a measure of total return over and above what is captured with systematic sources of return. In this equation, ai is alpha, or the return not attributable to systematic risk factors. Rit is the return on a security or portfolio i for period t, RFt is the risk free return, and RMt is the return on a market capitalization- weighted market portfolio. The factor premia quality, momentum, size, low volatility and value are represented by their respective abbreviations, and the exposure of the security or portfolio to each of these factors is represented by their betas (bqi, bmi, bsi, bli, bvi). e represents a residual term assumed to have a mean of zero. Some statistical testing on the output of this equation will help identify a truly skilled manager as one that shows statistically significant alpha, and can flag those that have only added excess return with factor tilts or luck. This same approach can be used to evaluate the profile of systematic factor-based strategies. Where it would be used to look for statistically significant alpha of traditional active strategies, the focus for factor strategies is on the strength of exposure to targeted factors. However, assessing these strategies is complicated, because different measures of even a single factor versus that same factor's style benchmark will give different answers as to whether a strategy had more or less factor versus idiosyncratic risk or alpha driving its return profile. For example, if we measure a price-to-book based value strategy against a value benchmark denominated by price-to-earnings and price-to-sales, the strategy may show idiosyncratic return that is really factor alpha in disguise. Similar problems arise if the strategy and benchmark chose different weighting schemes, market capitalization-weighted versus equal-weighted versus signal-weighted, for example. Evaluation of factor-based strategies is clearly a nuanced exercise. Figure 2 A representative equation for a time series regression to assess this skill is as follows: R it = a i + RF t + Bi(R Mt -R Ft )+b qi (qual t )+b mi (mom t )+b si (size t )+b li (lowvol t )+b vi (value t )+ e Skill Systematic risk factors