Investing During Inflation

Recently I have been looking at whether inflation might be in the pipeline. The jury is still out on that, but caution would be wise given the current situation.

That leads to a rather obvious question: what should investors do during an inflation?

First off, if we are to be naive stock investors, how much does inflation impact stock market returns? We can see the impact in the following chart.

But this chart simply does not capture the pain investors feel during proper inflations. In order to get a sense of this, let’s zoom in on the ‘decade of hell’ – the inflation period 1972-82.

As we can see, basically all of investors’ nominal stock market gains were wiped out during this inflation.

Were there alternatives? Many will point to commodities and their derivatives. I think that this is misleading because, in large part, the 1970s inflation was driven by rising commodities prices. There is no guarantee that inflations will always be accompanied by rising commodities prices.

Okay, so let’s just stick to really basic alternatives. What do they look like? Here are real returns by asset class sorted for various rates of inflation (and including the ‘decade of hell’).

Faced with this, the S&P500 looks like a pretty good bet.

So, if we abstract from alternative asset classes, do we just have to sit content with either low or stagnant stock market returns? Maybe not. There is a chance that we could juice them a little by chasing factors.

The following measures what might be called ‘factor enhancement’. That is, the ratio of the average factor component in a given inflationary regime relative to the total factor average over the whole time series. So, any reading above 1 indicates that the factor is ‘enhanced’ in a given inflationary regime.

Interestingly, even at a first cut, it appears that utilising factors during high inflations may be enough to juice a portfolio. Momentum (Mom) and value stocks (HML) seem to be pretty good bets during inflation; while small caps (SMB) have their day in the sun in very high and moderate inflation.

During the ‘decade of hell’ tilting your portfolio toward the three factors could have substantially eased the pain of low returns.

Update: I have expanded this analysis to the quality factors here.

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FX Crises and the Trajectory of Interest Rates

In this post, I want to get a sense of foreign exchange crises since 2008. The data that I am using is taken from the World Bank. It is not perfect. It is a bit spotty and could be improved upon. It is also annual data, so it will not pick up intrayear crises. But it is solid enough that it should give us a good first cut on the dynamics of foreign exchange crises.

The first question to ask is simple enough: how many foreign exchange crises were experienced in this time period? For our purposes, we will define a foreign exchange crisis as any time a currency fell by 15% or more in a single year. Here are the number of such crises in every year since 2008.

The next feature we want to explore is whether these crises were resolved or not. A foreign exchange crisis is – in developing countries at least – accompanied by a sharp rise in interest rates. Central banks often do this because the foreign exchange collapse triggers inflation. The policy has the additional benefit – although I would say the primary benefit – of attracting foreign capital to prop up the exchange rate.

In a country that gets its foreign exchange crisis under control, this interest hike should be a temporary phenomenon. After a year or two, the currency should stabilise and inflation should fall. The interest rate can then be lowered to allow domestic economic activity to pick back up at the new equilibrium exchange rate.

To detect this we will take each foreign exchange crisis and see by how much interest rates fell two years after this crisis. So, a strong positive reading indicates that the crisis was resolved and interest rates fell over the next two years. While a strong negative reading indicates that the crisis worsened and the country had to keep raising interest rates to get the currency and inflation under control.

We can also plot the number of succesful resolutions by year. We define this as any country that saw interest rates decline by 300bps in the subsequent two year period.

This is a very different looking chart to the overall number of exchange rate crises. Between 2008 and 2018 there were a total of 125 exchange rate crises using our definitions. In this time period only 22 of them were resolved in a resoundingly successful way.

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Is China Facing a Minsky Moment?

As Evergrande looks about to default on its debt many are asking whether this might be a Lehman moment for China. That is, are we about to see a wave of defaults that bring down the Chinese financial system as we did in Western countries in 2008?

Much of the discussion is based on a misunderstanding. The Western financial system as it stood in 2008 was a largely laissez-faire system. There were, of course, regulations in place and there were also protections – most notably, deposit insurance. But ultimately, the system was basically market-based.

This is what led to problems. When banks saw their loan-books turn sour they were faced with the very real possibility of default. True, the central banks – and some misguided governments., like Ireland – ultimately bailed them out, but this was a discretionary policy.

The Chinese economy is not a laissez-faire system, however. It has aspects of laissez-faire. The Chinese largely allow markets to distribute consumer goods, for example, in contrast to the old Soviet bloc economies. But much of the rest of the Chinese economy is firmly controlled by the government.

In practice, this works by the Chinese government effectively ordering state-owned companies to engage in large-scale investment spending. This investment spending creates jobs and this job creation generates consumption. The financing for this investment comes out of the banking system, which is almost completely controlled by the government.

