Factor investing the reference portfolio template

Advocates of factor investing refer to well-performing backtest results caused by low-cost factor exposures. While cost-efficiency is a result of the rules-based approach, an overly simplistic adaptation of factor investing strategies can lead to many missconceptions, Footnote 1 especially in cases of the implementation of multifactor strategies. In a recent discussion about efficient ways to combine multiple firm characteristics into a multifactor portfolio, the top-down TD and bottom-up BU approach were differentiated.

The TD method is a two-step approach that first builds the single-factor portfolios and then combines them into a multifactor portfolio. In contrast, the one-step BU approach integrates all firm characteristics simultaneously into a multifactor portfolio. Across the recent literature, there has been no consensus on the superiority of one approach against the other. While Bender and Wang , Clarke et al. In an attempt to balance this discussion, Ghayur et al.

They argued that, after the factor exposures of both approaches are matched, the advantage of the BU approach disappears. Lester also underscored the importance of factor exposures by analyzing how factor exposure translates into expected portfolio returns. The findings confirmed that, on average, factor returns and risk scale linearly with factor exposure. However, in the performance comparison between the TD and BU approach, factor exposures alone provided no sufficient answer to the dissent in the literature.

Because, even without any exposure adjustments Amenc et al. In this paper, we argue that the discussion has neglected pricing inequalities between the different factors. Both approaches integrate equally weighted characteristics and ignore interaction effects from differences in informational content and correlations between the firm characteristics.

This neglect follows the questionable assumption that all characteristics are equally priced. The strong simplification of this assumption is demonstrated in the zoo of factors discussion. The published factors documented by Harvey et al. Additionally, the results motivate the construction of multifactor portfolios to diversify the factor risk posed by unpriced factors.

Ignoring the different pricing relevancies of factors can lead to biased performance results. In particular, if the BU approach loads large weights on noisy factors, high exposure to these factors could lead to a disadvantage compared to the TD approach. Avoiding the full range of interaction effects can not only lead to false performance statements but also ignores one of the main benefits of the BU approach. The BU approach can easily be extended to consider factor relevancy, while the TD approach is not able to do so without losing simplicity, which can be identified as one of its main benefits.

To distinguish among multiple firm characteristics, we apply an alpha forecasting framework from Grinold and Kahn , p. Lewellen showed that FM-based return forecasts correspond well with true expected returns and provide an effective way to combine multiple firm characteristics into a composite estimate of expected returns. Furthermore, Heinrich and Zurek exhibited the benefits of implementing a linear alpha forecasting model as an operational tool to combine multiple firm characteristics into a multifactor portfolio.

However, they primarily focused on the BU approach. Therefore, in this article, we conduct a horse race between the TD and BU approach, in which interaction effects and informational content differences between the selected firm characteristics are explicitly considered. Our study contributes to the literature in various ways. Based on this framework, it is possible to pinpoint the reasoning for an investment in a factor portfolio from the practical view of an investor who aims to beat a given benchmark portfolio.

Since the implementation of the OOP can be interpreted as an investment in the missing risk factor portfolio, investors who believe that the given benchmark can be complemented by this factor should invest in the OOP, increasing the Sharpe Ratio of the overall portfolio. This perspective is not only helpful for operational portfolio management but might also be relevant for performance measurement and risk management.

Moreover, by the implementation of the alpha forecasting model within the BU approach, we evaluate the difference between the TD and BU approach with regard to the full range of interaction effects. This topic has not yet been addressed in the recent discussions and is especially important for practice since, in multifactor portfolios and even in single-factor portfolios, many investors attempt to diversify their factor investing risk by considering several firm characteristics.

For example, the Fama-French proxy portfolio for value relies only on the book-to-market ratio, while the MSCI value index uses three additional firm characteristics. With regard to the practical applicability of our study, we analyze the various approaches with factor portfolios defined according to an industry standard for MSCI factor portfolios. Overall, the entire multifactor portfolio contains 16 firm characteristics.

Our simulation and empirical findings point out the important role of the informational content structure within the alpha forecasting process. Ignoring the informational content by weighting all of the signals equally leads to related performance results irrespective of whether the TD or BU approach is applied. In contrast, if the BU approach is extended by the consideration of the full range of interaction effects, a significant performance improvement in factor models with a high concentration of informational content could be demonstrated.

