- Many investors now use factor analysis to identify the drivers of active performance. However, the investment industry has yet to agree on a single, definitive approach.
- Active factor contributions are not static. Exposures can shift both as portfolio positioning evolves and as correlations among risk factors change over time.
- Unexplained risk and the instability of factor relationships mean that the excess returns on active strategies may not be easily replicated using backward-looking factor exposures.
- We believe factor analysis should be applied within a robust framework that takes its potential imprecision into account and also includes other quantitative and qualitative methods.
Factor analysis has become a popular tool for portfolio managers, investors, and financial advisors seeking a deeper understanding of the performance of actively managed strategies. However, significant differences in factor methodology, and the growing complexity of many factor models, have raised questions about the objectivity and reliability of such analyses.
Historically, the primary focus for most active equity managers has been individual security selection. Over time, academic research also has identified a number of common equity market factors—such as size, valuation, quality, and momentum—that are not fully correlated with each other or with market beta and that are now used by investors when decomposing active risk and excess return.
By identifying and tracking factor exposures within active portfolios, investors may be able to gain deeper insights into how their managers have generated returns and how their investment processes have evolved over time. However, the investment industry has yet to agree on a single, definitive factor methodology. Researchers continue to debate different risk factors—which in turn can be defined or measured using a variety of financial metrics. Investors need to understand that these choices may produce very different models and lead to very different analytical conclusions.
The growth of factor‑based analysis also has led some investors to consider the possibility of reproducing the returns on existing active portfolios by tracking the factor exposures within them. In our view, the imprecision associated with factor modeling highlights the potential risks in such an approach. Our analysis also suggests that factor risks may not be as closely linked to active returns as many investors seem to assume. For these reasons, we believe that replication strategies based on backward‑looking factor positions are likely to result in subpar investment performance.
(Fig. 1) Relative Factor Importance Can Change Significantly Over Time
Factor Contributions to Total Active Risk
T. Rowe Price Global Focused Growth Equity
March 31, 2004, Through March 31, 2019
Sources: T. Rowe Price; MSCI (see Additional Disclosures); FTSE London Stock Exchange Group plc and its group undertakings (collectively, the “LSE Group”). ©LSE Group 2019. All rights in the FTSE Russell indexes or data vest in the relevant LSE Group company which owns the index or the data. Neither LSE Group nor its licensors accept any liability for any errors or omissions in the indexes or data and no party may rely on any indexes or data contained in this communication.
No further distribution of data from the LSE Group is permitted without the relevant LSE Group company’s express written consent. The LSE Group does not promote, sponsor, or endorse the content of this communication.; Thomson Reuters (see Additional Disclosures); I/B/E/S (see Additional Disclosures); S&P Global Market Intelligence; Compustat (see Additional Disclosures); ICE Data Indices, LLC (see Additional Disclosures). All data analysis by T. Rowe Price.
The Roles of Factor Analysis
While there are many potential considerations in factor‑based analysis, we focus on two common applications:
- Decomposition of active risk, including the variability of factor contributions over time and the impact of modifying the time periods used;
- Excess return attribution, or the ability of the various risk factors to explain excess returns.
In addition, we constructed a simple hypothetical model for seeking to replicate future excess returns for an active equity portfolio by tracking its historical factor exposures. Our hypothetical example illustrates the weak relationship between the active portfolio’s excess returns and those of the replication strategy.
The methodology used in this paper employs the T. Rowe Price Equity Style Factor Model, which primarily is based on corporate balance sheet and income statement items and other equity fundamentals.1 The specific portfolio used in our analysis is the T. Rowe Price Global Focused Growth Equity (GFGE) composite; however, we believe our findings provide insights that also can be applied to the analysis of other active strategies.
(Fig. 2) Correlation Shifts Can Raise or Lower the Importance of Factor Risk
Factor Risk as % of Total Active Risk in a Hypothetical Single-Stock Portfolio
March 31, 2004 Through March 31, 2019
Sources: FactSet (see Additional Disclosures); MSCI; FTSE (see Fig. 1); Thomson Reuters (see Fig. 1); I/B/E/S (see Fig. 1); Compustat (see Fig. 1); IDC (see Fig. 1). All data analysis by T. Rowe Price.