Much of this investment spending does not prove profitable. So, the loans go sour. This is not a new phenomenon. We saw something identical in the 1990s. Here are the rates of non-performing loans (NPLs) that resulted from the massive state-led investment campaigns of the 1990s – taken from an excellent 2006 paper from the Bank of International Settlements.

As we can see, the numbers were enormous. NPLs accounted for some 36% of Chinese GDP in the 2000s.

Why then did the Chinese not have a 2008-style financial crisis? Well, the government simply got rid of the NPLs. They set up so-called ‘asset management companies’ (AMCs) and these vehicles took the NPLs off the bank balance sheets. What happened to the NPLs? Effectively they were flushed down the memory hole.

If you think this sounds like a shell game, then you have to recognise the reality that fiat-based monetary systems are always a sort of shell game. So long as the banking system is unconstrained in the amount of money it can create, and so long as the banking system (and the central bank) are under control of the government, then bad loans can always be flushed down the proverbial toilet.

In 2008, what did such damage to Western economies – as opposed to Western financial systems – was the drying up of investment spending associated with the real estate market. This led to the recession and economic stagnation that followed.

Will the collapse of Evergrande lead to lower investment spending in China? That is up to the government. If the government decide to tell the state-owned enterprises to turn off the investment taps, they will. But why would the government do this – after all, it would result in recession and unemployment?

Perhaps the Chinese government will order the state-owned enterprises to stop investing so much in building real estate. They could focus their investment spending elsewhere, of course – possibly to military build-up now that the Australians are beefing up their submarine defences. Or the Chinese government may just keep beefing up real estate investment. Ultimately, it is up to them.

When Western commentators say that this investment is building ‘roads to nowhere’ and that it will all collapse eventually, they are implicitly assuming that the Chinese economy is a market-based system and that market discipline will inevitably prevail. This is simply incorrect. Because, as I said before, the Chinese economy is, for the most part, not a market economy.

Ultimately, if you want to know what is going to happen to the Chinese real estate market in the coming years you should just as Xi Jinping and his lieutenants. Because it is their decision to make.

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Inflation, the Quality Factor and Distribution

In a previous post I undertook a very simple analysis to show that factor investing during inflation helps investors to stop from simply treading water – at least, if history is any guide. An interlocutor asked if I’d looked into quality factors and I said that I would get around to it.

In fact, there is something very interesting in the quality factor analysis – something that highlights an aspect of inflation that is not properly appreciated by most economists and investors.

First, let us take a look at how returns stack up during inflations for the standard Fama-French quality factors. Once again, I will be using my ‘factor enhancement’ ratio – that is, the ratio of average returns during various levels of inflation relative to the total average return for that factor. Here are the results.

Note that we have taken seperate deciles, more on that in a moment.

First of all, what is the broad picture? Before getting into it we should note that this data only runs from the late-1960s. So, we only really have access to one inflation. That said, a striking trend emerges. Whereas the investment factor performs unusally well in inflations, the operating profitability factor performs unusally poorly.

This is in contrast to when we tested value, small and momentum, which all saw a boost from inflation. Ditto for utilising quality factors during the 1972-82 inflation: using the investment factor would have been a big winner, while using the profitability factor would have been a big loser.

Why is this? This is where the deciles come in to give us a hint. As the reader can see in the above, the negative impact on the profitability gets worse the smaller the decile used. The numbers above do not quite do justice to this effect, which can bee seen in the chart below.

The inflationary period 1972-82 in this chart is marked in grey. As we can see, the inflation did such damage to excess returns in the 10% decile that this index never really regained its ground after this. Why?

Let’s step back and recall that what this index represents is: outperformance of companies with unusually high profitability. Why would these companies be so negatively impacted by inflation?

Put another way: we know that all of the high profitability companies are impacted negatively by inflation, but as we move up the ladder this impact gets stronger – what does this suggest?

Well, it appears to suggest that inflation has a negative impact on profit margins – and the higher the profit margin, the worse the impact. This brings us to a little known fact about inflation more generally: from a macroeconomic point-of-view, inflation is a distributionary phenomenon.

We can show this using a simple national accounting identity which runs:

This is no abstract economic theorem. It fits the data from the inflation and national accounting statistics perfectly, as can be seen in the chart below (wage share is inverted to make the picture clear).

Recall that in the national account, the wage share is the inverse of the profit share. Translating from national accounting language into investment language: the profit share is simply profit margins. So, we can say that profit margins are ultimately determined by the change in prices relative to the rise in labour compensation and productivity.

