Category Archives: Stock markets

From Main St. to Wall St.: The long run

What is the long-run relation between economic activity and the stock market? From Main Street to Wall Street analyzes, inter alia, this question. Here, I present some of the conclusions.

As mentioned in my previous post (link), From Main Street to Wall Street describes how economic activity influences financial markets, in particular stock markets. It distinguishes between the long-run and the shorter-run relation. The “long run” refers to decades, even centuries. The short run refers to months, potentially a few years.

This post presents some of the book’s conclusions with respect to the historical long-run relation between the economy and the stock market. This is both an intriguing and fascinating topic, and the conclusions are not always as one would have guessed a priori.

The relation between stock returns and economic activity

The intuition of many people is that stock returns are high when economy growth is high. This is a good starting point. I emphasize in my book, though, that things are not always as simple as that. The long-run relation between economic activity and stock markets illustrates this.

A key observation is that, in the long run, stock returns and economic growth do not go hand in hand, generally. This is counterintuitive for many people, and thus important to emphasize. People have this tendency to believe that when the economy is doing well, firms prosper, which should lead to high stock returns, right? I emphasize and demonstrate that – in the long run – the story is more subtle.

There are several ways to illustrate this.

In those parts of the book that deal with the long run, I typically examine data spanning the past 100 to 150 years. Let’s start with a simple illustration. The average growth rate of what is still the world’s largest economy, the US economy, has been two percent per annum over the past 150 years (average annual growth rate of US real per capita GNP). Has the average annual real return from the US stock market over the same period also been 2%? No(!), for sure not. The average annual real US stock return has been close to 7% over the same period.

A five percentage-point difference (7% – 2%) might not seem like a lot, but compounded over 150 years it is enormous. To illustrate, imagine you (or one of your great-grand parents) invested one US dollar in the US stock market in 1871. Imagine also that you, since then, have reinvested all dividends. I.e., you invested one USD in the stock market many years ago, held on to the investment, and reinvested all dividends. The cumulative value of this investment strategy would have turned into app. USD 15,000 after 150 years. This is even in real terms, i.e. after taking account of inflation. The long-run performance of the US stock market has been truly amazing. USD 1 has turned into USD 15,000.

What about the performance of the economy? If we normalize economic activity to USD 1 in 1871, i.e. similar to our investment of USD 1 in 1871, then the cumulative value of US economic activity (real capita GNP) is USD 17 after app. 150 years. Yes, I write 17, not 17,000 or something the like. This is a thousandth of the cumulative value of the stock market. The eventual cumulative value of an investment in the stock market seems disconnected from the eventual cumulative value of real economic activity. In the long run, there is an enormous difference between something that grows by 2% per annum and something that grows by almost 7% per annum.

Figure 1 illustrates how the value of an investment in the US stock market has developed over the last 150 years and contrasts it with the development in economic activity, normalized to USD 1 in 1871.

Figure 1. Development in the cumulative value of an investment of one dollar in the US stock market in 1871, inflation-adjusted, and real US GNP per capita, normalized to one in 1871.
Source: From Main Street to Wall Street

In such a graph – where the cumulative value of the stock-market investment dominates so clearly – it almost seems as if there has been no growth in the US economy over the past 150 years. This is not true. Over such a long period, two percent annual growth in economic activity is in fact amazing. A country that grows by two percent per annum over 150 years ends up being one of the richest countries in the world. So, two percent growth in economic activity is a lot when sustained over such a long period. It just pales in comparison with almost 7% growth in the value of a stock market investment.

Another way to illustrate the lack of a simple relation between long-run economic growth and stock returns is to compare the experience of difference countries. Intuitively, one might expect that a country that has grown a lot historically is also a country that has had high stock returns. Again, the data do not back up this intuition.

Figure 2 shows average annual real stock returns and growth rates of real GDP per capita for a number of countries since 1900. The countries are ranked according to the size of average returns.

Figure 2. Real per capita GDP growth and real stock returns for selected countries. 1900–2016.
Source: From Main Street to Wall Street

The US stock market has delivered the highest average annual return, at close to 7%. Has the US economy also been growing the fastest? No. During the past century, the fastest growing economy has been the Japanese. But the Japanese stock market has delivered only half of the return the US stock market has delivered. Similarly, the French economy has been doing fine, but its average annual real stock return has been mediocre. And so on. There is only little evidence that fast growing economies have also delivered higher stock returns.

Thinking deeper about this, it might not be that surprising after all. Stock returns are the sum of a risk-free return and a risk premium. The risk-free rate is in most economic models tied to the growth rate of economic activity. Hence, when we add a risk premium, developments in economic activity and stock returns depart. The question thus becomes what determines the risk premium. I discuss this in the book.

When making comparisons such as those presented here, it becomes relevant whether one uses arithmetic or geometric averages. The volatility of economic growth and stock returns should be discussed too. One can also look at other periods, other measures of economic activity, and so on. I deal with all this in the book.

The relation between stock prices and economic activity

Does the stylized fact that average long-run growth rates of economic activity and average long-run stock returns differ imply that stock markets are living a life of their own, i.e. without any connection to underlying economic activity? The answer to this question is also “no”.

Stock returns are given by stock price gains and dividend yields. It turns out that growth in stock prices (not returns) in the long run is related to long-run growth in economic activity. Many economic models and an abundance of empirical evidence find that stock prices, dividends (not dividend yields), and economic activity share common long-run growth trends. With a fancy word, we say that stock prices and dividends/economic activity are cointegrated. Cointegration means that two variables that grow over time are driven by the same underlying stochastic growth trend, implying that a linear combination of the two series (for instance the difference between the two) does not grow over time. Lots of empirical work has demonstrated that stock prices and dividends are cointegrated, starting with the by-now famous articles by Campbell & Shiller in the late 1980s (link). I have also done extensive research demonstrating cointegration between stock prices and dividends/economic (link).

In my book, I do not conduct statistical tests of cointegration and so on. It is not that kind of book. But I explain what it means and I illustrate it. To provide just one illustration here, Figure 3 shows the development in the ratio of US real stock prices (S&P 500) to US real per capita GDP over the past 150 years, normalized to one in 1871.

Figure 3. The stock-price GNP per capita ratio normalized to 1 in 1871 and its ten-year rolling average. 1871–2018.
Source: From Main Street to Wall Street

The conclusion from Figure 3 is that there is a tendency that the level of US stock prices and the level of US economic activity follow each other in the long run. The ratio of the two is normalized to “1” in 1871. It keeps being attracted to “1” throughout 150 years. There are fluctuations around the mean (“1”), but always a tendency to revert to the mean. The economic interpretation is that there have been (long) periods when the S&P 500 grew faster than economy activity (such as during the pre-WWI period, during the 1950s and 1960s, and during the past four decades), but such periods are followed by (long) periods when the economy grows faster than the stock market, such that the ratio of the two series reverts to its long-run mean. Over 150 years, stock prices follow underlying economic activity.

In the book, I discuss the international evidence, the relation between economic activity and dividends/earnings, and many other interesting topics. The bottom line is that stock returns are not related to the growth rate of economic activity in a simple linear fashion in the long run, as we otherwise might have thought, but stock prices relate to the level of underlying economic activity.

The last four decades

The final topic I would like to discuss briefly today is the fact that the valuation of stocks has soared, according to a number of metrics, during the past four decades.

Figure 3, for instance, shows that the S&P 500 currently trades at a historically high level relative to US GDP. If one relates stock prices to earnings or dividends, today’s stock-market valuation seems even more extreme. I show this in the book. Stocks seem “expensive”.

The final graph today, Figure 4, shows the total value of the US stock market in relation to total US GDP.

Figure 4. The value of the aggregate stock market relative to aggregate GDP.
Source: From Main Street to Wall Street

In popular writing, this is also called the “Buffet Indicator” (link). People use different measures of the value of the stock market when calculating this measure. I use the total value of US corporate equity. As is clear from Figure 4, during the past four decades, growth in the value of US corporate equity has outpaced growth in the US economy. Warren Buffet uses this to argue that future stock returns will be low, as stocks have now become “expensive” in relation to the value of economic activity. Other people (link) use it to argue that the rewards to economic growth have been reallocated from workers to equity owners (helping to explain developments in inequality). In my book, I discuss some of the reasons we have seen an increase in valuations, such as the growing use of share buybacks, low interest rates, and other contributing factors. I also discuss whether it indicates low returns going forward, something that we will also return to on this blog.

Conclusion

This post has discussed some of the topics that I deal with in the initial parts of From Main Street to Wall Street. Those initial parts examine the long-run relation between economic activity and the stock market. I can, though, only provide a few glimpses here. The book discusses many additional topics in relation to long-run growth and returns, such as what is actually economic activity, are people “happier” when economic growth is high, growth and inequality, stock return decompositions, measures of risk, risk aversion, the equity risk premium, and so on and so forth. You have to read the book if you want to know more about these exiting topics.

In my next blog post, I will turn to a few of the book’s conclusions regarding the relation between economic activity and financial markets over the business cycle. 

From Main Street to Wall Street

My book – From Main Street to Wall Street (link) – has been published. This blog post explains why I wrote the book and its contents. The next three posts (that will be sent out in due course) will present some of the analyses and conclusions from the book.

Background

Let me start in 2008. The financial crisis was at its worst.

I was a young professor of finance. I studied financial crises (my Ph.D. is on currency crises) and the relation between the macroeconomy and expected stock returns.

One day, I was invited to a TV program. I was asked to explain something in relation to the crisis. I do not remember the exact topic, but the appearance revealed that I have an ability to explain complicated financial issues in simple terms. There was a need for people with such skills, as many found it difficult to understand what was going on during the financial crisis. I decided that it was part of my duty as a professor at a publicly funded university to help people understand the complicated interactions between the financial sector and the real economy that were brought to surface during the financial crisis. Back then, I became something like an “academic TV star” in Denmark.

A couple of years after the crisis, in 2012, the then Danish government established a commission. Its task was to evaluate the causes and consequences of the financial crisis in Denmark. Due to my presence in the Danish debate and my research on this area, I was asked to be its chairman. I accepted the offer.

The next couple of years, I worked day and night. The report – today called the “Rangvid-report” (link) – was published in autumn 2013.

The decision to write a book

At some point in 2015, or was it 2016, i.e. a couple of years after the report, I started talking to myself about my next big project. With “project financial crisis” completed, I felt I somehow needed a new project. I decided to write a book.

I did not want to stress myself. I have been writing when time has allowed it. Sometimes, I have been able to write a lot. At other times, several months have passed when I was not able to write anything. This has been fine. No stress. In fact, an enjoyable process.

What should its topic be? The obvious topic would have been the financial crisis, given my involvement here. But, as mentioned, I had just completed writing the report of the financial crisis, and everybody else were writing books on the financial crisis. It should be something different.

I am and always have been fascinated by financial economics. Economic growth determines so many things in life. Financial markets help people fulfill some of their big dreams: buy a house, save for retirement. I have done research on the relation between the macroeconomy and financial markets. I decided the book should deal with this.

I am an empirical economist. I have done formal theory when I was young, but I have to come to realize that I do not have a comparative advantage here. Hence, I wanted the book to be a fact-based book on the relation between the economy and financial markets.

I started investigating the market. I was surprised.

Every day, newspapers and business magazines cover the latest macroeconomic news, not least to help investors navigate financial markets. Is falling unemployment good for stocks because it indicates the economy is doing well? Or, is it bad because the central bank will then start tightening monetary policy, potentially hurting stocks? Will countries expected to grow fast provide higher returns? How will economic developments affect financial markets?

Given the attention academics and investors devote to understanding the economy and its impact on financial markets, it was somewhat surprising to me that no well-known book focusing on the relation between the economy and the stock market, and targeted towards a broader audience, existed. I decided that my book should aim at filling this gap.

