Political Calculations
Unexpectedly Intriguing!
28 February 2020
Cover of Maths on the Back of an Envelope by Rob Eastaway

How powerful was the first atomic blast? How many cats are there in the world? How much is China's coronavirus epidemic impacting its economy?

These are three very unrelated questions. If you were tasked with responding to them, how could you possibly come up with reasonable answers to any of the three?

Each of these questions are examples of what might be called "Fermi problems", which are named after Italian-American physicist Enrico Fermi. In addition to his pioneering work in nuclear physics, Fermi was also known for doing back-of-the-envelope calculations to come up with estimates that made real world sense even when nobody had any idea of what the right answers might be.

In Rob Eastaway's latest book, Maths on the Back of an Envelope (more affordably available in the UK), which discusses 'clever ways to (roughly) calculate anything', he tells the story of how Fermi estimated the force generated by the first-ever atomic bomb blast using confetti and a few quick calculations just after it happened:

The story goes that Fermi and others were sheltering from the explosion in a bunker about six miles from ground zero. When the bomb went off, Fermi waited until the wind from the explosion reached the bunker. He stood up and released some confetti from his hand, and when it had landed, he paced out how far the confetti had traveled. He then used that information to make an estimate of the strength of the explosion. We don't know for certain how Fermi did this, but it probably involved him estimating the wind speed and working out how much energy was required to push out a 'hemisphere' of air from the centre of the explosion.

Fermi's estimate of the bomb's strength was 10 kilotons. Later, more rigorous calculations revealed that the real strength had been nearer to 18 kilotons, in other words Fermi's answer was out by a factor of nearly two. Anyone submitting an answer that far out in a maths exam would probably get no marks, yet Fermi got huge credit for the accuracy of his back-of-envelope answer. The important thing was that his answer was in the right order of magnitude, and gave scientists a much better understanding of the potential impact of the weapon they were now dealing with.

At the time, the power of an atomic blast was such an unknown that other scientists working on the project had been concerned enough before the test to do some pretty sophisticated math just to provide the reassurance to themselves that the atomic bomb they built would be unlikely to set off a chain reaction that would ignite the Earth's atmosphere and destroy the planet. Getting the order of magnitude of the actual event correct under such uncertainty is what makes Fermi's quick math such a big deal.

So what does all that have to do with the other two questions?

It has everything to do with how you can take information you know and logically chain it together with reasonable guesswork so that you can use it to extrapolate an estimate for the answer you're trying to reach. In his book, Rob Eastaway describes how he once estimated the global population of cats while speaking to a school assembly (we've added the image as an illustration because, well, cats):

Jari Hytönen - Cats in a Basket, via Unsplash

Let's assume that most cats are domestic.

Some people have more than one cat, but usually, a household has only one cat, if any at all.

In the UK, and thinking of my own street as an example, it seems reasonable to suppose that there might be one cat in every five households.

And, if a household contains on average two people, that means there is one cat for every 10 people.

So, with 70 million people in the UK, let's say that there are, perhaps, seven million cats in the UK.

So far, so good. But what about the number of cats in the rest of the world? It seems unlikely that cats are as popular in countries like India or China as they are in the UK (although what would I know? Remember, this is purely guesswork on my part), therefore, I'd expect the ratio across the world to be smaller than it is in the UK - maybe one cat for every 20 people?

So, with eight billion people in the world, that suggests there are maybe:

8 billion ÷ 20 = 400 million cats

It doesn't seem an outrageous number.

A member of Eastaway's audience searched the question on Google and it returned 600 million as the answer, which Eastaway takes as a sign that the math he did was on the right track, or rather, that whoever came up with that larger estimate likely went about working up their estimate using similar methods.

And that brings us to China, which is where economist David Tufte recently did some back-of-the-envelope math to assess how hard China's economy has been hit by the COVID-19 coronavirus epidemic that has killed thousands and sickened tens of thousands in the nation.

This may be the best real time estimate yet on what COVID-19 has done to the Chinese economy. China’s power plants run mostly on coal. China’s coal consumption appears to be down between 20 and 45%.

Daily coal consumption around the Chinese new-year period at six generating companies reporting daily data, in 10,000 tonnes per day. X-axis shows days before and after Chinese new year eve, which falls on various dates in the second half of January or in February. Source: Analysis of data from WIND Information.