We will not go too much into this fascinating identity. Much could be said. For now, we just need to get a sense that profit margins are likely to be impacted by inflation.

All very good in theory, but what did macro-level corporate profits look like in this period?

A mixed bag, really. They did a round trip in the whole period. Needless to say: they were volatile.

That is more than enough for our purpose. It implies that profitability was shaken during this period. It is no surprise then, to see that firms’ chosen for their profitability saw their returns shaken too. With all this redistribution taking place via changes in both relative and aggregate price-levels, it would not be surprising at all to see redistribution of relative profit margins taking place between firms.

The lesson is clear: you probably do not want to rely on a factor during a period when the economic underpinnings of that factor become highly volatile. Hence why the more ‘concentrated’ that factor becomes – i.e. the further up the deciles we go – the more the volaitility of the economic fundamentals impacts the factor.

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Podcast on Inflation and Labour Shortages

I appeared on the Bullhouse podcast again to discuss some of the recent work I have done on inflation. I also talk about how the impact of COVID19 and the policy responses to it risk resulting in major labour shortages and a return to a 1970s-style inflationary regime.

Podcast: Inflation and Labour Shortages

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Forecasting Future Inflation Using Private Sector Indices

In our last post we saw that private sector indices for used car prices and for rent prices were highly predictive of future changes in the corresponding CPI component indices.

The next logical step is obvious: we should use this information to build an aggregate CPI index that factors in this forward-looking information to get a prediction of inflation over the next six months.

In all honesty, I was a little reticent to do this. Not to put too fine a point on it, but it is a pain in the rear to do this sort of forecasting. You need to strip the CPI series right down using the component weights and then rebuild it using the private sector forecasts and weighting them accordingly.

But, since inflation is such a hot topic right now, I figured it was probably worth doing this as I have not seen it done elsewhere. So, here are the findings. (See last post for forecast methodology).

First of all, here are the component forecasts using lagged Zillow Rent Price data and lagged Manheim Used Car price data.

As we can see, based on the lagged private sector indices, future rent price increases should accelerate and future used car prices increases should decelerate.

Now, here is what happens if we weight these indices and plug them back into a reconstructed CPI index.

What this tells us is that the accelerating rent prices largely offset the decelerating used car prices. This means that we should not expect CPI inflation to decelerate in the coming six months, but we should not expect it to accelerate either.

Of course, this forecast is based on forward-looking private sector indices for rents and used cars only. I have not tried to model the impact of rising house prices on the index – so-called ‘Owners’ Equivalent Rent’. I may do so in the future, but I would need to find a forward-llooking proxy for that too – probably from Zillow. I’ll see how much attention this series gets.

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Using Private Sector Data to Forecast Future CPI Moves

Back in early August, my old colleague James Montier and I released a White Paper on inflation. In it we argued that our baseline scenario was that we would see transitory inflation caused by the extreme supply shocks caused by the lockdowns. We drew an analogy to the end of rationing in Britain after WWII, where we saw temporary price increases in the markets for rationed goods.

Further, we argued that any sustained inflation would require a wage-price spiral. That is, in order for inflation to feed on itself, the price rises would have to give rise to wage rises that then spurred further price rises.

Since we wrote this, it appears that there are, in fact, anomalies in the labour market. Many service sector jobs appear to be finding it hard to get workers. Some are blaming this on unemployment benefits, but it seems far more likely to me that many workers are simply too frightened to return to work. It is well-established that the average person vastly overestimates the danger of the virus and so it is no surprise that a certain portion of the workforce are not willing to work. What is more, the ‘fear effect’ is even more pronounced in young people who typically work service jobs.

Anyway, we are not going to deal here with the potential for sustained, structural inflation. Needless to say, the risk is real. And the more transitory inflation we see, the greater the chance that a sustained inflation will take hold.

Private sector metrics of sectoral inflation have far outperformed public data in terms of predictive power. One of the core drivers of the uptick in inflation we saw in the second quarter of 2021 was used car prices. Yet if we were examining the Manheim Used Vehicle Index rather than the CPI we would have seen this two months in advance, as the chart below shows.

David Goldman – whose columns are a must read for anyone trying to get out in front of inflationary trends – spotted this in real-time.

Today we are seeing a new trend emerge – one that Goldman has again been ahead of the pack on: rent price inflation. If we look at the Zillow Observed Rent Index (ZORI) we are seeing rent prices rise sharply.

We can use this information to forecast future increases in the CPI Rent Primary Residence Index. First we need to figure out how far back the CPI rent index lags the ZORI. If we lag ZORI by 6 months we get a very nice fit – yielding an RSQ of 0.6454.

