Why Fixed-Income Factor Research and ML Lag Equity
Summary
The document explains why factor investing and machine-learning applications are more common in equities than in fixed income. Bonds differ by maturity, liquidity, optionality, and capital-structure position, so securities from one issuer are less interchangeable and many leave the investable universe as they mature. Bond pricing is also relatively opaque, and trading is less electronic, often costlier, and harder to short.
Investor objectives and return patterns also differ: some fixed-income investors prioritize coupon and principal payments, while benchmark rules can prompt forced trades. The cited discussion says bond returns are driven largely by systematic interest-rate and credit risks, with idiosyncratic risk becoming more relevant toward high yield. Equity research benefits from many listed stocks and standardized data; government bond markets have fewer issuers, while corporate bond data are less standardized because bonds trade over the counter and have varied terms. The document points to yield-curve modeling as one fixed-income application, but offers no direct comparative test of ML performance and presents these explanations as reasons for a research gap, not proof that fixed income lacks useful factors.
Key ideas
- Bond securities from one issuer can differ substantially in maturity, liquidity, optionality, and capital-structure priority.
- Fixed-income markets are generally less electronic, less transparent, and costlier to trade than equity markets.
- Interest-rate and credit exposures explain much of the variation in bond returns, while idiosyncratic risk rises toward high yield.
- Equity research benefits from broad cross-sections and standardized data, whereas bond data and instruments are more heterogeneous.
- Factor and machine-learning methods can still be applied to fixed income, including yield-curve modeling.
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# ML/AI in fixed income vs equity # ML/AI in fixed income vs equity From my perception of learning different ML/AI applications to finance I found there are lots of them in equity and not as many in fixed income. I wonder if the markets are different in some ways and why there is more done in equity than FI. For example, factor investing, is so well established in equity and yet I could not find any widespread research in FI. Every example in the book is also usually uses it as application to equity market. Why not fixed income: bonds, credit, etc? ## Answer by Sharad (score 3, accepted) https://quant.stackexchange.com/a/71878 While there do not appear to be any book-length treatments of factor investing applied to fixed income markets (as of August 2022), there are journal articles/papers (some of which are linked below) that discuss this topic in a fair amount of depth and also provide some (brief) arguments as to why adoption in the FI markets has lagged the Equity markets. These arguments somewhat tangentially also apply to the relative dearth of ML/AI approaches in FI versus Equity. The top-line summary is that fixed income markets are more complicated. Here’s a more detailed summary based on the papers cited below (note the overlap with the answer given by fes): Differences in market structure - Fixed income securities are inherently more complex than equities. For example, while equities of one issuer are interchangeable, bonds are typically not. Specifically, bonds of the same issuer can have different maturities, levels of liquidity, embedded optionality and can represent different parts of the capital structure. Moreover, bonds have finite lives and many disappear from the investment universe after five years. - Relatively opaque pricing - Equity markets are more electronified, have lower transaction costs, and offer deeper shorting liquidity relative to bonds (with the potential exception of certain government bonds). Differences in investor behavior - Equity investors generally target price appreciation while there’s a substantial segment of fixed income investors who may simply target timely coupon (and principal) payments. Furthermore, the relative share of fixed income investors who target coupon versus price returns is a function of interest rates. - The somewhat ad-hoc rules associated with benchmark fixed-income indices drive suboptimal behavior from typical FI investors (e.g forced selling of downgraded bonds or of shorter dated bonds that fall outside of the indexes). Differences in Return Profile - Compared with the equity market where idiosyncratic risk constitutes a significant proportion of a stock’s total risk, fixed income market returns are affected predominantly by systematic risk: Studies have shown that, on average, 67% of fixed income managers’ active returns can be explained by exposures to systematic risk factors. Interest rate risk and credit risk together account for nearly 90% of cross-sectional differences in bond returns. However, idiosyncratic risk as a proportion of total risk does tend to increase as investors move from one end of the credit spectrum to the other (government bonds to high yield). References https://caia.org/sites/default/files/why_should_investors_consider_credit_factors_in_fixed_incomes.pdf https://www.spglobal.com/spdji/en/documents/research/research-factor-based-fixed-income.pdf https://www.invesco.co.uk/dam/jcr:22ff5aa8-de4b-4ea8-b758-996d31c14123/inv-factor-investing-in-fixed-income-inst.pdf ## Answer by fes (score 3) https://quant.stackexchange.com/a/71823 These techniques are more popular for the equity market, especially in simple book examples, likely because: - There are thousands of stocks just for the US market alone and the techniques can be useful for understanding how they behave in the cross-section. - Availability of standardized data sources. For government bonds you cannot do similar analysis in the cross-section as there are less issuers (one for the US market!). Factor models can still be useful for modelling the yield curve though and applications of machine learning do exist, e.g. (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3232721). For corporate bonds the data sources are less standardized as bonds trade OTC and the same firm might issue bonds with different maturities, coupons and possibly other features. The credit pricing should also at least theoretically reflect similar factors than the pricing of the issuer’s stock, so it is not clear if we need separate factors for these bonds on top of equity factors. A possible additional reason is that the equity market has more popular interest since it tends to offer higher returns (unlevered).
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