This is measured in days since the Chinese New Year, which fell on January 25 this year. So, they’re usually down for about 10 days after that, and this slowdown has stretched on for almost a month now.

To get that to GDP we need to know China’s energy elasticity. A plausible value for any country is around one, estimates from 15 years ago suggest 1.5 is more suitable for China. Here’s the back of the envelope calculation:

  • Choose a round number for China’s GDP like $20,000B/yr
  • Coal consumption is down 20% to 45%
  • The elasticity suggests a hit of 30% to 70% for GDP
  • That’s $6,000B/yr to $14,000B/yr if it’s a discrete jump. It isn’t, so looking at that typical slope showing recovery by about day 25 in most years, that slope suggests effects so far that are perhaps half of that as China built up to a sustained shortfall.
  • This shortfall is new and gradual, let’s say it’s about 1/25th of a year so far (about 2 weeks). That converts to a GDP loss of between $120B and $280B so far, or –0.6% to –1.4% of annual GDP in total.
  • China’s economy in 2020 is roughly the size of the U.S. economy in 2008-9. During the worst parts of that recession, the U.S. economy was off $20B in 2008 III, $85B in 2008 IV, and $45B in 2009 I.

All of these numbers are sketchy, but the suggest that the effect of COVID-19 on China over a few weeks is already comparable to what a large recession did to the U.S. in a few quarters.

Given China's role in global supply chains, both as producers and as consumers, that magnitude of economic impact will affect other national economies, bringing a global recession into view, which is contributing to why the world's stock markets are plunging.

It will be a long time before all the actual damage has been tallied, but until then, the kind of estimations we can do using back-of-the-envelope maths will give us the best indication of how things are going. It's also why we can recommend Maths on the Back of an Envelope as entertaining reading material that you can actually put to use to answer questions that, on first glance, may appear to be unanswerable.

Previously on Political Calculations

Image credit: unsplash-logoJari Hytönen

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27 February 2020

Nearly two months ago, it looked like the new home market in the U.S. was set to follow the trends for existing home sales in ending 2019 on a high note.

And then, that scenario went on to happen! The release of January 2020's preliminary estimates for new home sales suggests that momentum carried into 2020.

Sales of new U.S. single-family homes raced to a 12-1/2-year high in January, pointing to housing market strength that could help to blunt any hit on the economy from the coronavirus and keep the longest economic expansion in history on track.

The report from the Commerce Department on Wednesday added to a raft of other upbeat data on the housing market, which is emerging as one of the few bright spots on the economy as business investment continues to slump and consumer spending slows. Unseasonably mild weather and the lower mortgage rates that followed the Federal Reserve’s three interest rate cuts last year are boosting housing market activity.

“The strength of new home sales should generate a little extra consumer expenditures on household items like appliances and furniture, and the spending is sorely needed because the coronavirus is likely to weigh on GDP growth in the first quarter,” said Chris Rupkey, chief economist at MUFG in New York. The Commerce Department said new home sales jumped 7.9% to a seasonally adjusted annual rate of 764,000 units last month, the highest level since July 2007. December’s sales pace was revised up to 708,000 units from the previously reported 694,000 units....

New home sales jumped 30.3% in the Midwest to their highest level since October 2007. They soared 23.5% in the West to levels last seen in July 2006 and rose 4.8% in the Northeast to more than a 1-1/2-year high. Activity was likely exaggerated by warmer temperatures. Sales fell 4.4% in the South, which accounts for the bulk of transactions.

For the market capitalization of new home sales, 2019 proved to be the strongest year since 2007. That result can be seen in the following chart showing the trailing twelve month average of the market capitalization for non-seasonally adjusted new home sales in the United States.

Trailing Twelve Month Average New Home Sales Market Capitalization, January 1976 - January 2020

That's also true after adjusting for the effects of inflation over time. The change occurs as the median new home sold in the U.S. has become more affordable for the typical American household, both because of rising incomes and because of falling mortgage rates in 2019.


U.S. Census Bureau. New Residential Sales Historical Data. Houses Sold. [Excel Spreadsheet]. Accessed 26 February 2020. 

U.S. Census Bureau. New Residential Sales Historical Data. Median and Average Sale Price of Houses Sold. [Excel Spreadsheet]. Accessed 26 February 2020. 