Of course, there are related books. The one closest to mine is probably “The long good buy” by Peter Oppenheimer (link). I like to think that my book is more thorough and has a firmer anchoring in the academic theories, given my background as an academic and Oppenheimer’s as a practitioner. Also, my book has this structure with the long and the shorter run. Lastly, and probably most importantly, I carefully explain how we can use the insights from the theories and historical evidence to formulate predictions about the future. This being said, Oppenheimer’s book is a great extra reading if you are interested in these topics.

Other related books could be “Stocks for the Long Run” by Jeremy Siegel (link), “Expected Returns: An Investor’s Guide to Harvesting Market Rewards” by Antti Ilmanen (link), and “Financial Markets and the Real Economy”, edited by John Cochrane (link). These are all great books, but they cover different aspects of the topic. Siegel’s book is a description of the stock market on the long run, whereas my book relates the stock markets to the underlying economy, over the short and the long run. “Expected Returns” focuses on providing a comprehensive overview of theories of expected returns on different asset classes, whereas my book focuses on the relation between the macroeconomic and equity markets, over the long run and the business cycle. “Financial Markets and the Real Economy” is a collection of academic articles written by many different authors, i.e. it serves a very different purpose and targets a very different audience than my book, even when the title indicates the same topic.

Writing style

The book is written in a style similar to this blog. Of course, this is a blog, so my language here is sometimes less formal than what I use in the book, but the use of tables and figures to provide a fact-based background to the relation between financial markets and the economy is similar to this blog.

I decided that my book should be academic in nature, but written in a style that is accessible to a broader audience. For instance, the book does not contain formulas or equations, except like “Real equity returns = Nominal equity returns – inflation, “Equity returns = dividend yield + capital gains”, and the like. The book presents the academic theories relating to the different topics, but in a way that should be understandable for interested non-academic readers and students.

An important characteristic of the book is its reliance on the data. Figures and tables richly illustrate and back up arguments and theories. The book is empirical in nature.

I wanted the book to be reasonably comprehensive. Its purpose is to give the reader a thorough understanding of how economic activity affects equity returns. Also, in some chapters, equity returns are compared to interest rates and bond returns.

All chapters end with a checkpoint list that summarizes key insights.

Table of Contents

A guiding principle in the book is that movements in economic activity contain a long-run and a business-cycle component. Over the long run, economies grow. Over shorter horizons – over the business cycle – economic activity fluctuates. My book examines both the long-run relation between economic growth and stock returns as well as the business-cycle relation.

After a first part that introduces fundamental stylized facts and important concepts, the remaining parts of the book are structured around the long-run and short-run relations. In my book, the long run refers to multiple years or decades. The short run to months or a few years.

Over the long run, economies grow. The rate at which an economy grows over the long run is the determinant of whether a country is rich or poor. Expectations to developments in economic activity over the long run help us formulating expectations to returns from financial assets over the long run. When a 25-year old individual asks how much he/she should save to live a decent life after retirement, i.e. after many years, the answer will not least depend on what we expect financial assets to return over the next many years. And, the answer to that question in turn depends on how fast we expect economy activity to grow over the long run. The second part of this book explains what we know about long-run economic growth, about returns on stocks over the long run, and about the relation between long-run economic growth and stock returns.

Over the short run, economic activity fluctuates. Sometimes even substantially so. There are years when the economy is booming, unemployment is low, and times are good. At other times, in recessions, economic activity falls, people are laid off, and times are rough. The recurrent alternations between good times and rough times is the business cycle. Economic activity and the stock market share a common business-cycle pattern. The level of economic activity and the value of stocks rise and fall jointly throughout the business cycle. In order to understand why stock returns are sometimes high and why they are sometimes low, it helps to understand business-cycle fluctuations in economic activity. The third part of the book explains how economic activity and stock returns stocks are related over the business cycle. This part also describes monetary policy, as monetary policy plays an important role in understanding business-cycle fluctuations and stock markets. This part of the book also devotes one chapter to the financial crisis of 2008-2009, as an example of an economic and financial event that dramatically influenced economic activity and equity markets.

When we understand how economic activity relates to financial assets, over the long run and business cycles, we are better equipped to formulate expectations to future returns on financial assets, over the short run and over the long run. This is what the fourth part of the book deals with. It explains how economists judge the outlook for the economy, and it takes us through short-run and long-run stock-return predictability. I emphasize that there is considerable uncertainty surrounding return forecasts, but also that stock returns contain a small degree of predictability. Even if it seems small at first glance, it accumulates and has important implications for academics and practitioners alike.

A final, short, section contains my view on a few practical investment advices.

Publication process

Oxford University Press is the publisher of the book. OUP has done a great job setting up the book. It looks good. The cover is also cool. I am happy.

I handed in the manuscript in spring last year, right before corona arrived. This means that I did not manage to include a chapter on the corona-crisis. If there ever will be a second edition, it will include such a chapter. Until then, many posts on this blog describe different aspects of how the corona-crisis has affected the economy and stock markets.

I received the book in print a couple of weeks ago. It is a nice feeling having published a book.

Conclusion

This post has provided some background on why I wrote From Main Street to Wall Street and its contents. My next three posts will describe some of its key conclusions. The first post will be on the long-run relation between the economy and the stock market, the next on the business-cycle relation, and the final on how we can use those relations to say something about expected future returns.

2020

It’s this time of the year. This post recalls events of 2020. It has been such an unusual year, so different from what we expected. Luckily, there seems to be light at the end of the very long and dark tunnel, and – I hope – that 2021 will be considerably more joyful than 2020.

2020 started out so well. The roaring twenties and all that. Wuhan was a city I had not heard of, corona a beer people tell me is best served ice-cold with a slice of lime (I do not drink beer, tough I do enjoy wine), and social distancing words we would only get to know too well. Today, we know that Wuhan is a Chinese city with more than eleven million inhabitants and a marketplace where it presumably all started, corona also means something terrible, and social interaction is an activity we have come to miss so dearly.

At the time of writing, app. 75 million cases of corona/COVID-19/SARS-CoV-2 have been confirmed globally and app. 1.7 million people have passed away because of corona. Most countries have been in lockdowns, many still are (again), and the social and economic costs of the crisis have been enormous.

I started this blog in April 2020. This had nothing to do with corona. I had wanted to set up a blog for some years (people ask me where I find time for this, and I really do not know, but seemingly I simply like writing economic stories and analyses). Starting the blog in April this year, however, naturally implied that many of the blog posts have dealt with various economic and financial aspects of the pandemic. In this post, I will review some of the learnings from 2020.

The worst recession on record. With the highest growth rate on record

The recession started in February 2020 in, e.g., the US. Initially, it was caused by a supply shock: lockdowns were imposed and firms could not sell their goods and services and households could not go shopping. In April, when the IMF released their Spring Outlook, they labelled it “The Great Lockdown”. This was a suitable label. The IMF also noted that “This is a crisis like no other” and that “many countries now face multiple crises—a health crisis, a financial crisis, and a collapse in commodity prices, which interact in complex ways”. As unemployment and bankruptcies increased, households and firm got nervous, and demand suffered, too.

The path of economic activity has been highly unusual. This graph shows the quarterly percentage changes in US real GDP since 1947:

Quarterly percentage changes in US real Gross Domestic Product. 2020 encircled.
Source: Fed St. Louis Database

2020 is very much an outlier. On average, from 1947 through 2020, real GDP has grown by 0.8% per quarter. Until 2020, quarterly growth had never exceeded 4%. Economic activity had never contracted by more than 2.6%. Then came the Great Lockdown. During the second quarter of this year, economic activity contracted by 9%. This is almost four times more than the otherwise worst contraction on record. In this sense, it was the worst recession ever.

It has also been the weirdest recession ever. During this recession, we have also witnessed the highest growth rate on record: economic activity expanded by 7.4% during Q3. This is twice as much as the otherwise highest growth rate on record.

This puzzling feature of the recession led me wondering what a recession really is (link). I expressed sympathy with members of the NBER Recession Dating Committee. They face a particularly difficult task this year. Should they conclude that we had one V in spring, with the recession ending in late April, and then a new V now, i.e. two separate Vs (VV), or that we have had one long recession with a double dip, i.e. a double-V (W)? Does it make sense to call it a recession when we experienced the fastest rate of growth in economic activity on record? If you conclude that the economy cannot be in recession when it expands at its fastest growth rate ever, then you must conclude that the recession ended during Q2. But, the NBER Recession Dating Committee has not called the end of the recession yet, i.e., officially, the recession is still ongoing.

You may ask why it is important to know whether the recession ended in April or whether it is still ongoing. The development in economic activity is what it is, whether we call it recession or not. It is important because a “recession” is such an important concept in economics. We inform the public, business leaders, students, and others about the characteristics and consequences of recessions. If a recession can contain the by-far strongest expansion of economic activity on record, we need to change our understanding of recessions.

The very unusual behavior of economic activity during Q2 and Q3 caused very unusual, and scary, developments in unemployment and related aspects of economic activity. This graph shows the monthly change in the number of unemployed in the US:

Monthly changes in the number of unemployed in the US. Millions. 2020 encircled.
Source: Fed St. Louis Database

During March, unemployment in the US increased by 16 million. Again, this was beyond comparison. Until March 2020, the number of unemployed had never increased by more than one million over one month. In March 2020, it increased by 16 million.

As the virus contracted during summer, unemployment fell. There has never been as fast a reduction in the number of unemployed as the one occurring during this summer. In May, the number of unemployed dropped by more than three million. Until May 2020, the number of unemployed had never fallen by more than one million over one month. In May 2020, it fell by more than 3 million. So, within a year, we have had the strongest-ever increase in unemployment, but also the largest-ever fall in unemployment. By far.

Such dramatic events happened all around the world. I documented this here (link) and here (link).

Inspired by these events, I did something admittedly nerdy. I calculated the probability that we would experience events such as these, given the historical data (link). I found the unconditional likelihood that we could see the increase in newly registered unemployed that we saw in spring to be 0.97 x 10^(-841). This is a zero followed by 841 zeroes and then 97. For all practical purposes, this is a zero-probability event. But it did happen. It was just very, very unusual.

The stock market

I use some of my time (a significant part, by the way) to try to understand the stock market. This has not been a straightforward task this year.

Today, the global stock market is 13% percent above its January 1 value, the US stock market is 18% higher, and the Danish stock market 29% higher (MSCI country indices). Given that we have been through the worst recession ever, and that the recession is not officially over yet, this is not what one would have expected prior to the events.

Then, on the other hand, in hindsight it is perhaps not so strange. The recession has been the worst on record, yes. But, we have also had the fastest growth in economic activity on record. I argued (link) that if we imagine that the recession ended in late April, when economic activity bottomed out, the behavior of the stock market fits perfectly well with the historical evidence on the behavior of the stock market.

Central banks have certainly played their role, too. When markets melted down in March, central banks intervened heavily. In contrast to the financial crisis of 2008, it was not banks that were in trouble this time, but firms. Firms could not sell their goods and services due to the lockdown. The limitless purchases of government bonds that central banks have become used to during and after crises thus probably did not do much good (evidence came out that central bank purchases of government bonds are less effective than we are often told, link ). What turned things around, instead, was the announcement on March 23 that the Fed would facilitate credit to firms (link). This was a new policy tool. It led to a complete turnaround of events. I produced this graph (I still think it is a supercool graph): 

Difference between yields on ICE BofA AAA US Corporate Index and 3-month Treasuries (Left hand axis) as well as the SP500 inverted (Right hand axis). Both series normalized to one on January 2, 2020. Vertical line indicates March 23.
Source: Fed St. Louis Database

The graph shows how the stock market lined up with credit spreads. Firms were suffering, and their credit spreads started widening, in late February. The stock market suffered. The Fed announced it would provide credit to firms on March 23. Credit spreads tightened. The stock market cheered. The graph summarizes how the Fed saved credit and equity markets. And, strikingly, the Fed did so by merely announcing they would intervene. Up until today, the Fed has not intervened a lot. In this sense, it was a “Whatever it takes moment of the Fed”.

