Decades of Innovation in Fixed Income Investing
Dimensional launched its first bond strategy in the early 1980s and has developed deep expertise into what drives expected returns and how to pursue them.
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In 1983, Dimensional got its start in fixed income with the launch of a US ultra-short duration strategy, implementing findings from Professor Eugene Fama’s research on the relation between forward interest rates and subsequent returns.1
What we knew then—and what remains true today—is that a bond’s return has three primary components: the yield, the expected capital appreciation or depreciation over the holding period based on the current yield curve, and the return due to future interest rate changes. The first two components are known and observable today and combine to form the forward rate. Fama’s work found that observed forward rates contain reliable information about future differences in expected bond returns.
This research led to the development of Dimensional’s variable maturity approach, which remains integral to our strategies today. Dimensional uses the information available in forward rates to identify bonds with the highest expected returns, within a strategy’s guidelines, and varies duration by targeting those securities. Generally, this approach implies that, within pre-defined ranges or relative to a benchmark, our strategies will pursue longer duration bonds where yield curves are upward sloping and shorter duration bonds where yield curves are flat or inverted.
Dimensional expanded its offering in the 1990s to include global bonds with the launch of a global short duration strategy. The approach was guided by Fama’s 1980s research on forward and spot exchange rates2 and additional work in the 1990s between Fama and Dimensional on currency-hedged global investing.
Investing in global bonds on a currency-hedged basis offered investors several potential benefits, including a broader opportunity set for identifying bonds with higher expected returns and diversification of duration exposure. Using information in forward rates, we can identify bonds issued in different currencies that offer higher expected returns and increase a portfolio’s allocation to those securities. Because global yield curves do not move in tandem, a global portfolio can also manage the risk of unexpected changes in rates across individual yield curves.
Dimensional’s first intermediate duration strategy was also introduced in the 1990s, designed to meet the needs of investors seeking a marketlike portfolio duration while pursuing greater-than-market returns. The strategy pursued a targeted duration similar to a market benchmark while allocating to a subset of bonds offering higher expected returns. This approach—rather than investing across all maturities3—was the crucial insight underpinning the strategy. The design of this portfolio established a value-added approach for duration-constrained investing and formed the basis for future benchmark-relative strategies.4
In the new century, the 2002 introduction of the Trade Reporting and Compliance Engine (TRACE) proved to be a major advance for the US bond market. The system requires dealers to report over-the-counter secondary market transactions in USD-denominated corporate bonds, agency bonds, and securitized products within 15 minutes of an execution. With TRACE, the fixed income market finally had bond trade transparency similar to the price visibility afforded to equity investors with the ticker tape.
Prior to this development in the bond market, Dimensional’s fixed income strategies focused on the highest-quality investment grade issues. After TRACE launched, Dimensional spent several years studying bond trade data, which provided a higher-resolution view of the full corporate bond market. The research confirmed that differences in forward rates contain reliable information about differences in expected returns not only across the duration spectrum but also across the credit spectrum and ultimately informed our variable credit approach.
Variable credit was first introduced in Dimensional’s credit and core fixed income strategies, which extended the eligible credit range down to BBB rated bonds. This approach uses information in forward rates (as captured, for example, by credit spreads) to identify bonds with higher expected returns across eligible yield curves of varying credit quality and dynamically allocate to those securities. In practice, the approach increases exposure to eligible bonds with lower credit quality when credit spreads are wider and those securities offer higher expected returns. Conversely, variable credit reduces exposure to eligible bonds with lower credit quality when credit spreads are narrower and expected returns for those securities are lower.
TRACE also provided us with additional opportunities for enhancing Dimensional’s risk management processes. We began systematically incorporating TRACE data on traded prices and quantities into our market-informed credit ratings, alongside a host of other considerations, such as information in prices of credit default swaps. Specifically, we began to continually monitor market yields of corporate and agency bonds and assign a lower credit rating to those bonds whose yields suggested a higher level of credit risk than indicated by their stated credit ratings. Additionally, because most global borrowers issue bonds denominated in USD, we could infer internal credit ratings for the vast majority of non-US issuers and apply our internal credit monitoring process to non-US and global strategies. This allowed us to improve our credit monitoring process, as our research shows that bonds trading at substantially higher yields than peers with the same credit rating generally perform more like lower-quality bonds and also experience a higher frequency of downgrades.
TRACE has also informed the development of our inventory modeling and enhanced our trade cost analysis. Dimensional has always employed a flexible trading approach to reduce implementation costs in our portfolios. Having visibility into daily bond trade data, including whether a dealer was involved in the transaction, allowed us to begin modeling dealer inventory. Gaining an understanding of dealer inventory and holding periods enables us to have a better view of available market liquidity across different bonds, which, paired with our flexible trading approach, generally allows us to trade at favorable prices. Further, TRACE has enabled extensive trade cost analysis that helps measure and monitor outcomes of our flexible trading approach relative to others in the marketplace. Our ongoing analysis and monitoring efforts have consistently validated the benefits of our approach and informed improvements over time.