U.S. Department of Labor Bureau of Labor Statistics. Consumer Price Index, All Urban Consumers - (CPI-U), U.S. City Average, All Items, 1982-84=100[PDF Document]. Accessed 13 February 2020. 


26 February 2020

We're going to take a short break from our ongoing coverage of the impact of China's coronavirus epidemic on the U.S. stock market to focus on a bright spot in the U.S. economy: rising existing home sales.

December 2019 saw the strongest aggregate totals for existing home sales in years, with the equivalent market cap for this portion of the U.S. economy reaching $1.63 trillion based on Zillow's available data.

Estimated Aggregate Existing Home Sales, 49 States and District of Columbia*, January 2016 to December 2019

A combination of both rising prices and rising number of sales contributed to 2019's strong finish, with the Northeast and South regions showing the greatest strength.

Estimated Aggregate Existing Home Sales, U.S. Census Northeast Region, January 2016 - September 2019
Estimated Aggregate Existing Home Sales, U.S. Census South Region, January 2016 - September 2019

Aggregate sales were also up in the Midwest and West regions of the U.S., although not as strongly as in the Northeast and in the South.

Estimated Aggregate Existing Home Sales, U.S. Census Midwest Region, January 2016 - September 2019
Estimated Aggregate Existing Home Sales, U.S. Census West Region, January 2016 - September 2019

Meanwhile, seasonally adjusted aggregate sales were also up in each of the U.S.' top five states for existing home sales in December 2019:

Estimated Aggregate Transaction Values for Existing Home Sales in Top Five States, January 2016 to December 2019

California and Texas are still both below their March 2018 peaks, but aggregate existing home sales in Florida, New York, and New Jersey are reaching new relative highs. It should be noted however that they are doing so mainly the basis of rising prices rather than a rising number of sales.

That was true across most of the United States. Zillow indicates 40 states recorded new peaks in their seasonally adjusted median sale prices for existing homes in December 2019, where Zillow's available data extends as far back as March 2008 for 37 of these states.

New home sales through January 2020 will be out later today, which we'll follow up tomorrow since they represent a much bigger contributor to the U.S.' gross domestic product.


25 February 2020

Statistically speaking, the S&P 500 (Index: SPX) got pretty interesting yesterday, where we define "interesting" as any change in the level of the S&P 500 of 2% or more.

In falling 3.35%, the decline in the S&P 500 on Monday, 24 February 2020 falls into pretty rarefied territory, where in the 70 years since 3 January 1950, covering some 17,649 trading days for which we have data, the S&P 500 has only recorded drops 2.85% or more on just 133 days.

That's not as rare as you might think, because on average, the market has seen drops like that on average every 132-133 days. Except, when volatility in the market cranks up to levels like that, big changes in the level of the S&P 500 are not evenly distributed over time, but instead tend to be clustered close to each other when they occur. The following chart shows all the daily percentage changes in the S&P 500 and its predecessor indices from 3 January 1950 through 24 February 2020, where the biggest changes fall outside the red-dashed lines, which represent daily percentage changes that are more than three standard deviations away from the mean trend for the index.

S&P 500 Daily Volatility (Percent Change Between Closing Value and Previous Trading Day's Closing Value), 3 January 1950 - 24 February 2020

Looking at the level of the S&P 500 itself, the change that took place on 24 February 2020 wasn't all that surprising. To help make that point, we've animated the following chart showing the forecast from the dividend futures-based model we use to project the future trajectory of the S&P 500 that we presented long before the market opened on Monday to what it looks one trading day later. If you're accessing this article on a site that republishes our RSS news feed, please click through to our site to access a working version of the animated chart.

Animation: Alternative Futures - SP 500 - 2020Q1 - Standard Model - Snapshots 20200221 and 20200224

The heavy black line representing the actual trajectory of the S&P 500 is closely paralleling the orange-dashed line representing the trajectory the dividend futures-based model indicates would apply if investors are closely focusing on 2020-Q4 in setting current day stock prices. You'll also note a big change in the future forecast about 30 days from now, which is an echo of the volatility event that just took place. This echo is an artifact of our use of historic stock prices in projecting future stock prices, where the important thing to know right now is that it will be in flux until things settle down.