It should be mentioned that the Fed announced other initiatives on March 23, too, such as the Main Street Lending Program (link) and the Term Asset-Backed Securities Loan Facility (link). The Corporate Credit Facilities were the ones that directly targeted corporate bonds, though. Due to the nature of this crisis, the stock market lined up with credit spreads during this crisis, as the above graph reveals, emphasizing the importance of the announcement of the Corporate Credit Facilities.

Eurozone troubles, or rather no Eurozone troubles

The fact that we have not had to talk a lot about the risk of a Eurozone breakup since summer has been a positive surprise. In spring, there was talk about the risk of a Eurozone crisis. Like so often before, Italian sovereign yields rose relative to German sovereign yields. There was reason to be anxious. I argued that “Some kind of political solution at the EU level would be needed” (link).

This we got. The European Union agreed on a “Recovery and Resilience Facility” (link) that includes both loans and grants. EU has moved one inch closer towards a common fiscal policy. Who will pay is not clear, but EU has shown solidarity. I believe this is positive. At the same time, the European Central Bank continued its interventions and bought a lot of Italian debt. This has kept yields on sovereign bonds low. Here is the Italian-German yield spread during 2020:

Italian yield spread towards Germany. Ten-year government bonds. Daily data: January 2, 2020 – December 21, 2020.
Data source: Thomson Reuter Datastream via Eikon.

Italian yields have been falling continuously since summer, when the EU agreed on its recovery plan. It is positive that we have not had to discuss Eurozone troubles. We have had so many other troubles. Whether this means that we do not have to discuss Eurozone troubles again at some point, I am less sure. But, that is for another day.

Banks have been doing OK

The risk of a Eurozone breakup did not materialize. Another risk that did not materialize was the risk of systemic bank failures. This is positive as well, as economic activity suffers so much more when banks run into trouble and credit consequently does not flow to its productive uses.

During the worst days in March, stress in the banking system intensified. For instance, the spread on unsecure interbank lending increased relative to secure lending:

The TED spread. Difference between 3-Month USD LIBOR and 3-Month US Treasury Bills. Daily data: January 2, 2000 – December 14, 2020. 2020 encircled.
Data source: Fed St. Louis Database.

Stresses lasted only a few days, though. During the financial crisis in 2008, on the other hand, spreads remained elevated for much longer. This time, trust in the banking system was quickly reestablished.

I think I am allowed to claim that this was one of the predictions I got reasonably right. In autumn 2019, when nobody knew about the upcoming crisis, I wrote a policy paper on the Nordic financial sector. It was presented in December 2019 and finally published in June this year (link). I argued that banks are safer today, compared to 2008. Some doubted my conclusion and said, “just wait until the next crisis, then you will see that banks are not safer today”. Well, few months later we had the worst recession ever. Luckily, though, we have not had bank-rescue packages and we have not had to bail out banks. Banks have been withering the storm. In some instances, banks have even been part of the solution by showing flexibility towards troubled firms. I am not saying everything is perfect, but I am saying that the situation has been very different from the situation in 2008. On a personal note, this made me happy, too, as it would have been somewhat embarrassing if banks had failed at the same time I published an analysis arguing that the banking sector is safer. This, luckily, did not happen. Instead, the banking system turned out to be far more resilient than in 2008, as I predicted.

You may add that the Fed rescued markets during spring, as mentioned above, and thereby rescued firms and subsequently banks. True, but there was certainly also tons of rescue packages in autumn 2008. Banks nevertheless failed in large numbers in 2008. They did not this time around. Perhaps, thus, we did learn something from the financial crisis of 2008, and have gotten some things right. This would be no small achievement.

US election and Brexit

There have been other events, for instance the US election and Brexit negotiations. In normal years, such events would potentially have been among the most important events for markets and the economy. This year, the pandemic has certainly been more important. I did manage to write a post on the US election and the stock market, though (link). I discussed evidence that stock markets perform better under Democratic presidents. Only time will tell whether the same will happen under Biden.  

I did not manage to find space to discuss Brexit, but we got a trade agreement on Dec. 24 (link). Hopefully, the EU and UK can now move on.

The cost of the crisis

It is impossible to summarize the pandemic in one number or one word. Hence, I will not attempt to do so. But, I did present a calculation of the expected cost of the crisis in Denmark (link). I arrived at DKK 336bn, or app. USD 10,000 per Dane. This calculation generated some attention in Denmark.

One can discuss every single assumption one needs to make when calculating the expected cost of a crisis: What is the value of a statistical life? What is the value of a statistical life of those who pass away due to COVID-19, i.e. who are typically above 80? What is the past loss as well as the expected future loss in economic activity due to the crisis? Does it make sense to present one number when there is so much uncertainty? And so on. These are all fair points, but if we want to have a meaningful discussion of the impact of the crisis, we have to start somewhere.

In my calculation, I closely followed the assumptions of Cutler & Summers, such that US numbers and Danish numbers can be compared. This allowed me, for instance, to conclude that the cost of the crisis in Denmark, most likely, will be much lower than the cost of the crisis in the US.

Conclusion

I must admit I find it difficult to end this last post of 2020 on a happy note. Right now, at the time of writing, the situation is bad in the country I live, Denmark, and in many other countries in Europe and around the world. Numbers of new cases and deaths have been rising recently, or are on the rise again, and more and more restrictions and lockdowns are being imposed. Days are grey and short. The crisis has already been tremendously costly and it is clearly not over yet.

Nevertheless, I will try to end the post on a positive note. It gives me hope that several countries have started vaccinating people, and it seems to be working well. Finally, the EU also starts vaccinating people now. This has taken way too long, however, given the severity of the crisis and the fact that other countries started weeks ago. And, yes, every day counts. If it is correct, though, and I deliberately write if, that the EU has failed when it comes to the approval process and purchase of vaccines, as the normally well-informed and serious magazine Der Spiegel claims (link), it is a scandal. Biden aims to vaccinate 100m Americans within his first 100 days in office (link), close to a third of the US population. As things look now, it seems unlikely that we will be able to achieve the same in Europe. Christmas is all around us, though, so let us hope that somehow things will develop in the right direction.

Therefore, let me focus on the bright side. With the jabs, the situation will most likely start to improve within a not too distant future. I will try to convince myself that I see weak light at the end of the long and dark tunnel, even when we probably have to wait many months before things really calm down. Days are at least getting longer. I will focus on this, then.

With this, which is meant to be a positive message, let me thank you all for reading this blog and for sending me many encouraging mails with feedback. Please keep on doing so – it is highly appreciated.

I conclude by expressing hope that next year will be considerably more joyful than the one we leave behind.

Happy New Year!

VV or W: When did (or does) this recession end?

This corona recession started in February 2020. Officially, it is still ongoing. But, perhaps, it has in fact already ended. This might seem confusing but it helps explaining the performance of financial markets during this “recession”.

In my soon-to-be-released book From Main Street to Wall Street (link and link), I – among many other things – carefully examine the historical relation between the business cycle and financial markets. I verify that stock markets typically perform considerably better during expansions than recessions. In the book, I examine and explain why this is so. I also explain that this is not a bulletproof finding. It is not always so. Sometimes stock markets do fine during recessions. Is this recession one of them?

This recession

In the US, the Business Cycle Dating Committee (link) determines peaks and troughs in US economic activity. On June 8, 2020, the Committee announced that:

“The committee has determined that a peak in monthly economic activity occurred in the US economy in February 2020. The peak marks the end of the expansion that began in June 2009 and the beginning of a recession. The expansion lasted 128 months, the longest in the history of US business cycles dating back to 1854.”

This means that this recession started sometime in February 2020. It also means that the expansion preceding this recession became the longest on record.

The Committee has not declared the end of this recession yet. Officially, the US economy is still contracting, with the caveat that the Committee determines business-cycle turning points with a lag. E.g., it was in June only that the Committee concluded that the recession had started in February.

How has the stock market performed during this recession? As readers of this blog know, it has performed well since March 23 when the Fed had is “Whatever it takes moment” (link). During this recession, the US stock market has returned 10% up until today (total return on the MSCI USA index). This is particularly noteworthy in light of the fact that this recession has been unusually severe (link and link).

Perhaps the recession ended in April

This corona recession is special. Usually, recessions are caused by some economic imbalances that need to be corrected, such as an overvalued housing market or an implosion of the financial sector. This is not the case here. This recession was caused by the sudden arrival of a virus. The sudden arrival of the corona virus caused a sudden shutdown of economic activity.

The Business Cycle Dating Committee has traditionally relied on aggregate economic data when determining turnings points in the business cycle. Aggregate data, such as industrial production, GDP, etc., are available at the monthly or quarterly frequency. Given the underlying cause of this recession, which led to the closure of business activities from one day to the next, we need to look at higher-frequency data when searching for turnings points in economic activity.

One interesting new higher-frequency indicator is the Weekly Economic Index developed by the New York Fed (link). It summarizes information in ten weekly indicators of real economic activity. It is designed to track the four-quarter growth rate of real GDP. As the St. Louis Fed notices (link) ”This series is potentially a useful indicator to watch because James Stock (one of the authors of the study developing the index) is a member of the BCDC (Business Cycle Dating Committee).”

The interpretation of the index is that it is “scaled to the four-quarter GDP growth rate; for example, if the WEI reads -2 percent and the current level of the WEI persists for an entire quarter, one would expect, on average, GDP that quarter to be 2 percent lower than a year previously.” The Weekly Economic Index is available since January 2008:

FRED Graph
Weekly Economic Index for the US economy.
Source:
Fed St. Louis Database.

The index tracks the financial crisis well. In particular, as the figure shows, it bottoms out in Q2 2009 when the recession ended.

The indicator also tracks the beginning of this recession well. It fell like a stone from a rock when the recession started in February.

The interesting point here is that the indicator reaches its bottom during the last week of April 2020. I.e., if a recession ends when this indicator bottoms out, as in 2009, this recession ended in late April.

The idea that this recession ended in late April would also be consistent with the observation that US real GDP dropped dramatically from the first to the second quarter of 2020, and rebounded strongly from the second to the third quarter:

Quarterly percentage changes in US real Gross Domestic Product.
Source: Fed St. Louis Database

What about international evidence? I am not aware of a well-designed index that tracks, e.g., Eurozone economic activity at the weekly frequency. So, as an alternative when looking at data from other countries than the US, let us consider industrial production. Industrial production is a traditional business-cycle indicator. It is available at the monthly frequency:

Indices of industrial production, normalized to one in January 2019, for the US and the Eurozone.
Source: Fed St. Louis Database and Thomson Reuter Datastream via Eikon.

For the US, industrial production bottomed out in April, like for the weekly indicator above. The collapse in Eurozone industrial production is much larger than in the US, but the timing is the same. Also for the Eurozone, industrial production bottoms out in April. So, perhaps this recession really ended in April.

The stock market and the recession

Typically, stock markets tank during the early phase of a recession, and starts rebounding when the end of the recession is in sight, i.e. before the actual end of the recession. Furthermore, the stock market normally does well during the early phases of expansions.

This graph splits the cumulative return on the US stock market into a mid-February (start of recession) to late April (assumed end of recession) period, in red, and a period thereafter, in green:

Total return index for MSCI USA, normalized to one mid-February 2020. Daily data. Red line indicates assumed recession. Green line indicates assumed early phase of expansion.
Source: Thomson Reuter Datastream via Eikon.

If we decide that this recession ended in late April, instead of assuming that the recession is still ongoing, the behavior of the stock market makes perfect sense. In this case, the stock market lost 14% during the recession. Since May, i.e. since the assumed end of the recession, the stock market has gained 27%.

Zooming in on the correlation between the Weekly Economic Indicator and the cumulative return on the US stock market, it follows that the stock market bottomed out before economic indicators started improving. This again emphasizes that the reason for the turnaround in the stock market in March was the “Whatever it takes moment of the Fed on March 23” (link). Since late April, however, the stock market and economic conditions have moved in tandem, i.e. economic conditions have improved and so has the stock market:

Total return index for MSCI USA and Weekly Economic Index. Weekly data
Source: Fed St. Louis Database and Thomson Reuter Datastream via Eikon.

