Value investing is buying securities that trade below their “intrinsic value”, which is derived from a company’s fundamental qualities such as assets or earnings, and holding them until that value is realised in the market price. The ‘value factor’ was a reliable predictor of excess returns for stocks up until 2013 but has disappointed since. Several theories have sought to explain the shift, including low interest rates, changes in the drivers of economic growth and in the definition of industry sectors, as well as the rising value of intangible company assets. While studies have found it hard to pin the underperformance on any single one of these, it seems investors can no longer be as confident that value investing in equities works as well as it once did.

Value investing in bonds

In bond investing there is no widely accepted definition of value, as there is in equities, but the concept is the same. Investors seek to identify the relative value of bonds compared to some measure of inherent or structural issuer risk. Simple metrics can work. Credit spread per turn of leverage is one key valuation score for corporate bonds that has proven predictive power, for example. But some investors use a slightly more complex approach to identifying value, for instance based on the residual value of a regression that controls for sector and credit rating. Either way, most approaches have yielded the same results - value investing in bonds has consistently generated excess returns, even over the past decade when value stocks have struggled versus growth stocks.

In 2019, Fidelity developed a multifactor approach to investing in corporate bonds that includes a valuation score to help construct portfolios that outperform the market. The aim is that by exploiting this factor, among others, and reducing the reliance on discretionary portfolio management, the strategy can deliver consistent excess returns for a lower management fee. Extracting the performance of the value factor from this process shows just how persistent and consistent its performance has been within the US bond market*.

Moreover, the value factor dominated the momentum and quality factors in our back tests - contributing to over 70 per cent of overall returns for both US investment grade and high yield bonds. This shows value’s historical importance and, unlike in equities, there is greater confidence that its predictive power will persist. The extra complexity of fixed income investing with its many dimensions to issuer and bonds types, trading and investor preferences means there are many more and larger inefficiencies to exploit for returns. Factor investing in fixed income is also only in its early stages and is nowhere near as dominant as in equities, where some risk premia appear to have been arbitraged away, or at least reduced.

However, the dominance of the value factor in bond markets is not assured, and different factors work better at different times. Therefore, a smarter approach to factor investing can help to improve performance outcomes. Pre-selecting factors can lead to biases such as fitting the model to historical data that may no longer be so important. Instead, Fidelity’s multifactor algorithm flexibly weights factors to maximise the expected risk-adjusted return of the portfolio. Fundamental credit research by a team of in-house analysts is also a unique input to the process, which balances other quantitative factors. The result is a strategy that benefits from exploiting the value premium but is also adaptive to changing markets. This provides a smoother return profile than an approach that simply relies on one source of return.

Rumours of value investing’s death are exaggerated, and it is alive and well - in fixed income. But any factor’s relevance can wax and wane over time, making a flexible, multifactor approach a more reliable strategy for investors. 

 *Fixed income returns are calculated using a hypothetical 5-year bonds based on our proprietary credit curves. These returns are also adjusted by normalising by rolling cross-sectional dispersion, are scaled by starting OAS, and have caps and floors applied to reduce the impact of outliers. This is illustrative information that we use to show how the different factors have performed, it is not directly comparable to the final multifactor portfolio. The portfolio uses the combined multifactor score as well as bond level factors and transaction costs in an optimisation to produce the multifactor portfolio.