As for what to pay attention to in the days ahead, the big question now is not whether the Federal Reserve will be cutting short term interest rates in the U.S. in 2020, but how many times it will in 2020. At the end of the last week, investors were betting on two rate cuts, but as investors respond to growing indications of massive supply chain disruptions resulting from China's coronavirus epidemic, the odds are increasing there will be a third. We've animated the probabilities recorded by the CME Group's FedWatch tool for what the Fed will do with the Federal Funds Rate at upcoming meetings of its Federal Open Market Committee to show how it has changed from what we presented yesterday:

Animation: CME Group FedWatch Tool - Probabilities of Federal Funds Rate Changes at Upcoming FOMC Meetings - Snapshots 20200221 and 20200224

To summarize what the FedWatch tool is indicating, investors are by betting the Fed will be forced to implement quarter point rate cuts in both 2020-Q2 and 2020-Q3, and the odds of a third rate cut in 2020-Q4 have risen.

That uncertainty for 2020-Q4 accounts for why the alternative futures spaghetti forecast chart is indicating investors are remaining mostly focused on 2020-Q4 at this time. However, if more bad news erupts to cause investors to shift their forward-looking focus earlier in the year, particularly to 2020-Q2, such a change in focus would likely be accompanied by a much larger drop in stock prices.

And that's without any changes in investor expectations for future dividends. If those fundamental expectations change, say with companies acting to cut dividends to preserve their cash flows because they anticipate sharply lower revenues and earnings from experiencing supply chain disruptions, then the market could be in for a really rocky ride.

We told you the S&P 500 was getting pretty interesting. The next several days should see quite a lot of action, particularly from central banks looking to head off as much trouble as they can, which will have an effect on stock prices. How much remains to be seen.

Update 25 February 2020: More bad news erupted today, so now we're looking at a genuine new Lévy flight event, with investors shifting their focus from 2020-Q4 inward toward the nearer-term future of 2020-Q2:

Will the new Lévy flight event continue, or will something change to refocus investors on the more distant future horizon? We'll see what tomorrow brings!

Update 26 February 2020: The S&P 500 continued down, but by a small amount compared to the previous two days:

Update 27 February 2020: Another big drop as the market's latest Lévy flight event continues. We're likely within several percent for where the market will stabilize in the short term as investors have nearly completed shifting their forward-looking focus from 2020-Q4 to 2020-Q2.

The S&P 500 has recorded its fastest ever correction (a decline of at least 10%) at this point.

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24 February 2020

Coronavirus epidemic-related news continued to dominate the headlines most relevant for S&P 500 (Index: SPX) in the third week of February 2020, sending the index up to a new record high of 3,386.15 on Wednesday on expectations of a much bigger economic stimulus out of China, before also sending stock prices downward to close the week 1.4% lower as the virus' impact to global supply chains started to draw attention.

And yet, the S&P 500 continued to behave predictably, with its trajectory following the path defined by a dividend futures-based model assuming investors are focusing on 2020-Q4 in setting current day stock prices.

Alternative Futures - S&P 500 - 2020Q1 - Standard Model - Snapshot on 22 Feb 2020

The reason we believe investors are focusing on the distant future quarter of 2020-Q4 is because that's when investors are increasingly betting the U.S. Federal Reserve will be compelled to cut interest rates by a quarter point for a second time in 2020 to stimulate economic growth, following an already expected quarter point rate cut in 2020-Q3. The latest rate change probabilities indicated by the CME Group's FedWatch tool confirm that growing expectation:

CME Group FedWatch Tool Probabilities of Federal Funds Rate Changing at Future FOMC Meeting Dates, Snapshot on 21 February 2020

That doesn't mean that Fed officials are happy about that situation, where many are clinging to the hope they can avoid additional rate cuts. And as you'll see among the market-moving headlines of the past week below, at least one Fed official is counting on China's coronavirus epidemic dissipating to justify avoiding additional rate cuts.

Tuesday, 18 February 2020
Wednesday, 19 February 2020
Thursday, 20 February 2020
Friday, 21 February 2020

Meanwhile, if you're looking for more context for what else was going on in the Presidents Day holiday-shortened week, Barry Ritholtz lists the positives and negatives he found in the past week's economics and market-related news - we like how he framed the week's Number 1 negative!

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21 February 2020

COVID-19 is the name the World Health Organization has given to the highly contagious and deadly new viral infection that has severely impacted China's economy as it threatens to become a pandemic.