Did the recession really end in April, then?

We do not know. It is, as mentioned, the NBER Business Cycle Dating Committee that determines peaks and troughs in the US economy. They have not called the end of the recession yet. Hence, officially, the US economy is still contracting.

I am happy that I am not a member of the NBER Business Cycle Dating Committee. This time, it must be particularly difficult to decide whether the US economy (as well as the European economy, by the way) is really out of the recession or not. As we all know, numbers of new cases are on the rise again in the US and have been doing so for a while in Europe. This will hurt economic activity going forward. So, should one conclude that there was a recession from February through April, or should one conclude that the recession is still ongoing. This is no easy question.

VV (two single Vs) or W?

Did the recession start in February and stop in April? Are we now entering a new recession due to a rising number of corona cases and a subsequent slowdown in economic activity? Or, are we still in the recession that started in February? One way to try to judge this is via NowCasts, i.e. daily forecasts of what growth in economic activity will be the current quarter. This is related to, but different from, the Weekly Index above. It is an estimate of the growth rate of GDP during the current quarter, but based upon data available at a higher frequency. Here is the NowCast from the New York Fed:

NowCasts of US quarterly growth in real GDP. Daily forecasts of growth during the current quarter.
Source: New York Fed

It has a clear V-shape during the second quarter. During April/May/June, incoming data indicated that the fall in GDP during Q2 would be enormous, as it turned out to be (see figure above with actual GDP growth). Incoming data during Q3 indicated that Q3 growth would be high, as it turned out to be. Q4 growth is expected to be considerably lower than Q3 growth, i.e. a new V, but growth is still expected to be positive.

Why is it important whether the recession lasted from February through April only (one V), whether we left the recession in April/May but enter a new now (two Vs, i.e. VV), or whether we are still in the recession, but economic growth was high during Q3 but expected to be low during Q4 and possibly Q1 2021, i.e. W? The reason is that it influences how we should think of recessions and financial markets during recessions.

Conclusion

Historically, stock markets have suffered during recessions. This recession started in February. Since then, the US stock market has gained 10%, seemingly in contrast to its usual behavior during recessions. Economic activity bottomed out in late April, though. The US stock market lost 14% from February through April. Since then, it has returned more than 25%. So, whether you conclude that the stock market has suffered during this recession or not depends on your favorite definition of when the recession ended, if at all until today.

To get the official answer, we have to wait for the NBER Business Cycle Dating Committee.

For the future path of the stock market, the question will be how the number of new infections develop and their impact on economic activity.

For the history books, i.e. for the conclusion of whether the stock market performed surprisingly well during this recession or not, the question is whether the recession ended in April or hasn’t ended yet. Did we have one V from February through April, do we get a new V now, so this ends up being VV, or are we still in the recession, i.e. W?

If Biden wins

If history is any guide, it will be four good years on the stock market if Biden wins on November 3. Historically, stocks have performed so much better under Democratic presidents. The question is whether history will be a guide also this time around.

Are Democratic or Republican presidents better for the stock market? To evaluate, let us recall the performance of the US stock market under Trump – a Republican – and compare it to its performance under Obama – a Democrat and Trump’s predecessor. Afterwards, let us look at the full history of the stock market under Democratic and Republican presidents. Finally, let us discuss what it implies for this election and the next four years on the US stock market.

Obama vs. Trump

This graphs shows the cumulative return to USD 1 invested in respective January 2009 (Obama 1st term), January 2013 (Obama 2nd term), and January 2017 (Trump). I show real returns, i.e. returns after inflation:

Cumulative real returns to US large-cap stocks under Obama and Trump. Own calculations.
Data source: http://www.econ.yale.edu/~shiller/data.htm

A couple of months remain, but during most of Trump’s presidency, the stock market has performed worse than under Obama. The exceptions are the first few months of Obama’s first term and the beginning of this year, right before the corona crisis.

In numbers, one US dollar invested in the US stock market in January 2009, when Barack Obama – a Democratic president – was inaugurated, had turned into 2.68 USD in December 2016 (when Obama’s second term ended) in real terms, given reinvestments of dividends. This is a total accumulated real return of 168 percent over eight years, or an average annual return of 13%.

One US dollar invested in the US stock market in January 2017, when Donald Trump – a Republican president – was inaugurated, had turned into 1.46 USD in September this year (in real terms). This is a total accumulated return of 46 percent over four years, or an annual average real return of 10% (there are still three months to go of Trump’s presidency, so the numbers might change a little in the end).

Some extraordinary events influenced the stock market under both Obama and Trump.

Early 2009, the stock market was still suffering from the financial crisis of 2008. Obama was inaugurated in January 2009. A few months later (in March 2009), the stock market turned around. Following March 2009, many great years on the stock market followed.

This year, 2020, has seen the fastest bear market in history (in March; link), caused by the corona crisis, hurting the stock market’s performance during Trump’s presidency.

The financial crisis of 2009 and the corona crisis were very unusual events. Perhaps the stock market’s performance under Trump and Obama was unusual.

It turns out that the picture painted above – that the stock market performs better under Democratic presidents – is robust. In fact, the US stock market has historically performed much better under Democrats.

The Presidential Puzzle

Pedro Santa-Clara and Rossen Valkanov published in 2003 a paper (link) entitled ”The Presidential Puzzle”. They documented an intriguing stylized fact: Over the 1927-1999 period, the excess return on the US stock market has been nine percentage points higher under Democratic than Republican presidencies. The average excess returns under Democratic presidencies, they showed, was 11 percent versus 2 percent only under Republican presidents. A nine-percentage point difference is enormous. For instance, it exceeds the excess return on the stock market in general, i.e. the return stocks provide over and above the return on risk-free assets.

Santa-Clara and Valkanov took great care in investigating potential reasons for this stylized fact. The most obvious explanation, financial economists would suggest, is that this is simply a compensation for risk, i.e. that risks have been higher under Democratic presidents. It turns out that this is not the case. In the end, their conclusion was:

”There is no difference in the riskiness of the stock market across presidencies that could justify a risk premium. The difference in returns through the political cycle is therefore a puzzle. ”

Pastor and Veronesi (link) update the results of Santa-Clara and Valkanov. They add app. twenty years of data (until 2015, i.e. their sample runs from 1927 through 2015). Pastor and Veronesi find even stronger results. In their extended sample, they find that the average return under Democratic presidents is eleven percentage points higher than the average return under Republican presidents.

Pastor and Veronesi have this figure with average annual excess returns under each president:

Average excess returns during individual presidencies.
Source: Pastor and Veronesi (2020).

The figure shows that the only Democratic presidency that delivered returns significantly below the average return over the whole period (the dotted line) was Roosevelt’s second term (1937-41). In contrast, a number of Republican presidents have delivered subpar returns (Nixon, first Reagan term, Bush Jr.). My small calculation in the beginning of this post, that returns have been higher under Obama than Trump, strengthens this conclusion.

Pastor and Veronesi show that this finding is robust during subsamples, for instance if excluding the large negative and positive returns during the 1930s. They also show that it is particularly the first year of the presidency that drives these differences. During the first year in office, Democrat presidents have experienced a 37%-points (!) larger return than Republican presidents have:

Difference between average excess returns in the returns during the presidents’ early years in office.
Source: Pastor and Veronesi (2020).

(If you are interested, here is a detailed account of the stock market’s performance under every single president since Truman: link).

There is some discussion whether this finding (that the stock market does better under leftwing presidents) holds internationally. Pastor and Veronesi show in the (114 pages…) appendix to their article that it holds internationally while others claim that it does not (link). We leave this aside here.

Democratic presidents are elected when times are bad

Pastor and Veronesi develop an interesting explanation of this intriguing pattern of the data. They argue that the reason for the difference between the stock market’s performance under Democratic and Republican presidents has nothing to do with economic policies during presidencies but everything to do with the economic situation when new presidents are elected.

Pastor and Veronesi argue that Democratic presidents are elected at points in time when risk aversion is high. Remember, for instance, the election of Obama in autumn 2008. This was right in the middle of the financial crisis. The worst financial and economic crisis since the 1930s. Everybody was afraid and uncertain how things would evolve. Risk aversion was high. When risk aversion is high, investors demand high compensation for taking on risks in the stock market. Subsequently, investors get compensated by high average returns.

So, the story of Pastor and Veronesi is that Democratic presidents get elected when times are bad. When times are bad, voters have a tendency to vote for left-wing candidates, as voters demand more social insurance during hard times. An important implication of this theory is that Democrat presidents do not cause higher stock returns (and Republican presidents do not cause lower stock returns). Instead, Democratic presidents are elected when the economy is suffering and risk aversion is high, causing high expected returns and subsequent high average returns.

Implications for this election

If (and I write if) it is correct that Democratic presidents get elected when risk aversion is high, and that good returns subsequently follow, what does this imply for this election and the next four years on the stock market?

Pastor and Veronesi suggest a number of proxies for risk aversion. To illustrate, let us look at two of them. The first is unemployment. The idea is straightforward: when unemployment is high, people are afraid of taking on risk on the stock market. Risk aversion is high and so are expected returns.

Here is unemployment in the US since 1947:

US unemployment rate.
Source: FRED.

Unemployment is currently high, at 7.9% in September (latest figure at the time of writing). This is more than two percentage points above the historical average rate of unemployment of 5.8%. Unemployment is coming down, though, and fast. Also, there are still two weeks to go until the election. Nothing is for sure. But, as it looks now, this indicates high risk aversion, and thus high expected returns.

Another measure of risk aversion is the habit-persistence idea of Campbell and Cochrane (link). If your consumption is low today, in relation to your past consumption, you feel that times are bad. If you are used to go on holiday a couple of times per year, but now you don’t dare because you are afraid of losing your job, you also become afraid of taking on risk in the stock market. Your risk aversion is high. You require high expected returns if you should be convinced to nevertheless invest in stocks.

Let me present a simple illustration here, inspired by Atanasov, Moeller, and Priestley: link. I take consumption (real per capita) and divide by habit. I approximate habit by the average of the past three years of consumption (this is a blog, so this should be OK), i.e. the habit ratio is here: C(t)/(AVG[C(t-1)+(C(t-2)+…..+C(t-12]), where C(t) is consumption in period t and AVG is the average. I do this calculation for every quarter since 1950. The result is the following time series:

Consumption habit ratio. Own calculations.
Data source: FRED.

When an entry in the figure is above one, consumption during that quarter is higher than its past three-year average, and times are good. When the habit-ratio is low, times are bad. The habit ratio (current consumption to habit) thus has a tendency to drop around recessions (1980, 2008, etc.), as, during recessions, people cut consumption in relation to past their consumption.

The lower is the habit ratio, the higher is risk aversion and thus expected returns. Currently, due to the enormous drop in consumption resulting from the corona-crisis, the habit-ratio is historically low. Times are bad. Consumption is low. Biden should win (according to this theory). Risk aversion is high. Expected returns should be high. The next four years on the stock market should be good.

There is uncertainty, of course

Many things can happen until November 3 and the next four years. And, for sure, this has been a really weird campaign. Nothing is for sure.

Some reservations:

My figure with the habit-ratio is based on quarterly data (consumption is quarterly). The last entry is Q2 2020. Since Q2, the stock market has been sprinting ahead, implying that the potential for future returns is, all else equal, lower now than in Q2.

Also, financial markets have been pricing in an enormous amount of event risk surrounding this election. The uncertainty surrounding this election is high. Protection against event risk is expensive (link, link, and link).

At the same time, the stock market has been doing really well since March 23 (link). This is unlike recessions in general. During normal recessions, the stock market tanks. This stock market rebound since spring, everything else equal, reduces the potential for future returns as of now.

On the other hand, at least right now, the polls seem to offer some support for the story. We are in a big downturn (corona crisis). In downturns, people have a tendency to vote left-wing. As we all know, Biden leads the polls at the time of writing:

Poll average. October 16, 2020.
Source: www.realclearpolitics.com.

