Stagflation is hitting the headlines again: high inflation and central bank tightening are already leading to rising yields and negative bond returns. At some point, though, higher yields and weaker growth will begin to offer opportunities for bond investors. To capture these opportunities when they arise, while also protecting portfolios against rising yields, active duration management is needed. But how do we know whether a strategy that was successful in recent decades will also work in a completely different environment?
To answer this question, we use the long-term back-test described in our academic paper ‘Predicting Bond Returns: 70 Years of International Evidence’. The back-test demonstrates that quant duration management works in an environment of high inflation and weak growth. Typically, it works best when bond markets move most – a positive finding given that the current environment is likely to cause significant market moves.
Stagflation risk has risen
The risk of stagflation is clear as commodity prices have surged due to the war in Ukraine, and energy supply is threatened. US inflation has already reached levels unseen since the early 1980s and growth will be weaker as high energy prices hurt disposable income and force energy-intensive companies to reduce their production. Strict lockdowns are again implemented in economically important parts of China, while Covid-related supply chain disruptions and chip shortages are already limiting growth. Fiscal policy might cushion demand, but the resulting higher bond issuance could push yields even higher. Monetary policy has supported the growth rebound from the 2020 lows, but this support is now being withdrawn.
Back-test using a deep historical dataset
Robeco’s Dynamic Duration strategy aims to protect against rising yields and benefit from declining yields. Since its inception in 1998, the duration positioning of this strategy has been determined by a quantitative model. When the research leading to the creation of this model was performed in the early 1990s, the limited availability of historical bond market data was one of the challenges. Most academic research on bond market timing uses data sets starting in the early 1980s, a period characterized by a secular decline in interest rates. Periods of high inflation or stagflation are rare in these data sets and, as a result, evidence for bond market timing in such conditions was scarce.
In recent years, however, a much wider set of historical financial market data has been made available, thanks to researchers digitalizing archives of exchanges, central banks and newspapers. We recently published a paper in which we present evidence for bond market timing using a deep historical dataset. This back-test includes decades with higher inflation and periods of stagflation. In this article, we use the dataset from that paper to analyze the performance of bond market timing in different growth and inflation regimes, including periods of stagflation.
Figure 1 | US inflation, 1950 – February 2022
Source: Bloomberg, Datastream, Global Financial Data
Figure 1 shows the development of US inflation from 1950, and we can see immediately why we need to examine more than just the most recent decades: current inflation is higher than anything seen since the early 1980s. The combination of high inflation (above 4%) and a recession is very rare in recent decades, occurring in less than 5% of the time after 1982.
Our deep sample allows us to better study stagflationary environments: these occurred 23% of the time in the 1950-1982 period, mainly between the late 1960s and the early 1980s. We use a global dataset that includes six large bond markets (the US, UK, Germany, Japan, Australia and Canada) to study bond market timing all the way back to 1950.
Performance in times of stagflation
Our academic paper shows that bond market returns can be predicted using a combination of four variables: bond trend, yield spread, equity return and commodity return. This finding is robust over markets and over time periods. Figure 2 shows the cumulative back-tested performance of this strategy.
Figure 2 | Cumulative back-tested performance, 1950-2019
This figure indicates that the back-tested performance is good in the period with rising yields before 1982, as well as in the period of the secular decline in yields after that. In the paper, we provide formal evidence that the strategy’s performance is good in both these sub-periods, in recessions and expansions, and in periods of high inflation and low inflation.
In this article, we now take that analysis one step further by classifying periods based on a combination ofgrowth and inflation. We distinguish periods with low, moderate and high inflation (below 2%, between 2% and 4%, and above 4%) in recessions, and do the same for expansions (following the same classification of recessions as in our academic paper). Figure 3 shows the model performance in these 3 x 2 = 6 categories.
Figure 3 | Model Information ratio in times of recession and expansion, and different inflation levels
Source: Robeco. Period: 1950-2019
The blue bars denote the information ratio of the bond market predictability strategy in recession periods, the grey bars in periods of expansion, with the bars (from left to right) showing the results in periods with low, moderate and high inflation. The figure shows that the back-tested strategy worked in all environments. Bond market predictability is especially strong in periods of stagflation, where inflation is high in a recession.
本文由荷宝海外投资基金管理(上海)有限公司(“荷宝上海”)编制, 本文内容仅供参考, 并不构成荷宝上海对任何人的购买或出售任何产品的建议、专业意见、要约、招揽或邀请。本文不应被视为对购买或出售任何投资产品的推荐或采用任何投资策略的建议。本文中的任何内容不得被视为有关法律、税务或投资方面的咨询, 也不表示任何投资或策略适合您的个人情况, 或以其他方式构成对您个人的推荐。 本文中所包含的信息和/或分析系根据荷宝上海所认为的可信渠道而获得的信息准备而成。荷宝上海不就其准确性、正确性、实用性或完整性作出任何陈述, 也不对因使用本文中的信息和/或分析而造成的损失承担任何责任。荷宝上海或其他任何关联机构及其董事、高级管理人员、员工均不对任何人因其依据本文所含信息而造成的任何直接或间接的损失或损害或任何其他后果承担责任或义务。 本文包含一些有关于未来业务、目标、管理纪律或其他方面的前瞻性陈述与预测, 这些陈述含有假设、风险和不确定性, 且是建立在截止到本文编写之日已有的信息之上。基于此, 我们不能保证这些前瞻性情况都会发生, 实际情况可能会与本文中的陈述具有一定的差别。我们不能保证本文中的统计信息在任何特定条件下都是准确、适当和完整的, 亦不能保证这些统计信息以及据以得出这些信息的假设能够反映荷宝上海可能遇到的市场条件或未来表现。本文中的信息是基于当前的市场情况, 这很有可能因随后的市场事件或其他原因而发生变化, 本文内容可能因此未反映最新情况,荷宝上海不负责更新本文, 或对本文中不准确或遗漏之信息进行纠正。