There exist two most common techniques for constructing multifactor portfolios. The mixing approach creates single-factor portfolios and then invests proportionally in each to build a multifactor portfolio. The integrated approach combines single-factor signals into a multifactor signal and then constructs a multifactor portfolio based on that multifactor signal. Which methodology is better? It is hard to tell, and numerous papers show each method’s pros and cons. The recent paper from Joris Blonk and Philip Messow explores this question from the standpoint of the credit fixed-income portfolio manager and offers their analysis, which shows that an integrated approach is probably better in this particular asset class.

To make these two approaches comparable, authors use exposure-matched portfolios and limit themselves to long-only portfolios, as long-short strategies are more of a theoretical construct than a realistic, practical application for corporate bond investors. The authors found consistent results that indicated that integrated multifactor portfolios outperformed mixed multifactor portfolios. These results hold across different investment universes (Investment Grade and High Yield), different underlying factor suites (two or four factors), different exposure concentrations (low or high), and different market environments (falling/rising interest rates, falling/rising credit spreads, etc.).

In addition, they show that an integrated approach reduces downside risk by avoiding investing in bonds with offsetting single-factor exposures (e.g., high value & low momentum), the so-called “value traps.” Most studies in the credit factor investing literature lack an answer to implementing these strategies under realistic conditions and achieving attractive risk-adjusted returns. Their analysis provides a first direction for translating these theoretical studies into “real” portfolios. Therefore, this study has important implications for practitioners who want to implement multifactor strategies for corporate bonds.

The next logical step would be to ask another question – which approach is better in all-equity investment universe where shorting is allowed and easier?

Authors: Joris Blonk and Philip Messow

Title: How to Construct a Long-Only Multifactor Credit Portfolio?

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4775767

Abstract:

This paper examines how to combine single factors into a multifactor portfolio of corporate bonds. The two most common approaches in the literature are the so-called ‘integrated’ and ‘mixing’ approaches. This paper analyzes these two methods in corporate bond markets, and finds that the integrated factor portfolios generally lead to higher risk-adjusted returns. This is largely due to the fact that they do not invest in underperforming bonds that score poorly on a single factor, to which the ‘mixing’ approach is exposed to. Our results are robust over time and hold in different macro environments and in both Investment Grade and High Yield markets.

As always, we present several exciting figures and tables:

Notable quotations from the academic research paper:

“In the purest form of passive equity investing, an investor’s portfolio includes each stock in the market in exact proportion to its weight in the market (i.e., the total stock market index). However, for several reasons, including that it is impractical for most investors to hold several thousand stocks, funds typically attempt to replicate only a subset of the market, known as an index. They do so using one of two methods.

First, owning each stock in proportion to the underlying index is known as full replication. This strategy is challenging for many reasons, including that it typically requires adjustments to all (i.e., tens, hundreds, or thousands) of the portfolio’s positions each time an index adds or removes a stock. Many of the required adjustments are small and pertain to relatively illiquid stocks, which creates the potential for large trading costs that reduce the benefits of replication.

The second approach, called representative sampling, selects only a subset of index components for inclusion in the investor’s portfolio, but retains the goal of matching index returns. Of course, sampling creates the potential for even greater tracking errors and thus strays farther from the passive ideal. However, because the strategy requires holding fewer stocks, it may reduce trading costs, which would enhance returns. For example, because they do not hold the entire index, samplers might be able to avoid the most illiquid stocks or avoid trading following many instances of index reconstitution.

We show that sampling funds have higher turnover than replicating funds. This suggests that the active component of sampling, or the selection of stocks using variables other than index weights, more than offsets any reduction in trading arising from holding fewer positions. We also find that sampling funds have higher expense ratios and management fees, consistent with the costs of active selection more than outweighing the benefits of holding fewer positions, and with fund managers seeking compensation from investors for their efforts to actively invest. However, our examination of fund returns suggests these higher expenses and fees are not warranted because the sampling fund managers do not appear to be skilled at active investing. In particular, sampling funds’ returns are lower than replicating funds.

Several additional analyses support and extend our main results. First, our results hold in subsamples of S&P 500 indexers and other market-cap-based indexers, which helps rule out concerns that our findings are driven by one or a few peculiar indices, by “style” or “sector” funds, or by unobservable cross-index differences. Second, we find that our results are strongest among funds following indices with fewer constituent stocks, and that they entirely disappear for samplers following indices with 1,000 or more stocks. This suggests sampling is not harmful only when it can drastically reduce the number of stocks held in the portfolio. Third, we find that investors’ funds increasingly flow to samplers relative to replicators over our sample period, which is puzzling given our cost and return results.

The differences in costs, returns, and flows we document are economically significant. For example, replicators outperform samplers by about 60 basis points (bps) per year on a net return basis. To illustrate the potential wealth effects of this difference, consider a hypothetical investor who makes a one-time index investment of $100K at 35 years old and holds the investment for the next 30 years. Assuming a constant 8% annual return, the investor’s holding will be worth about $1,000K at age 65. However, if annual returns are 60 bps lower (i.e., 7.4%), then the value of the investor’s holding would only be about $850K at age 65. This $150K, or 15%, difference in portfolio value is approximately equivalent to losing the last two years of returns over the 30-year horizon.

Most importantly, our findings should be useful to fund managers trying to decide how to track an index, to plan sponsors selecting investment options for an organization’s employees, and to the ultimate investors trying to evaluate their index fund managers. The disparate approaches and outcomes of replication vs. sampling have been surprising to financial economists (including both academics and practitioners) with whom we have shared our results thus far. To us, this suggests that most mom-and-pop investors, and even many finance professionals, are likely similarly unaware of the distinctions.”

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