Conclusion

The stock market has performed worse under Trump than under Obama. This is not an outlier. Historically, the US stock market has performed much better under Democratic presidents.

One explanation why the stock market performs well under Democratic presidents is that Democratic presidents get elected during bad times, when risk aversion is high.

Currently, we are in the midst of a terrible situation (corona crisis). Supporting this theory, Biden leads the polls. If history is any guide, this indicates good years ahead on the stock market. Not necessarily because Biden – if he wins – implements policies that support the stock market (perhaps he does, but this is not the point of the theory), but because risk aversion is high right now, at the time of the election.

Of course, so many things can happen. This certainly has been a strange (and, at least for many Europeans, myself included, a very weird and scary) campaign to follow from the sideline. The performance of the stock market during this recession has also been weird. It is important to recognize that the stock market rebound since March reduces the potential for future returns as of now. And, in general, so many things can happen during the next four years. Perhaps the situation will thus play out differently this time around. We will know how the election turns out in two weeks, and we will know how the stock market performs during the next presidency in four years and three months. It will be interesting to follow.

The Fed’s “Whatever it takes” moment. Or, how the Fed saved equity and credit markets

Facing a looming recession and financial market panics, the Fed intervened heavily in late-February/early-March, lowering the Fed Funds Rate to zero and expanding its balance sheet dramatically. In spite of this, markets kept on panicking. Then, suddenly, on March 23, everything changed. Stock markets started their rally. This was not because the Fed lowered the rate or expanded its asset purchases even further, nor because the economic data improved. What happened? The Fed made an announcement. Nothing else. It is a fascinating illustration of how expectations can change everything on financial markets.  

Much has been written about the massive interventions of the Fed during February and early March. In my previous post (link), I list Fed interventions as one of the reasons why the stock market is back to pre-crisis highs. In this post, I dig one step deeper and explain the fascinating story of how the Fed said something and thereby rescued markets.

Asset purchases and rate reductions did not save markets in February/March

Let us start by illustrating how the actual Fed interventions (interest rate changes and asset purchases) did not save markets in March. The Fed lowered the (lower range of the) Fed Funds Target Range to 1% from 1.5% on March 2 and then again to 0% less than two weeks later, on March 14. At the same time, it bought Treasuries and mortgage-backed securities to the tune of USD 600bn per week. These interventions succeeded in lowering yields on government and mortgage-backed bonds, but did not cheer up stock markets.

This graph shows how Treasury yields came down significantly in February/March, by basically 1.5%-point (from close to 2% to close to 0.5%; I show yields on 10-year Treasuries in this graph), as a result of reductions in the policy rate (the Fed Funds Rate) and asset purchases by the Fed.

SP500 and yield on 10-year Treasuries. Daily data.
Source: Fed St. Louis Database

The graph also shows that the SP500 continued falling throughout February/March. In other words, the massive interventions by the Fed in late-February/early-March (and these interventions really were massive – buying for USD 600 bn per week and lowering rates to zero is indeed a massive intervention) did not convince stock markets that the situation was under control. And, remember, these were not minor stock-market adjustments. It was the fastest bear market ever (link).

What turned the tide?

On March 23, the SP500 reached its low of 2237, a drop of 31% compared to its January 1 value. Since then, everything has been turned upside down and markets have been cheering, as the above graph makes clear.

What happened on March 23? The Fed had its finest hour. It did not do anything. It merely said something. A true “Whatever it takes” moment.

As you remember, a “Whatever it takes” moment refers to the July 26, 2012 speech by then ECB-president Mario Draghi (link). The speech was given at the peak of the Eurozone debt crisis. The debt crisis pushed yields on Italian and Spanish sovereign bonds to unsustainable levels. Italy was too big to fail, but also too big to save. The pressure on Italy was a pressure on the Eurozone construction. Mario Draghi explained the situation and said the by-now famous words:

But there is another message I want to tell you. Within our mandate, the ECB is ready to do whatever it takes to preserve the euro. And believe me, it will be enough.

Investors understood that the ECB would be ready to buy sovereign bonds to save the Euro. Markets calmed down. Italian and Spanish yields fell. And this – and this is the point here – without the ECB actually intervening, i.e. without the ECB buying Italian or Spanish bonds. The announcement that the ECB would intervene was enough to calm down investors.

Why is this relevant here? Because on March 23 the Fed sent out a press release, announcing that:

“…the Federal Reserve is using its full range of authorities to provide powerful support for the flow of credit to American families and businesses.”

And, then, as one of the new features announced the:

Establishment of two facilities to support credit to large employers – the Primary Market Corporate Credit  Facility (PMCCF) for new bond and loan issuance and the Secondary Market Corporate Credit Facility (SMCCF) to provide liquidity for outstanding corporate bonds.

This statement turned everything upside down. The important thing is that this turn of events happened without the Fed using any money at all. It was a “Whatever it takes” moment (though, perhaps not as Dirty-Harry dramatic as Mario Draghi’s “And believe me, it will be enough”): The Fed announced what they would do, investors believed the Fed, and markets started cheering. It is a prime example of how investor expectations influence financial markets.

What are the PMCCF and SMCCFs, why did the Fed announce them, and why were their effects so dramatic?

The lowering of the Fed Funds Rate and the purchasing of Treasuries succeeded in lowering yields on safe assets, such as Treasury bonds, as explained above, but did not lower yields on corporate bonds. In fact, during the turmoil in late-February/early-March, credit spreads (the spread between yields on corporate bonds and safe bonds) widened dramatically. When yields on corporate bonds rise, it becomes more expensive for corporations to finance their operations. And, when yields rise a lot, as in February/March, investors get nervous about the profitability and survival of firms.

This graphs shows how yields on both the least risky corporate bonds (Triple A) and speculative grade bonds (Single B) rose dramatically in relation to 3-month Treasury Bills, with yields on lower-rated bonds (Single-B) naturally rising more than yields on higher-rated (Triple-A) bonds.

Difference between yields on ICE BofA AAA US Corporate Index and 3-month Treasuries as well as the difference between yields on ICE BofA Single-B US High Yield Index and 3-month Treasuries. Daily data.
Source: Fed St. Louis Database

The important point in the picture is that the spreads continued rising throughout late-February/March, in spite of the intensive interventions described above, i.e. in spite of Fed purchases of government and mortgage bonds. The Fed was happy that safe yields fell, but was concerned that credit spreads kept on rising. Volatility in corporate bonds markets also rose (link), making the whole thing even worse.

More or less all firms saw their funding costs increase, even when there were differences across firms in different industries, with firms in the Mining, Oil, and Gas, Arts and Entertainment, and Hotel and Restaurant sector hit the hardest, and firms in Retail and Utilities sectors less affected (link). The Fed became nervous because higher funding costs for firms affect firms negatively, causing them to cut jobs, reduce investments, etc.

The Fed decided to act. It announced on March 23 that it would launch two new programs, PMCCF and SMCCFs. The PMCCF is the ’Primary Market Corporate Credit Facility and the SMCCF the Secondary Market Corporate Credit Facility. Primary markets are where firms initially sell their newly issued bonds. The PMCCF should thus ease the issuance of newly issued corporate bonds, i.e. help firms raise funds. Secondary markets are where bonds are traded afterwards, i.e. the SMCCF should ease the trading (liquidity) of already existing corporate bonds.

The effect on equity and credit markets of the announcement of the PMCCF and SMCCFs was immediate and spectacular. Immediately after the announcement, credit spreads narrowed (see, e.g., link, link, and link).

Interestingly, stock markets reacted immediately, too. The stock market, thus, did not react to the lowering of the Fed Funds Rate and the extensive expansion of the Fed balance sheet in late-February/early-March, but reacted strongly to the announcement that the Fed would buy corporate bonds on March 23.

This (supercool, I think 🙂 ) graph shows developments on corporate bond and equity markets. The graph shows the spread between yields on AAA-rated corporate bonds and 3-month Treasury Bills and the stock market inverted, both normalized to one on January 1. The graph for the inverted stock market means that when the SP500 is at 1.45 on the y-axis on March 23, the stock price at January 1 was 45% higher than it was on March 23.

Difference between yields on ICE BofA AAA US Corporate Index and 3-month Treasuries (Left hand axis) as well as the SP500 inverted (Right hand axis). Both series normalized to one on January 2, 2020. Vertical line indicates March 23.
Source: Fed St. Louis Database

The parallel movements in equity and credit markets are striking. Equity and credit markets moved in parallel during January, when stock markets rose and credit spreads narrowed, in late-February/early-March when credit spreads widened dramatically and stock markets fell like a stone, as well as after March 23, when both credit spreads and stock markets improved spectacularly. Since then, stock markets have continued to rise and credit spreads have continued to narrow.

The correlation between the two series is an astonishing 0.93 for the January 2 through May 31 period.

The announcement effect

The Fed has experience with and a mandate to buy mortgage-backed securities and Treasuries. It had no such experience when it comes to purchases of corporate bonds and corporate bond ETFs. This means that the Fed could not start buying corporate bonds on March 23, it needed an institutional set-up. It created an SPV with capital injections from the Treasury and leverage from the New York Fed, it asked a financial firm (Blackrock) to help them purchase the bonds, etc. These things take time. The Fed only started buying corporate bond ETFs in mid-May and corporate bonds in mid-June.

Given that the Fed only started buying bonds and ETFs in May/June, the spectacular turnaround on March 23 really was due to the announcement only.

In slightly more detail, it played out as follows. The March 23 announcement primarily dealt with higher-rated corporate bonds (Investment Grade), and spreads narrowed immediately. On April 9, the Fed announced that they would expand “the size and scope of the Primary and Secondary Market Corporate Credit Facilities (PMCCF and SMCCF)”, buying bonds that were Investment Grade on March 22 but had been downgraded since then as well as corporate bond ETF. High-yield spreads tightened even more (link). Today, the Fed explains that the programs allow the Fed to buy “investment grade U.S. companies or certain U.S. companies that were investment grade as of March 22, 2020, and remain rated at least BB-/Ba3 rated at the time of purchase, as well as U.S.-listed exchange-traded funds whose investment objective is to provide broad exposure to the market for U.S. corporate bonds.”

The actual purchases began in May/June, several months after the announcement. Interestingly, the purchases themselves have had a modest impact only. First, the amounts of corporate bonds and corporate bond ETFs bought pale in comparison with the amounts of mortgage-backed securities (MBS) and Treasuries bought. The Fed has bought MBS and Treasuries to the tune of USD 3,000bn this year. It has “only” bought corporate bonds and ETFs for around USD 12bn. The amount used to buy corporate bonds thus corresponds to less than 0.5% of the amount used to buy MBS and Treasuries. This graphs shows the daily purchases. Using the latest figures from August 10, the Fed has basically stopped buying corporates.

Daily Fed purchases of corporate bonds and corporate bond ETFs. Daily data.
Source: Fed webpage. Thanks to Fabrice Tourre for collecting and sharing the data.

So, the Fed has not bought a lot of corporate debt. It has, however, the power to buy a lot. The PMCCF and SMCCF set-up is such that the Treasury has committed to make an USD 75bn investment in the SPV that buys the assets (USD 50bn toward the PMCCF and USD 25bn toward the SMCCF). The New York Fed then has the ability to level this up by a factor of ten, i.e. the Fed can buy corporate debt for up to USD 750bn. This is a sizeable fraction of the total US corporate bond market. This, that the Fed can potentially buy a lot, helps making the program credible and thus helps explaining its powerful impact.