The world recently ran an experiment on how potentially contagious the viral infection formerly known as Novel Coronavirus 2019 (or 2019-nCoV) on a cruise ship, which was quarantined in Japan from 3 February 2020 through 19 February 2020. During that period, some 3,711 passengers and crew on board the Diamond Princess were at an elevated risk of exposure to the new coronavirus, as the ship effectively became a 'super spreading' site. In those 16 days, the number of passengers and crew testing positive for the COVID-19 infection grew from 1 to 621. The following chart tracks the cumulative number of passengers testing positive for the COVID-19 infection on each day of the Diamond Princess' quarantine period.

Reported Cases of Diamond Princess Passengers and Crew Testing Positive for COVID-19 Virus Infection by Day

Cruise ships have earned notorious reputations for becoming incubators for contagious diseases given their design, where people are contained in close quarters that provide an environment that is conducive to the spread of diseases. In the case of the Diamond Princess, in addition to sharing these characteristics, the quarantine practices put into effect on the ship after the first passenger was confirmed to have been infected proved to be ineffective in slowing the spread of the COVID-19 viral infection. The measures taken have been described as "an improvisation", including the fiasco of having the wrong masks initially provided to passengers. After the correct face masks were finally provided, the passengers were not adequately trained to fit them properly, impairing their effectiveness.

All these problems combined are why a National Institute of Health official described the ship's attempted quarantine as "ineffective", as the Diamond Princess became the home of the largest cluster of coronavirus cases outside China in the epidemic's earliest phase.

The failed quarantine of the Diamond Princess then gives us an example of how fast the COVID-19 coronavirus might spread in a crowded city with little-to-no effective quarantine procedures in place to slow the spread of the viral infection. But can we really tell anything about how fast it might spread from just 16 days worth of data?

The answer to that question is yes, provided we have a growth model that can provide a good indication of how quickly the disease could spread through an entire susceptible population.

To that end, we can apply the mathematical growth models that have been developed to track the progression of diseases in plants. The following figure illustrates the various growth models that might be employed:

Figure 1. Examples of disease progress curves represented by monomolecular, exponential, logistic, and Gompertz models

The following slideshow gives more background into these models:

Based on what has been learned about the COVID-19 virus, it fits the profile of a polycyclic disease. An exponential model might be used to describe its early phases, but a logistic (or Richards) growth model or a Gompertz growth model will more closely match its full progression.

But which to use? A 2015 paper by Wendi Liu, Sanyi Tang, and Yanni Xiao gives the edge to the general logistic growth model, although a 2017 paper by C.R. Sebrango-Rodriguez, D.A. Martinez-Bello, L. Sanchez-Valdes, P.J. Thilakarathne, E. Del Fava, Patrick Van Der Stuyft (UGent), A. Lopez-Quilez and Z. Shkedy argues in favor of averaging the models' results together.

For our part, we built a tool to model the progression of the COVID-19 virus onboard the Diamond Princess using both the logistic and Gompertz growth models, where we've assumed that all those on the ship would be susceptible to becoming infected if they had remained on board. To use the tool, just select the growth model for which you would like to see the results and enter the number of days you would like to project results for. If you're accessing this article on a site that republishes our RSS news feed, please click through to our site to access a working version.

Growth Model and Number of Days
Input Data Values
Select Growth Model
Number of Days Since First Reported Case

Projected Spread of Infection
Estimated Results Values
Modeled Percentage of Population Infected

The tool's results are expressed in terms of the percentage of the population affected, which for the Diamond Princess, totals 3,711 individuals.

In playing with the tool with our basic assumptions, we find that virtually all of the passengers and crew would have been likely to have become infected with the COVID-19 coronavirus if they had remained on the ship for more than 60 days, regardless of which growth model we select. The following chart visualizes the growth models' projections, where we find the COVID-19 virus would have been likely to spread to 95% or more of the 3,711 passengers and crew quarantined on the Diamond Princess after 60 days.

Reported and Modeled Percentage of 3,711 Passengers and Crew Testing Positive for COVID-19 While In Quarantine Onboard the Diamond Princess Cruise Ship

In reality, it's more likely that not all passengers would have been susceptible to the COVID-19 virus, but that's the conservative assumption to make for any new virus until we learn more about it.