Factor Contributions Are Dynamic
A central point to understand in any factor analysis is that active factor contributions are not static. Exposures can change dynamically both as portfolio positioning changes and as correlations among risk factors shift over time. Figure 1, for example, shows historical factor contributions to active risk within the GFGE portfolio over three‑year rolling periods ended March 31, 2019. At varying points in time, quality and growth each have been the single most significant factor contributing to portfolio active risk and at other times have been the least significant.
This instability in factor exposures across time highlights the importance of using rolling performance periods when decomposing a strategy’s active risk in order to capture factor relationships across a variety of market environments.
Our analysis implicitly suggests that the level of unexplained risk in an actively managed portfolio also may vary widely depending on the particular period studied. Three primary reasons for these fluctuations are:
- The impact of security selection decisions;
- adjustments in factor exposures implemented by the manager;
- changes in factor relationships (such as cross‑factor correlations).
That last cause—shifts in cross‑factor correlations—often is overlooked in factor analysis, yet it can have a significant impact on a portfolio’s assessed factor profile and on the expected risk and return characteristics associated with that profile.
To illustrate this point, we created a hypothetical portfolio holding a single security, the common stock of a large, diversified financial services company. We then regressed the historical returns of that security over three‑year rolling periods ended March 31, 2019, based on the same factors we used to analyze the GFGE portfolio.
As the ultimate “high conviction” strategy, a portfolio holding only one stock normally would be expected to have an extremely large unexplained risk factor—reflecting the historical predominance of security‑specific risk within unexplained active risk. However, Figure 2 indicates that even for a single‑stock portfolio, the contribution of unexplained risk can shift significantly over time—in this case, from as high as 75% to as low as 29% over the period shown.
In part, the variation in unexplained risk in our hypothetical single‑stock portfolio was driven by the specific factor set in the T. Rowe Price Equity Style Factor Model. If the model had included a financial factor, for example, the unexplained contribution probably would have been significantly lower. However, the variability over time seen in Figure 2 also was driven by changing relationships among the risk factors themselves.
These results appear to have significant implications for investors using factor analysis to assess the “activeness” of an investment strategy. As shown, the unexplained component of active risk can be highly unstable and may depend on changing relationships among the factors themselves. In our view, this variability should be a key consideration when applying any backward‑looking factor analysis.
We believe it is important for investors to recognize these analytical nuances to avoid the temptation of applying overly simplified rules when evaluating their own active strategies. In our view, factor attribution should be applied within a robust framework that takes into account its potential imprecision and that complements the analysis using other traditional quantitative and qualitative methods—for example, by supplementing a returns‑based factor analysis with a holdings‑based analysis.
(Fig. 3) The Importance of Unexplained Risk May Rise Over Longer Periods
Unexplained Risk as a % of Total Active Risk
T. Rowe Price Global Focused Growth Equity
Backward-Expanding Windows, March 31, 2004, Through March 31, 2019
Sources: MSCI; FTSE (see Fig. 1); Thomson Reuters (see Fig. 1); I/B/E/S (see Fig. 1); Compustat (see Fig. 1); IDC (see Fig. 1). All data analysis by T. Rowe Price.
Factor Importance Is Time‑Dependent
Because factor exposures within an active portfolio can be expected to change dynamically over time, the results of any factor decomposition analysis are highly time‑dependent—thus the importance of evaluating performance across multiple rolling windows to capture the instability of factor exposures. However, modifying the time frames for those rolling windows (e.g., using 10‑year instead of one‑year rolling periods) may significantly change the outcome of the analysis.
Figure 3, for example, shows the contribution of unexplained risk to total active risk for the GFGE portfolio over the period March 31, 2004, through March 31, 2019. The data points in the chart are all backward‑expanding windows—in other words, each data point adds an additional month to the regression compared with the one before it.
Interestingly, the unexplained share of active risk in the portfolio increased quickly until the regression covered a three‑year rolling period. From that point on, the influence of unexplained risk leveled off somewhat but still rose gradually, peaking at a 15‑year window, the end point in our analysis.
The implication is that, over the long run, unexplained risk accounted for the majority of the strategy’s active risk, while the combined contribution of the six risk factors (including market beta) specified in the T. Rowe Price Equity Style Factor Model diminished steadily.
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