Dilemmas

Given the success of the PMCCF and SMCCFs, commentators have started arguing that the Fed should be allowed to buy corporate bonds as part of its standard toolkit (link). This might be relevant, but purchases of corporate bonds by central banks raise a number of dilemmas:

  • Keeping zombie firms alive. By easing up stresses in corporate bond markets, the Fed calmed down markets. This was the intention of the PMCCF and SMCCF announcements and it worked. It eased access to funding for firms, and firms have raised a lot of cash as a consequence (link). It is positive that malfunctioning markets are stabilized, but if central bank intervention makes markets too cheerful it may allow firms that in principle should not receive funding to nevertheless get it. And, thereby, to keep firms alive for too long, and increase firm leverage too much. Basically, the fear is that programs such as the PMCCF and SMCCFs create too many zombie firms. My CBS colleague Fabrice Tourre and his co-author Nicolas Crouzet has an interesting paper that examines this (link). Fabrice and Nicolas find that when financial markets work perfectly (no disruptions), Fed intervention might be detrimental to economic growth. On the other hand, if markets are disrupted, Fed intervention might prevent a too large wave of liquidations. One thus needs to determine when markets are disrupted “enough” to rationalize interventions in credit markets. This is no straightforward task.
  • Distributional aspects. By buying some bonds but not others, the Fed exposes itself to the critique that it helps some firms at the advantage of other. To alleviate such criticism, the Fed has been very transparent and publishes a lot of information about the bonds it buys, the prices at which it buys the bonds, etc. (link). Nevertheless, it is easy to imagine that some firms at some point will start saying ‘Why did you buy the bonds of my competitor, but not my bonds’?
  • The Fed put. The more the Fed intervenes when troubles arise, the more investors get reassured that the Fed will also come to the rescue next time around. When the Fed saves markets, it is sometimes called the Fed exercises the “Fed Put”. The potential problem here is that if investors believe that the Fed will exercise the Fed put, investors will be tempted to take on even more risk. Those of us concerned about systemic risks get nervous.

So, in the end, the announcement of the PMCCF and SMCCFs was crucial during this crisis. There are, however, important dilemmas that need to be addressed when evaluating whether such programs should be part of the standard toolbox. I am not saying they should not. I am saying that one needs to be careful.

Conclusion

The Fed launched massive “traditional” interventions in late-February/early March, lowering the Fed Funds Rate and buying mortgage and government bonds. In spite of these very large interventions, equity and credit markets kept on tail spinning. When firms struggle, it hurts economic activity and employment. The Fed got nervous.

The Fed announced – and this is the whole point here; they only announced – that they would start buying corporate bonds. Markets turned upside down. Credit markets stabilized, credit spreads narrowed, corporate bond-market liquidity improved, and stock markets cheered. And, all these things without the Fed spending a single dime until several months after the fact. Even today, the Fed has spent very little (some might say that USD 12bn is a lot, but compared to asset purchases of USD 3,000bn, it pales).

It was a “Whatever it takes” moment. It illustrates how managing investor expectations can be crucial. Understanding this announcement is thus important for understanding the behavior of financial markets during this pandemic.

The weird stock market. Part II: Potential explanations

The behavior of the stock market during this recession raises three main questions: (i) why did stock markets fall so spectacularly during February/March, (ii) why did stocks rebound so spectacularly during April/May, and (iii) why is the stock market currently at its pre-crisis level? My previous post (link) presented the stylized facts and addressed stories that cannot explain the facts. In this post, I provide some potential explanations.

I my previous post (link), I mentioned that the rebound in the stock market since its low in March cannot be explained by (i) revisions to the economic outlook – in fact, economic forecasts were revised down during spring, (ii) a preference for just a few (FAANG) stocks, as the rebound has been international and broad based, even when large-cap stocks have done particularly well, (iii) a hypothesis that this recession is not particularly bad – in fact, at least in the short run, this recession is much worse than the 2008 recession, or (iv) the fact that markets also recovered after 2008 – markets always recover, i.e. this is not an explanation.

What can explain it, then? First, a warning: I do not want to raise expectations too much (sorry). I will not be able to offer a full explanation of these puzzles in an app. 1,900 words blog post. Also, as you will see, puzzles remain, even if we can explain some things. Finally, I have not seen a good explanation of these puzzling stylized facts (though Gavyn Davies’ piece in the FT is a good place to continue after reading this post; link). Hence, it would not be serious nor academic if I claimed that I could explain everything. What I can do, though, is to provide some hints at what might be relevant parts of an explanation.

Explanations that contain elements of truth

Earnings suffered more in 2008               

In the end, earnings and not economic activity (GDP) determine stock prices. In the long run, there is a strong relation between earnings and economic activity, which is why we focus on economic developments when we try to understand stock markets. The relation is not one-to-one, though, in particular in the short run. Hence, let us discuss earnings.

Earnings suffered dramatically in 2008. Often, we look at 12-month earnings, as quarterly earnings are volatile. 12-month reported earnings per share for the S&P500 came in at 6.86 in Q1 2009, which is app. 10% of 12-month reported earnings in Q1 2008. I.e., earnings fell by a mind-blowing 90% during the financial crisis of 2008.

Earnings fell in Q1 2020, too, but much less: From USD 139 per share in Q4 2019 to USD 116 (12 month reported earnings), i.e. by 17%. At the time of writing, around 90% of companies in the S&P500 have reported earnings for Q2 2020. Earnings (12-month reported) seem to be coming in at close to USD 96 per share, which is a drop of 17%, too, compared with Q1. Earnings have thus dropped by 17% during each of two consecutive quarters. But, and this is the main thing, these drops pale in comparison with autumn 2008. In autumn 2008, earnings dropped by 68% from Q3 to Q4 and then again by 54% from Q4 2008 to Q1 2009. This figure shows the quarterly percentage changes in 12-month reported earnings of the S&P500 in 2008 versus 2020. “0” on the x-axis is Q2 2020, respectively Q4 2008. The drop in earnings was just so much larger in 2008.

Quarterly changes in 12-month reported earnings per share, S&P500. “0” is Q2 2020, respectively Q4 2008. For Q3, 2020 and forward, these are expected earnings.
Source: https://www.spglobal.com

“1” on the x-axis in the figure is thus Q1 2009 and Q3 2020, i.e. firms’ expected earnings in Q3 2020 (and actual earnings in Q1 2009). If these expectations hold true, earnings are expected to do relatively fine going forward.

Earnings do fall from Q1 to Q2 this year. Earnings surprises matter, too, though, i.e. whether actual earnings are higher (or lower) than expected. At the time of writing, around 80% of companies have reported positive earnings surprises for Q2. This, that actual Q2 2020 earnings are better than expected, helps explaining why stock prices rise currently.

If investors primarily look at earnings, this helps explaining why stock prices fell considerably more in 2008 than they have done during this recession and why the stock-market recovery took longer in 2008. Earnings have simply not suffered so much this time around. For that reason, stock prices have not suffered so much this time around either.

Monetary and fiscal policies have been aggressive

Another factor that helps explaining why stock prices today are at pre-crisis levels, in spite of the severity of the recession, is that policy interventions have been aggressive. Immediately, at the onset of the crisis, monetary policy turned very expansionary. This can be illustrated by the weekly changes in the Fed balance sheet, as in this graph:

Weekly changes in the Fed balance sheet. USD mio.
Data source: Fed St. Louis Database

The figure plots the weekly changes in the Fed balance sheet due to purchases of financial assets, in millions of USD. The Fed balance sheet changes when the Fed buys/sells financial assets. During March this year, there were weeks when the Fed intervened to the tune of USD 600bn. This is an enormous amount of money. It is, by way of comparison, more than twice the weekly amounts spent during the financial crisis of 2008, the figure also shows.

At the same time, yields are very low. This forces investors to buy risky assets if they want some kind of return, supporting stocks.

In addition to very aggressive monetary policy, fiscal policy has also been very aggressive.

Why did stock prices fall during February/March?

OK, so earnings have not fallen as much as in 2008, earnings for Q2 2020 have surprised positively, monetary and fiscal policies have been very aggressive, central banks pump liquidity into the system, and interest rates are very low, implying that investors need to invest their money in risky assets if they want a positive expected return. These features help explaining why stock prices today are not much lower than in the beginning of the year. But, if this is the story, why did stock markets drop so dramatically in March?

Changes to expectations to earnings

Earnings have dropped in Q1 and Q2, even if considerably less than in 2008. Perhaps the stock-price drop in February/March was simply due to a downward revision in expected earnings. Alas, this is not the case. Landier & Thesmar (link) have an interesting analysis where they look at analysts’ expectations to the earnings of S&P500 companies during Q1 and Q2 2020. They find that analysts cut their expectations to earnings of S&P500 firms, but not so much that it can account for the drop in stock prices during February/March. In other words, the drop in stock prices in February/March cannot be explained by analysts reducing their earnings forecasts.

If the drop in expected earnings cannot account for the drop in stock prices in February/March, what happened then? Financial economists have a straightforward way of explaining this. Stock prices are discounted cash-flows. To understand stock price movements, we must understand cash-flow and discount rate movements. When the fall in expected earnings is not large enough to account for the drop in stock prices, discount rates must have increased in February/March. In other words, during February/March investors required a higher expected return if they should invest in stocks. This is also the conclusion in Landier & Thesmar and in Gormsen & Koijen (link). Basically, this is the conclusion in most empirical asset-pricing literature: stock prices move too much to be justified by movements in cash-flows. Discount rate variation is the reason, the literature concludes.

When stock prices move, and it cannot be because of cash-flow movements, it must be discount-rate movements. This is fine, but it is also somewhat tautological: If A = B + C, and A moves but B does not, then it must be because C moves.  A more difficult question is what makes discount rates move in the first place? I.e., what is the deeper economic explanation? Perhaps/probably risk aversion spiked, when investors in February suddenly realized the severity of the virus. Perhaps/probably some market participants faced funding constraints. And so on. In other words, I agree that discount rates most likely spiked during March, but I am not 100% sure why and I do not know if the increase in risk aversion is large enough to account for the increase in discount rates, and thus account for the drop in stock prices.

Lingering doubts

So, a potential reason why stock markets have done better than in 2008 is that earnings have not suffered as much as in 2008 and central banks have flooded the market with liquidity (and yields are at zero). The reason why we saw a massive fall in stock prices in February/March, and an unprecedented rebound during April/May/June, then probably is that discount rates increased during the early phase of the crisis, and then stabilized. This is a story that somehow makes sense.

Two things are still strange (to me at least), though.

First, if stock prices are at their right levels now, why did they have to fall so much in March? OK, because risk aversion increased and funding conditions tightened. But, if risk aversion increased in February because investors understood that a recession was looming, why would investors turn less risk averse three weeks later, when economic forecasts just kept on deteriorating? Probably the Fed and the Treasury (via monetary policy and fiscal policy) eliminated the spike in risk aversion via aggressive policy interventions, but it is still not 100% clear how to reconcile these features of the data.

Second, the behavior of earnings has been surprising. During recessions, earnings normally contract much more than economic activity. So, when economic activity in 2020 contracts much more than in, as an example, 2008, a reasonable hypothesis is that earnings in 2020 would contract much more than in 2008. This has not happened.

To some extent, this graph summarizes both the explanation of the relatively mild response of the stock market to this recession and the remaining puzzle:

Percentage changes in US real GDP and earnings of S^P 500 firms from Q3 to Q4 2008 and from Q1 to Q2 2020.

The figure shows how earnings dropped 68% from Q3 to Q4 2008 but seems to drop only 17% from Q1 to Q2 this year. This explains why the stock market has performed considerably better during this recession compared to its performance in 2008, for instance. On the other hand, the figure also shows that GDP dropped by 10% from Q1 to Q2 this year and only by 2% from Q3 to Q4 2008, i.e. the fall in economic activity is five times larger during this recession. It is puzzling that earnings drop so much less during this recession when economic activity contracts so much more.

So, the relatively modest contraction in earnings helps explaining the relatively fine performance of the stock market during this recession, but it is difficult to understand why earnings have behaved reasonably well. Perhaps this is because banks were suffering in 2008, but are able to help firms getting through this recession. Perhaps it is because firms have been able to adapt better this time. We do not really know. From an academic asset-pricing perspective, it is interesting that the stock market has behaved so differently this time around, compared to how it normally behaves. From an investor perspective, we can only hope that the stock market will keep on behaving in a different way than it usually does, as, otherwise, stock prices will fall going forward.