While TRACE opened up several opportunities for enhancing our investment approach, Dimensional also expanded into new strategy offerings in the 2000s to meet investor needs. These solutions included our first US municipal bond and inflation-linked strategies, which applied our understanding of the drivers of fixed income returns to new areas of the market.
At Dimensional, we are on a continual search for new research and ideas that may lead to advancements in fixed income investing. However, we do not lose sight of the foundational understanding of fixed income returns that has benefited investors over time.
We have seen trends come and go, and we expect to see more in the future. A current example is the rise of “smart beta” approaches in fixed income. Some investment managers propose that additional variables, such as company size, leverage, or bond momentum, are linked to expected bond returns. A recent study by Dimensional, conducted as part of our ongoing research into fixed income markets, found that most of the information these variables provide is already reflected in forward rates, originally examined by Fama in the 1970s.
The paper, “The Cross-Section of Corporate Bond Returns,”5 offered strong empirical support for the ability of forward rates to explain differences in expected returns across corporate bonds. In addition, the research found that, if a company’s stock underperforms the equity market in a given month, its bonds tend to underperform the bond market in the next month. While we have for many years evaluated a bond issuer’s recent stock price as part of our credit investing approach, this finding contributes to our understanding of the relation between short-term equity returns and expected bond returns.
As the 2020s begin, we have entered new areas, such as mortgage-backed securities, where TRACE reporting requirements make the market more transparent. We are also preparing to meet the challenges of conducting peer-to-peer trading in Europe.
Change is a constant, and we expect the evolution of research, market microstructure, and client needs to create new opportunities for investment innovation. At Dimensional, we look forward to continuing this tradition of innovation—in fixed income and elsewhere. It is key to our mission of delivering the best solutions we can to investors.
1. Eugene F. Fama, “Forward Rates as Predictors of Future Spot Rates,” Journal of Financial Economics 3, no. 4 (1976): 361–377; Eugene F. Fama, “The Information in the Term Structure,” Journal of Financial Economics 13, no. 4 (1984): 509–528.
2. Eugene F. Fama, “Forward and Spot Exchange Rates,” Journal of Monetary Economics 14, no. 3 (1984): 319–338.
3. Wei Dai, Joseph Kolerich, and Douglas Longo, “Pursuing Higher Expected Returns with Duration Constraints,” Dimensional Fund Advisors, October 2017.
4. Dimensional’s benchmark-relative strategies are managed to consider the duration of a specified index without seeking to match the index’s holdings or other specific characteristics of the index.
5. Marlena Lee, Philipp Meyer-Brauns, Savina Rizova, and Samuel Wang, “The Cross-Section of Corporate Bond Returns,” Dimensional Fund Advisors, February 2020.
Forward interest rate
The yield and expected capital appreciation of a bond based on current market prices.
A measurement of a bond’s price sensitivity to interest rate changes. Generally, higher duration bonds are more interest rate sensitive than lower duration bonds.
A graph that plots yields at a specific point in time of bonds with similar credit quality but different maturity dates. A curve’s slope can vary through time based on changes in the shape of the curve and the level of yields. Generally, curves can be upward sloping, downward sloping, or flat.
Spot exchange rate
The current settlement price to exchange one currency for another in the market.
Investing in financial instruments intended to offset the impact of exchange rate fluctuations on bonds traded in a foreign currency.
A portfolio’s general level of interest rate risk as reflected in its average duration.
The average duration range allowed in a portfolio, which is usually specified relative to a market benchmark.
The secondary market where buyers and sellers trade bonds directly or through a dealer that maintains an inventory of bond issues.
Bonds with a relatively low risk of default, as indicated by their credit rating. Bonds rated AAA, AA, A, and BBB are considered investment grade.
The yield difference between bonds of similar maturity but different credit quality. A narrow spread indicates a smaller difference between yields of higher and lower credit quality bonds. A wide spread indicates a larger yield difference between higher and lower credit qualities.
The evaluation of specific bond issues and quantities held by a bond dealer.
Trade cost analysis
Describes various methods for assessing an investment manager’s trading efficiency.
An asset’s ability to be bought or sold quickly without a significant change in its price. The US Treasury bond market tends to offer the highest level of liquidity to bond investors.
The difference between the highest price a buyer is willing to pay for a bond (bid) and the lowest price for which a seller is willing to sell it (ask).
Bonds with credit ratings below those of investment grade (BB and lower).
Eugene Fama is a member of the Board of Directors for, and provides consulting services to, Dimensional Fund Advisors LP.
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