- 31 dec.Vector 2021 Annual Review
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- 17 aug.Chinese crackdowns
- 22 jul.Vector 2021 Semi-Annual Review
- 25 jun.Why we still like value
- 25 mei'Transitory' Inflation
- 22 apr.Reversal to the mean?
- 17 mrt.Vector's take on sustainable finance
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- 19 feb.David versus Goliath: An analysis of 2020 stock market performance
- 30 dec.Vector 2020 Annual Review
- 20 nov.Factor momentum
- 20 okt.How will the US elections influence your portfolio?
- 25 sep.Are better times for quant investing on the horizon?
- 26 aug.Fama/French going through its biggest drawdown since 1963
- 17 jul.Vector 2020 Semi-Annual Review
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- 31 dec.Vector 2019 Annual Review
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- 31 dec.Vector 2018 Annual Review
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25 mei 2021
Equity markets posted another strong month in April. The MSCI All Countries added 1.9% to its year-to-date gains and now stands at a 10.9 profit%. While the economy is clearly heating up, investors are increasingly wondering to what degree the stimulus cheques that supported this growth won’t just translate into inflation. US-inflation spiked to a year-on-year increase of 4.2% in April. While a substantial part of the increase was driven by the surge in gasoline prices (+49.6%), a component of the CPI which is unlikely to spur the Fed into action, the news led to a small sell-off when it hit the markets nonetheless.
In this newsletter we will take a look at how different inflation regimes have historically affected the performance of various sectors and investment styles. In order to conduct this experiment, we make use of Robert Shiller (CPI) and Kenneth French’s (Sector & Factor Returns) databases, which go back to 1926 for the United States.
We divide these 95 years of data into two regimes based on the year-on-year change in inflation (*). When the inflation figure is above the sample’s median value (2.7%) we label the period “High inflation”, otherwise it is labelled “Low inflation”. This gives rise to the following graph, where the high inflation periods are marked in grey. The great inflation (1965-1982), for instance, which was caused by the excessive growth in the supply of money can easily be observed below.
Subsequently we calculate the excess returns of 30 sectors and 5 factor families during both “high” and “low” inflation regimes. The results are clear: sectors that offer goods which people need at all times (food, utilities, oil) are able to pass through the inflation to their customers and outperform the market substantially during periods with higher inflation. For instance, while the tobacco industry underperformed the market by 3.8% per annum during low inflation regimes, it outperformed the broad market by 8.2% p.a. when inflation was above its long-term average. This leads to a return differential of 12% between the high and low inflation subset for the “Smoke” sector. Cyclical industries (Automobile, Resources…) or industries where the profits lie far ahead in the future (IT, gaming) follow the exact opposite pattern. Either they cannot fully pass on their increased input prices to their consumers. Or alternatively, in the case of high growth stocks, the present value of their future earnings declines as these are discounted at increasingly higher interest rates.
Within factor styles we see that all factors, with the exception of SMB (Small minus Big – the size factor) perform better when the economy is heating up and inflation is on the rise. This is good news for quant investors if the spike in inflation would turn out not to be transitory after all.
Our funds slightly lagged the market in April as growth stocks made a small comeback. With the announcement of the CPI figures however, the rotation trade is again in full swing. During the first two weeks of May, our funds were once more able to fully profit from the rotation from growth to value stocks, and from recovering factor returns in general.
Werner, Thierry & Nils
(*) We lag this figure by a quarter as the information usually is made available to the market with a certain delay. For instance, the y.o.y. inflation as at 07/1926 is calculated as the change in CPI between 04/1926 and 04/1925. This is also the reason why the graph isn’t shown in grey at the very end of the sample.