Conclusion

The facts that earnings have done better than in 2008 and that monetary policy has been very aggressive help us understand why the stock market has done well during this recession. Some puzzles still remain, though. For instance, why have earnings done so reasonably well, given the severity of the recession, and why did risk aversion increase so dramatically in February only to normalize few weeks later, in spite of the worsening recession. Perhaps the sensible answer is that this recession has been unusual in many dimensions, and some things will remain difficult to understand.

The weird stock market. Part I: Facts and wrong explanations

The behavior of the stock market during this recession has been perplexingly. To understand it, we need to answer three main questions: (i) why did stock markets fall so spectacularly during February/March, (ii) why did stocks rebound so spectacularly during April/May, and (iii) why is the stock market currently at its pre-crisis level? This blog post explains why these stylized facts are so difficult to understand jointly. In my next blog post (link), I will offer my interpretation of the evens.

Observing the stock market during this recession has been a like watching a roller coaster. The fall in late-February/early-March was the fastest bear market in history, the rebound has been historically fast, too, and – in spite of the worst recession ever – the stock market is currently back at pre-recession levels. Somebody leaving for the moon in January and returning now would not be able to see any trace in the stock market of the worst recession ever. This is in stark contrast to normal recessions. People interested in financial markets and the economy should be puzzled.

The problem – in a nutshell – is that if you claim that you understand why stocks are currently at pre-recession levels, then you face a challenge explaining why we needed to go through the fastest bear market ever during February/March. On the other hand, if you claim that the fast fall and rise in markets was just a short-lived technical blip (perhaps due to funding squeezes and fear), then you face a challenge explaining why that technical blip should result in a loss to the tune of USD 20 trillion (link). Finally, if you claim that you understand why markets fell so fast in February/March (perhaps because we were facing the worst recession ever – which, by the way, is a good explanation), then you face a challenge explaining why markets recovered so strongly in April/May/June, as the recovery cannot be explained by a brighter economic outlook. In fact, we have seen continuous downgrades of the economic outlook. E.g., the IMF WEO in June lowered its April forecasts for global economic output to –4.9%, from –3.0%. A huge downgrade. Strange that stocks recover in spite of this.

In this post, I describe the facts. I also review a couple of suggested, but wrong, explanations. In my next post (that I will publish next week), I will offer my view on what has been going on.

The facts

Here is an updated version of a graph that I have presented earlier. It shows the Danish, US, emerging market, and world stock markets (MSCI), normalized to one on January 1, 2020:

Stock markets since January 1, 2020. MSCI indices.
Data source: Thomson Reuter Datastream via Eikon.

Stock markets reached a temporary peak on February 19, after which they tanked dramatically. As mentioned here (link), it was the fastest US bear market in history.

The rebound has been equally spectacular. The US stock market is already back at its January 1 level. World and emerging markets are still a few percentages behind, but, basically, they have also recovered from the crisis.

In the US, it was the fastest bear market ever, as mentioned. In this graph, I document that it has also been one of the fastest recoveries. I calculate rolling 11-week gains/losses in the SP500 during the last 50 years.

S&P 500, 11-week percentage changes.
Data source: Fed St. Louis Database

As you can see from the encircled peak on the right-hand-side of the graph, from its bottom on March 23, the SP500 gained app. 45% until June 8. The graphs shows that no other 11-week period since 1970 has witnessed such a large gain (I cherry-pick the 11-week period, but the main message is that the rebound has been spectacular, and this message is robust).

Wrong explanations

You hear/read many explanations. Some of them are just not correct.

It all boils down to FAANG

Some claim that it is not the overall stock market (typically these people refer to the SP500) that has recovered. Instead, some argue, it is all due to FAANG (Facebook, Apple, Amazon, Netflix, and Alphabet’s Google). It is true that the behavior of FAANG has been impressive and has driven a significant part of the SP500’s recovery, but this is not the whole story. As you see from the first graph above, emerging markets have recovered, too. This is obviously not due to FAANG. Also, some markets, such as the Danish, have recovered even more spectacularly than the US market. Not FAANG either. Even US small-cap stock have recovered. Not fully, but significantly. The Wilshire US Small-Cap Index gained an astonishing 57% during the March 23 through June 6 period. True, small-cap stocks are still (early August) 12% below their January 1 level, but when small-cap stocks have rebounded spectacularly, too, it emphasizes that this is not all about all FAANG, even when FAANG stocks have done very well.

Markets also recovered after the 2008 financial crisis

This is a no-brainer: Markets always recover. The point here is that this rebound has just been amazingly strong. In this graph, I compare the behavior of the SP500 during autumn 2008 and the corona crisis. “0” in the figure is September 19, 2008, respectively February 21, 2020:

S&P 500, daily closing prices. Normalized to “1” 25 days before September 19, 2008, respectively February 21, 2020. September 19, 2008 and February 21, 2020 marked by vertical line.
Data source: Fed St. Louis Database

During the first couple of weeks of this spring’s crash, it looked like autumn 2008. The pain was short-lived, though. Stock markets fell for a couple of weeks and then rebounded. In 2008, stocks just continued falling.

It has taken a couple of months to recover this time around. After the financial crisis of 2008, it took more than two years. This figure shows that the US stock market was back at its summer-2008 level in early 2011 only, and the world stock market later still.

Stock markets since January 1, 2020. MSCI indices.
Data source: Thomson Reuter Datastream via Eikon.

This recession is not as bad as 2008

It should be obvious by now that this argument is very wrong indeed. I have argued myself that there is hope that, when we get a vaccine and economic activity picks up again, the economic rebound will be stronger than in 2008, the reason being that we entered this recession with a more “balanced” economy than in 2008 (in 2008, we had a housing bubble, a weak financial sector, etc.). Nevertheless, the short-run loss this time around is just so much larger than in 2008.

Last week, we got figures for GDP in the US and Europe. This graph compares the cumulative falls in real GDP in the Eurozone and the US in Q3+Q4 2008 with the cumulative drops during the first two quarters of this year.

Cumulative change in real GDP in the US and the Eurozone. “2008” is Q3+Q4 2008 and “2020” is Q1+Q2 2020.
Data source: Fed St. Louis Database

US real GDP fell by more than three percent in Q3 and Q4 2008, whereas Eurozone real GDP dropped by five percent in autumn 2008. It was a severe recession in 2008. Nevertheless, it pales compared to this recession. This time around, US GDP has fallen by more than ten percent already and Eurozone GDP is down by a staggering 15% during the first two quarters of 2020.

What about the length of the recovery? After 2008, it took around two years before economic activity had reached its pre-crisis level. This figure shows for, e.g., the Eurozone that it will last more or less equally long this time (expectations for 2020 and 2021 are from the EU Commission, Spring 2020).

Development in real GDP for the Eurozone as of Q2 2008 and as of Q4 2019. Developments after Q2 2020, i.e. after 2 quarters in 2020, are expectations from the EU commission.
Data source: EU commission.

The figure shows that Eurozone GDP was 5% lower in Q4 2008 compared to Q2 2008 whereas it is 15% down in Q2 2020 compared with Q4 2019. After eight quarters, GDP is basically back at its pre-crisis level. This means that the recovery is expected to be stronger, but this recession is just very deep.

In other words, this recession is much deeper – one can multiply the drop in 2008 by a factor of three or so to get the drop this time – and it will take several years before economic activity is back at pre-crisis levels. This means that the short-run cost to society is bigger this time around (a deeper fall stretched over the same period).

And, in spite of this very strong recession, stocks are doing fine. That is perplexing.

Above, I have presented stories that cannot explain what has been going on. In my next post (link), I will offer my view on this.

Expected returns

The Council for Return Expectations published its forecasts of expected returns last week. There is a wide dispersion across asset classes. Returns on “safe” assets (government bonds) are expected to be very low, even negative, on the short horizon, whereas emerging markets equities are expected to return close to ten percent per year. The Council also publishes forecasts for standard deviations, correlations, and inflation.

As one of my external activities, I chair the Council for Return Expectations (link). The Council estimates expected returns, standard deviations, and correlations on ten broad asset classes. The estimates are widely used in the Danish financial sector and public discussions. Danish pension funds use the estimates when they calculate pension projections for their customers and banks use them when they make projections for how outside-pension savings can be expected to develop. Newspapers also write about them, too.

I will argue that the Danish financial sector has been a front-runner in designing a way to make such independent return assumptions. I hope the set-up helps improving the credibility of the projections banks and pension funds make.

In this blog post, I present the latest estimates from the Council. As it is the first time I present these estimates on this blog, I start out describing why the Council was established, how the Council works, and the procedure we use in the Council to find expected returns. Afterwards, I present the forecasts.

Background: The Council for Return Expectations

The Council was established (under a slightly different name) two years ago. The background was as follows.

Danes have large pension savings. Together with the Netherlands, Denmark has the largest pension savings in the world relative to GDP (We have a paper describing some of the key features of the Danish pension system here; link). Historically, pension savings have been guaranteed, meaning that pension holders were guaranteed a minimum average annual return on their pension savings. Because of low interest rates, longer life-expectancies, etc., Danish pension funds have shifted into so-called market-based pension products during the last decade or so. In these products, there is no minimum guaranteed return. This allows the pension funds to invest more freely, hopefully enabling them to secure higher risk-adjusted returns. Basically, you go from a constrained to an unconstrained (or at least less constrained) maximization problem, which should lead to a better outcome. However, when a pension holder is not guaranteed a minimum return, the expected pension payouts during retirement will obviously become more uncertain. The assumed expected returns and risks on the different assets that pension funds invest in thus become even more important (compared to a guaranteed pension product) for an individual’s expected pension payments during retirement.

A couple of years ago, I was approached by the Danish pension industry and asked whether I would help them design a set-up that could be used to generate expected returns on pension funds’ investments. The result was the following: based on inputs from international financial institutions, present expected returns over the next ten years on ten different asset classes, as well as long-run (> 10 years) expectations on two assets classes (stocks and bonds), and update these forecasts annually. The pension industry judged that an independent committee should specify and regularly update the return expectations, in order to secure arms-length. They asked me if I would chair this committee. The Committee is now called the Council for Return Expectations

Last year, banks in Denmark asked whether we (The Council) would be able to expand the set of return assumptions. The background was – probably fair to say – a scandal in the largest bank in Denmark. Danske Bank had advised some of its customers to invest in an investment product that the bank itself expected would yield a lower return than a bank deposit. I.e., the bank had advised its customers to invest in a suboptimal product, given expected returns. It led to the resignation of the interim Danske Bank CEO (link). The Danish banking sector concluded that arms-length was needed in determining return expectations used for investments outside pension savings. They asked the Council if we could include return expectations for a shorter horizon (1-5 years, in addition to the 1-10 years horizon) and a more frequent update of the return assumptions (twice a year, instead of annually). These forecasts were published last week.

Procedure for determining expected returns

We provide forecasts for nominal returns on ten broad asset classes. Having done extensive research on the determinants of expected returns myself, I know that even within a precisely defined asset class, forecasters can disagree substantially on the outlook for the asset class. When designing the set-up, I thus suggested that return expectations should be based on an arms-length principle and take estimation uncertainty into account. We do this by basing our expectations on inputs from several international investment houses.

We (The Council) receive inputs from Blackrock, J.P. Morgan, Mercer, and State Street. We are truly thankful for their help.

Blackrock, J.P. Morgan, Mercer, and State Street provide their estimates of expected returns, standard deviations, and correlations on each of the ten asset classes for each of the different horizons to the Council. The ten asset classes for which we provide expected returns are:

  • Government and Mortgage Bonds
  • Investment-grade bonds
  • High-yield bonds
  • Emerging market sovereign bonds
  • Global equity (developed markets)
  • Emerging markets equity
  • Private equity
  • Infrastructure
  • Real estate
  • Hedge funds

The horizons are:

  • 1-5 years.
  • 6-10 years.
  • 1-10 years.

Returns are nominal. Estimates are for the arithmetic average annual return per year during the different horizons.