The remainder of the paper is organized as follows. Section 2 provides a simplified simulation example, which visualizes the importance of the full range of interaction effects within the bottom-up approach.

Section 3 presents the data set and the applied firm characteristics, whereas Sect. The figure below provides an overview of the four principal style factors—quality, value, momentum, and low volatility—that we believe have the most application for investors. Overview of fixed income style factors Source: BlackRock. Forecasts are based on estimates and assumptions, there is no guarantee that they will be achieved. The information shown above is for illustrative purposes only and not meant to be a recommendation to buy or sell any security.

Investors are often forced to reach for yield and take on more risk to meet return and income targets—further bidding up the prices of the riskiest securities. While reaching for yield may work in upward trending markets, avoiding defaults when the credit cycle turns downward is extremely important to maximize total return in the long run. Consider an alternative approach to investing in US corporate bonds that reaches for safety—not yield. First, a quality factor screen can be used to remove riskier securities that have a higher probability of default.

Intuitively, we believe removing such securities may help provide downside mitigation and avoid the behavioral bias of chasing yield in riskier securities. Second, a value factor tilt can be used to favor bonds that appear to be underpriced relative to fundamentals.

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Style Factors Style factors are a number of factors that aim to explain why some assets perform differently from those in the same industry and asset class. They relate the fundamental value of an asset to its current market price, typically by looking at the health and management of the company which is being invested in.

Examples of style factors include: Value: Sometimes, stocks are discounted relative to their fundamental value. Low volatility: Stocks which have proven to be stable over the medium term are valuable for active investors, making this an popular factor. Momentum: This factor measures the direction in which an asset is moving, and at which velocity.

Quality: A factor which looks at how well run a company is, and uses this to predict the future value of its stock. Size: An important element for many investors is the size of the company they are investing in. All of these factors can be used to predict the movement of a particular asset, and crucially the movement of an asset in relation to similar assets.

Because of this, style factors can be a powerful tool for active investors looking to make medium-term gains. These factors tend to change more slowly than style factors, but can still be excellent predictors of value in the medium term.

Examples of macroeconomic factors include: Economic Growth: Some stocks and bonds are more exposed to the macroeconomic business cycle than others, and this is an important element in their performance. Real Rates: Similarly, some assets are more affected by central banks varying interest rates.

This factor measures this exposure, and puts a definite value on it. Inflation: Inflation effects different assets differently, but this is often overlooked in less sophisticated investment strategies. Credit: By looking at the default risk of a particular company, this factor aims to give investors a more realistic understanding of the risk of an asset.

Liquidity: Conversely, the liquidity factor aims to express and quantify the value of a company having easy access to liquid funds. Just like style factors, these macroeconomic factors can be an accurate predictor of the movement of a particular asset. All of these factors aim to measure the impact of macroeconomic shifts on the value of individual stocks and bonds, and to explain why a particular security will perform better than its peers under the same macroeconomic conditions.

Why Factor Investing? The reasons why active investors should use factor investment approaches are pretty clear — factors help to explain why particular assets increase in value relative to the market, but can also be used to build portfolios that perform well across a range of macroeconomic conditions. This means that factor based portfolios can represent the best of both worlds — an investment strategy that is both diverse and high-return.

This is why two of the biggest institutional investors in the world — Blackrock and Vanguard — now offer factor based ETFs. These investment funds use a wide variety of factors, both style and macroeconomic, to offer investors well-balanced portfolios that claim to also offer high returns. However, these funds are one-size-fits-all affairs, and are better suited to wealthy investors.

For active investors who want to take a more hands-on approach, and to design their own factor based portfolio, there are other options available. One of these is Wealthface. Our platform allows investors to design their own factor based portfolios.

Using our system, you can design a portfolio based on just one factor, or on many. The recommendations should pass the criteria of — Suitability, Acceptability, and Feasibility. Are the recommendations acceptable given the culture of the Cppib Reference. Often consulting companies make this error that they strive to provide best in class solution even though implementing it may run counter to the culture of the organization. The recommendations should be consistent with the culture of Cppib Reference.

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