We can then use this to produce a forecast of the future CPI rent index.

So, how much of an impact will this have on the CPI index as a whole? Well, consider this: ‘Used Cars and Trucks’ makes up 2.75% of the total CPI index. And a sharp uptick in used car prices was able to have a major impact on the overall index.

‘Rent of Primary Residence’ makes up a full 7.86% of the total index – almost three times as much as used cars. That said, we should not expect rent prices to increase as sharply as used car prices and our forecast suggests that they will not. But this is still cause for concern.

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Podcast Appearence on the Bullhouse

Yesterday I appeared on the Bullhouse podcast with Kenna for a very interesting discussion about a wide range of financial and economic topics.

We discussed a number of different topics. Some of these were as follows:

  • Whether there is a property bubble in the US and internationally right now.
  • The potential bubble in the junk bond market and its implications.
  • Why the risks in the market for rental properties are idiosyncratic this time around.
  • How real estate is now a financial asset and why it should be thought of in context with a person’s broader portfolio.
  • The specifics of the London property market and the idiosyncratic shock we saw there last year.
  • The debt-for-equity swap and how low interest rates have not led to the ‘euthanasia of the rentier’ but rather has led to an evaporation of equity – both stock equity and now home equity – for the middle classes.
  • Why the private equity move into home markets is probably going to end up being a bad investment.

Real Estate Market with Philip Pilkington

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Systematic Detection of Housing Bubble – Part II: Linear Trends and Points-Based Detection

In the last piece on detecting housing market bubbles, I an through some of the problems with using standard-scoring. Here I will provide two solutions; one complex, the other simple. But before we move forward with this, we must understand one other problem when it comes to determining whether a housing bubble is indeed a housing bubble.

This problem is not, like standard-scoring, related to how we manipulate the data. Rather it is related to how we define a housing bubble itself. The best way to approach this is to give examples. Below I show two very different real house price series; one from Norway, the other from Ireland.

Here we see two series with two very different properties. The Norway series shows steady, upward growth in real house prices over the past 30 years. Whereas, the Irish series shows a boom, a bust and another boom.

Are we implying that if a series behaves like Norway then it is not a bubble? Not necessarily. It could easily be a bubble that has not burst yet – after all, our a priori assumption should be that house price growth should largely track inflation and so real house prices should be largely stable. But there could be reasons why real house prices rise in a certain country that is not due to a bubble.

Norway is an excellent example of this, because it has large oil reserves. These reserves render the population more and more wealthy through time, even if they do not increase their productive capacities. This could, in theory, result in ever increasing real house prices. Does this mean that Norway is definitely not experiencing a housing bubble over the past 30 years? No. But it provides a reasonable explanation for why they might not be.

Seeing a stable linear trend in real house prices over a long time horizon then, reduces our confidence that this trend is the result of a bubble. We can be fairly confident that rising Irish home prices are bubbly because we’ve seen this movie before in Ireland – hence the jagged data series. But when it comes to a country like Norway we cannot be nearly as sure.

The reader will probably have noticed that I included R-squared statistics in each chart. This is because we can use this statistic to get a sense of how tightly linear a series is. If we see an R-squared of 0.8 or over in a country’s real house price index, this makes us less confidence that rising real home prices are a bubble. This gives us one variable for our model.

But now we have to solve the problem with standard-scoring we explored last time. We can do this by taking our base from a point at which the series was stable. Typically when standard-scoring we use the mean and the variance of the entire series. But if this is throwing the results off, when can choose a more stable base. Unfortunately, this will give us statistics that are impossible to interpret in terms of actual standard deviation, but they will work much better when trying to detect worringly elevated real house prices.

Combing through the series, I found that the most universally stable base was between 1993 and 1996. This was after the Japanese and Swedish bubbles had deflated and before the infamous 2008 bubbles started to inflate in the late-1990s.

We can then assign a points system to determine whether there is a housing bubble in a given country. Because elevated prices are more important than linear trend, we will give this priority. Therefore we will assign two possible points on the basis of elevated prices. One point is assigned if the Z-score is higher than 20; the other if it is higher than 35. An additional point is assigned if the R-squared of the series in below 80 – this gives a point for a lack of linearity.

Here are the results (full results in the appendix).


Is there a simple number that tells us of a definite bubble? No. But if we accept our premises then the higher the score, the more likely that country is to be in a bubble.

So, the $64m question: are the world’s property markets bubbly right now? Based on our model we would say: yes. Of the 21 countries/regions under scrutiny, 12 have achieved a score of 2 or over – with 3 achieving a perfect score of 3. Only 9, by contrast, had 1 or less – and only 1 had a score of 0.