Returns are hedged into euros, i.e. are euro returns, except from emerging market equities and local currency emerging market debt that are unhedged (emerging market debt is 50%/50% local/hard currency). The Danish kroner is pegged to the euro, i.e. euro returns are basically also Danish Kroner returns. Returns are before fees, expect from the last four asset classes (private equity, Infrastructure, real estate, and hedge funds) that are after fees to the funds that manage these types of investments, but before the fees to the Danish pension/mutual fund that in turn invests in the private equity funds, hedge funds, etc.

In addition, we provide forecasts for Danish inflation based on inputs from a number of Danish forecasters (the Danish central bank, ministry of finance, etc.).

The Council consists of three people: Torben M. Andersen (professor of economics, University of Aarhus; link), Peter Engberg Jensen (former CEO of Nykredit and current chairman of Financial Stabilitet; link ), and myself as Chairman. Our job in the Council is to specify the relevant asset classes and their characteristics, choose methods used to weight the inputs together, come up with reasonable forecasts, communicate these to the public, etc.

Forecasts of expected returns

The following table presents our forecasts of expected annual returns for the next five years, the next ten years, and years six through ten:

Source: Council for Return Expectations.

Over the next five years, we expect “safe” investments (government bonds and Danish mortgage bonds) to return a negative 0.3% per year on a pre-fees, pre-taxes, and pre-inflation basis. This is a low return. On a net-of-fees, after-tax, and real-terms basis, it is even lower. In the Danish media, much was written about this number (-0.3%). For finance professors and professionals, the number is no big surprise given low interest rates, but for ordinary investors, the publication of numbers such as these from trustworthy sources helps communicating the message that it is difficult to earn a decent return these days without taking on risk.

On a more technical term, the return on this asset class is the return to a bond portfolio consisting of 50% Danish mortgage bonds (that are triple-A rated, as you probably know), 20% Danish government bonds, and 30% Euro government bonds, with a duration of five years.

There is a wide dispersion across the different asset classes. For instance, we expect emerging market equity to yield 9.5% per year. There is thus an almost ten-percentage point difference between the expected return on the safest asset class and the asset class yielding the highest expected return.

Notice also that we generally expect higher returns after five years. We expect higher interest rates, causing capital losses and thus low returns during the first five years, but then higher returns later on. This in itself helps raising expected returns on other asset classes.

Standard deviations

In the Council, we want to stress that estimates of expected returns are surrounded by uncertainty. To the finance professionals, this is obvious (though, one might sometimes have the impression that even professionals tend to forget this), but to the ordinary investor, this is even more important to emphasize. The ordinary investor might otherwise conclude that when expected returns on emerging markets is 9.5% per annum, but government bonds are expected to yield a negative 0.3% per annum, I better put all my money in emerging market equity. We want to stress that this is a risky strategy. This ambition has guided us when it comes to our estimation of standard deviations.

The expected returns we present are constructed as simple unweighted averages of the expected returns of Blackrock, J.P. Morgan, Mercer, and State Street, asset-class by asset-class. They also provide us with their expected standard deviations. We do not use the simple averages of these as our estimates of expected returns, though. Instead, we regress the standard deviations we receive from the investment houses on the expected returns received from the same investment houses. The standard deviations the Council presents are then the fitted values from this regression. The main objective we achieve by following this procedure is that we make sure that there is a clear relation between risks and returns.

The standard deviations surrounding the estimates of expected returns are here:

Source: Council for Return Expectations.

We expect a 3.5% standard deviation of government and mortgage bonds (over the next ten years) whereas we expect a 29.6% standard deviation surrounding the estimated returns to emerging markets equity. There is thus a clear relation between expected risks and returns. This clear relation also appears from this graph that plots risks on the ten asset classes against their returns (1-10 years horizon):

Source: Council for Return Expectations.

One might say that the price we pay from estimating standard deviations in this way is that some of the individual estimates of standard deviations might differ slightly from market consensus. As an example, our estimate of the standard deviation of global equity is around 14%. Market consensus probably is that this is a little higher, at 16%-17%. On the other hand, the advantage we obtain from proceeding in this way, as mentioned, is that we secure a clear relation between risk and returns.

Correlations

We also present correlations between expected returns. Here they are for the first ten years:

Source: Council for Return Expectations

Correlations are simple averages of the correlations we receive from Blackrock, J.P. Morgan, Mercer, and State Street, asset-class by asset-class. The main thing to notice probably is that we expect all correlations to be positive. This is bad news for investors, one might argue, but we saw this during the March turmoil. In March, bonds and stocks both lost in value, i.e. bonds did not hedge the risk of stocks. Our correlations reflect this.

The correlations and standard deviations are used by the pension funds to calculate confidence intervals surrounding the individual’s expected pension income. I want to stress that it is advanced – and good! – that pension holders in Denmark not only learn about their expected pension income but also the uncertainty surrounding this expectation.

Inflation

We expect the Danish rate of inflation to be 1.2% over the next five years and 1.4% over the next ten years.

All the numbers

All the numbers – means, standard deviations, correlations, for all the horizons, etc. – are available on the webpage of the Council (link). On this webpage, addition information about the Council can be found as well.

Conclusions

We live in low-interest times. Investors might be tempted to invest more risky in order to generate a decent return. But what are decent returns? And how much extra risk do investors incur when investing more risky? In most countries, you get one set of answers when you visit one pension fund/bank/financial advisor, but another set of answers when you visit a different pension fund/bank/financial advisor. This makes investors uncertain and do not help building trust between the financial sector and investors. The financial sector in Denmark has found a cool way of presenting independent/arms-length forecasts. I hope that other countries might be inspired from this way of addressing this important issue.

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PS. In my last post (link), I wrote about a new paper I have written on the financial stability in the Nordics. The journal where the paper is published is now available (link). It contains my own article and articles on the banking union, bail-in debts, household debts, and macroprudential policy, as well as a number of enlightening discussions.

Zero-probability events that happened

Some of the events we have seen during this crisis are so unlikely that we need tricks to calculate their probabilities. This is a journey into the world of very small probabilities and very large numbers. I admit it is somewhat surreal. But it is important as these small-probability events can have long-lasting effects on economic activity.

The defining characteristic of this recession is the speed with which markets and economies started freefalling. From one day to the next, economies were shut down. Economic activity came to a complete sudden stop in some sectors. In different blogposts, I have used words such as “unprecedented”, “completely crazy”, “horrifying”, “tremendous”, etc. to describe what happened.

Using words like these is adequate, I think (I admit I was shocked by what happened), but I have come to a point where I want to be more precise. As economists, we want to put numbers on things. I want to calculate the probability of the events. This turned out to be more complicated than I had first imagined.

The probability of a 121-sigma event

On Monday, April 20, the oil price (West Texas Intermediate) closed at USD -38 for a barrel of oil. I describe this here (link). This was a drop of 306% relative to the closing price the previous trading day. Based on daily data since 1983, the average percentage change in the oil price is 0.03% and the standard deviation 2.5%. I called the event “completely crazy”, which I still believe is a fair description. Now, I want to know “how crazy” it was. What was the probability that it would happen?

The happy-go-lucky Professor of Finance, i.e. me, opens his Excel spreadsheet and enters the command used to find the probability that an event of -306 happens when data are normally distributed with mean 0.03 and standard deviation 2.5.

I press “Enter”. Excel returns “0”. The Professor of Finance thinks this look strange and increases the number of decimal places. Excel just shows “0.000….”. According to Excel, this is a zero-probability event. It could not happen.

But it did happen. I start remembering the statistic classes many years ago, and recall the discussions on low-probability events.

Luckily, I have helpful colleagues. I write CBS Professor of Statistics Søren Feodor Nielsen. He tells me that the probability I am looking for is below the “machine precision”. He also tells me that the probability itself might be extreme, but the logarithm to the probability is probably not. We calculate the log-probability (using a different statistical package than Excel). It is -7495.137, i.e. the likelihood is exp(-7495.137). This is, unfortunately, such a low number that a computer cannot calculate this either.

We then calculate the log base 10 probability. Now we have the result. The probability is 0.799 x 10^(-3255).

This is a zero followed by 3255 zeroes, and then 799. Practically zero, but not exactly zero. The event happened, but it is extremely unlikely. We can call it “completely crazy”, but we can also say that the probability is 0.799 x 10^(-3255).

There is another way to illustrate how unlikely this event was. When the probability is 0.799 x 10^(-3255), we should see this event happen every 1.2516 x 10^(3255) days. I.e., a number with 3255 numbers before the decimal place. I simply do not know what such a number is called (link). This is where it starts getting surreal.

How precise is this? We have calculated it, but Søren tells me we should not put too much faith in its precision. Tail-probabilities in the normal distribution are calculated by numerical approximations, and it is questionable how precise they are when we are that far out in tail. Admittedly, the question is of course also how important it is that it is precise that far out in the tail. The probability is unbelievably small. That is the main thing. Exactly how small is perhaps not that important.

S&P 500

This graph shows daily percentage changes in the S&P 500 throughout the last 50 years.

S&P 500. Daily data. May 1970 – May 2020.
Data source: Thomson Reuter Datastream via Eikon.

On March 13, the S&P 500 fell 12%. The average daily percentage change in the S&P 500 (calculated up until March 12) is 0.03% and the standard deviation is 1.05%.

The likelihood that we will see a 12% drop in the S&P 500 on a daily basis, given the behavior of the S&P 500 during the last 50 years, is very small, but not so small that it cannot be calculated in Excel. Excel says it is 1.0827 x 10^(-29).

These are daily data. If we assume 250 trading days per year, we should see this event happen every 3.69 x  10^(27) = 3,694,454,429,465,560,000,000,000,000 year. I.e., every 3.694 octillion year.

This is also somewhat surreal.

There are many caveats. We are again so far out in the tail that we should not put too much emphasis on the exact number of decimals and the exact number after all the zeroes. Also, all these calculations are based on the assumption of normally distributed data. Given that we saw a 21% fall in the S&P 500 on Black Monday, October 19, 1987, we have seen two very unlikely crashes within the last three decades. According to these calculations, they should happen much more seldom. We use the normal distribution, but data might follow a different distribution. For most practical purposes, however, it is not necessary to know the exact distribution of these extreme events. The important thing to know is that the likelihood is very small.

Other numbers

In this post (link), I describe how the initial jobless claims soared, confidence indicators fell, GDP dropped, etc. As an example, the likelihood that we should see almost 7 million people filing for jobless claims on March 28, given the complete previous history of jobless weekly claims, is 0.97 x 10^(-841). This is again such a small probability that Excel cannot even calculate it.

Economic effects

There is a broader point to these discussions. Events like those described above might have long-lasting consequences for economic activity.

When events are unlikely, we assign low probability to their occurrence. One hypothesis is that we keep on assigning low probabilities to their occurrence. In this case, future investment decisions of firms and future consumption decisions of households are unaltered by the events we have been going through. It was a temporary shock. It has very low probability. It will not influence how we form expectations going forward.

An alternative hypothesis is that we have become so scared by the event that it will haunt us for years to come. We update our beliefs disproportionally much.

Which of the two scenarios play out is important for the recovery from this recession. Will we start consuming when economies open up or will we hold back because we have become scared?

Kozlowski, Veldkamp and Venkateswaran (2020) have an important paper on this (link). Their main point is that events like those studied above, i.e. events that are very unlikely but have large impacts, will have persistent effects on beliefs. They write that “tail events trigger larger belief revisions” and that “because it will take many more observations of non-tail events to convince someone that the tail event really is unlikely, changes in tail beliefs are particularly persistent”. This is an important insight. If this is how we form expectations, it will slow down the recovery from this recession. Kozlowski et al. also show, however, how government interventions can reduce the effect, but not eliminate it.

Conclusion

These weeks we are seeing infection rates declining in many countries and economies opening up again. These are very good news. Let us hope we can start returning to some kind of normality. It will take time before we are back to where we came from, though. Some things might even have changed for good. One thing is for sure: we will not get back as fast as economies and markets fell in March. What happened was very unlikely. But it did happen.