Finally, we would note a much simpler way to detect bubbles: by eyeballing the time series and making a judgement on whether or not it looks like a bubble or not. In the vein, we present the time-tested and much underappreciated practice of… just looking closely at panel data. (Varible codes below panels).

1 = ‘AUS’
2 = ‘BEL’
3 = ‘CAN’
4 = ‘DNK’
5 = ‘FIN’
6 = ‘FRA’
7 = ‘DEU’
8 = ‘IRL’
9 = ‘ITA’
10 = ‘JPN’
11 = ‘KOR’
12 = ‘NLD’
13 = ‘NZL’
14 = ‘NOR’
15 = ‘PRT’
16 = ‘ESP’
17 = ‘SWE’
18 = ‘CHE’
19 = ‘GBR’
20 = ‘USA’
21 = ‘COL’
22 = ‘ZAF’
23 = ‘EA’
24 = ‘EA17’
25 = ‘OECD’


Full results.

Zscr-93-96BaseRSQZ>20Z>35RSQ<80Bubble Score
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Systematic Detection of Housing Bubble – Part I: Z-Scoring and It’s Problems

In a recent piece in Newsweek that got some attention, I made the case that the United States is currently experiencing a housing bubble. The next logical question is obvious: are other countries? After all, the 2008 meltdown was a global crisis; the US was not alone in its housing bubble.

In order to try to detect housing bubbles ideally we would like some sort of systematic framework that we can deploy. The problems with using this approach when it comes to hosuing bubbles, however, are not widely appreciated. In this post I am going to highlight some of the core problems here and in the next post a will propose a remedy.

The most obvious approach to building a systematic housing bubble detection is to get access to a widely published time series for multiple countries and then subject it to standard-scoring (Z-scoring). Standard-scoring works by telling us how unusual a datapoint is relative to the other datapoints in the time series.

So long as we are comfortable with manipulating large datasets, this should be fairly straightfoward to do. OECD publish solid databases on a variety of housing price metrics for most of the developed world countries. This data mostly goes back to the 1990s, so the sample should – in theory – large enough to subject to standard-scoring. We will use real house prices as they tend to use the two most internationally comparable datasets (house prices and CPI).

Here is what the housing markets for major developed countries look like when we apply this technique.

At first glance this looks good: if my argument in the Newsweek article is correct, then we know that the US is experiencing a housing bubble and that seems to show up here as a 2.5 standard deviation event. But to take this at face value would be enormously misleading. We can see why if we take some of these time series raw and plot them.

Let us look briefly at three seperate countries with very different housing market dynamics. We will take Germany, which has not experienced a bubble and a crash since our data begins; Ireland, which experienced possibly the largest housing bubble in history in 2006-08; and the US, which had a large housing bubble in 2006-08 and which we suspect to be in a bubble again.

Let us comment country-by-country and compare these more intuitive readings with the more abstract standard-scoring readings above.

Germany’s housing market looks somewhat overvalued relative to history. But it might be a stretch calling it a full-on bubble. Yet if we turn back to the standard-score, Germany looked like the worst bubble in our entire dataset – the country’s housing market is experiecing a 3-standard deviation event (something that has a less than 0.13% chance of happening!). The reason that this is happening should be obvious to those who understand the mathematics of standard-scoring: since Germany’s housing market has been so ‘boring’ for the past 30 years, even a secular uptrend in prices will stand out as a highly unusual event.

Ireland’s housing market went nuts in the last cycle. We can see the ‘biggest bubble in history’ clearly: real house prices increased around 300% from their 1990s levels! Yet if we look at where Ireland is today it appears that they are once again in a bubble – albeit one not quite as bad as last time. Yet if we turn back to our standard-scores, Ireland is near the bottom of the list with only a 1.2 standard deviation event. Once again, if we understand the mathematics we will readily know what is going on: because Ireland has had such a wild ride in the past, datapoints that should register as highly unusual do not.

The United States is somewhere in the middle. We see the housing bubble clearly in the data. It is, as I wrote in the Newsweek piece, as bad as the last bubble. And lo and behold, this register perfectly well on our standard-scoring framework as a 2.5 standard deviation event.

These examples allow us to draw a general inference about using standard-scoring for housing bubble detection: standard-scoring only really works when the volatility – i.e. the standard deviation – of the datasets under examination are relatively similar.

But, to paraphrase Tolstoy, since all housing markets are bubbly in their own specific way the standard-scoring framework – applied naively – fails completely when trying to systematically detect bubbles in various housing markets. Lessons to be learned here about deploying standard-scoring generally, I should think!

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