We should also note that the data for the cumulative number of infected passengers and crew may not be representative of the true rate of spread of COVID-19. Japan's national health laboratories only had the capacity to run 300 tests per day during most of the quarantine period, where the daily numbers reported may well have been understated.

Speaking of which, here are our sources for the cumulative daily numbers of infected passengers and crew on the Diamond Princess:

Reported Cases of Diamond Princess Passengers and Crew Testing Positive
for COVID-19 Virus Infection During Its Quarantine Period
Date Number Tested Positive Source of Figure
03 February 2020 1 Princess Cruise Ship Temporarily Delayed for Coronavirus Testing
04 February 2020 10 10 Test Positive for Coronavirus Aboard Carnival's Diamond Princess
05 February 2020 20 20 people test positive for coronavirus on board Carnival’s Diamond Princess cruise in Japan
06 February 2020 41 Coronavirus cases triple to 61 on cruise ship quarantined in Japan, with 41 more reported
07 February 2020 61 Oregon woman infected with coronavirus quarantined in Japan
08 February 2020 67 Diamond Princess cruise ship has 67 passengers who have tested positive for coronavirus onboard, Japan's Health Minister announced
09 February 2020 69 Inferred from 10 February 2020 report.
10 February 2020 135 Diamond Princess cruise ship coronavirus cases double to 135; Marysville woman still healthy
11 February 2020 174 Japan confirms 39 new virus cases, 174 total on cruise ship
12 February 2020 174 No reports indicating change in number of positive test results for COVID-19 infection.
13 February 2020 218 44 more on Diamond Princess cruise ship test positive for COVID-19
14 February 2020 218 No reports indicating change in number of positive test results for COVID-19 infection.
15 February 2020 285 Travis Air Force Base Prepares For Coronavirus Airlift Of Diamond Princess Passengers
16 February 2020 355 Virus spreads on ship in Japan, American passengers set to disembark
17 February 2020 454 99 more coronavirus cases reported on cruise ship docked in Japan
18 February 2020 542 Japan says 88 more virus cases confirmed on quarantined ship
19 February 2020 621 79 New Cases Of Coronavirus On Quarantined Japan Cruise Ship Take Toll To 621

Since Japan's official quarantine ended on 19 February 2020, two Diamond Princess passengers who became infected with the COVID-19 coronavirus have died.


Wendi Liu, Sanyi Tang, and Yanni Xiao. Model Selection and Evaluation on Emerging Infectious Disease Data Sets including A/H1N1 and Ebola. Computational and Mathematical Methods in Medicine. 2015; DOI: 10.1155/2015/2017105.

SEBRANGO-RODRÍGUEZ, C., MARTÍNEZ-BELLO, D., SÁNCHEZ-VALDÉS, L., THILAKARATHNE, P., DEL FAVA, E., VAN DER STUYFT, P., LOPEZ-QUILEZ, A., SHKEDY, Z. (2017). Real-time parameter estimation of Zika outbreaks using model averaging. [PDF Document]. Epidemiology and Infection. (2017), 145, 2313–2323. DOI: 10.1017/S0950268817001078.

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20 February 2020

Every three months, we take a snapshot of the expectations for future earnings in the S&P 500 (Index: SPX) at approximately the midpoint of the current quarter, shortly after most U.S. firms have announced their previous quarter's earnings.

While the earnings outlook for the S&P 500 slipped somewhat since our Fall 2019 snapshot, based on the data available as of 14 February 2020, trailing year earnings for 2019-Q4 have rebounded strongly over 2019-Q3, marking the end of a very short-lived earnings recession.

Forecasts for S&P 500 Trailing Twelve Month Earnings per Share, September 2014-December 2021, Snapshot on 14 February 2020

Looking past the earnings data for 2019-Q4 that is still in progress for being reported, we see very little change projected for earnings through the end of 2020, where forecast trailing year earnings per share dipped slightly from the $161.95 per share projected on 11 November 2019 to $162.22 per share in this snapshot.

Meanwhile, Standard and Poor has extended their earnings forecast for the S&P 500 out through the end of 2021, with the trailing year earnings per share in the fourth quarter of that year initially set at $176.20.

In the next snapshot, we'll simplify the chart by resetting the horizontal axis to start with December 2016.

Data Source

Silverblatt, Howard. Standard & Poor. S&P 500 Earnings and Estimates. [Excel Spreadsheet]. 14 February 2020. Accessed 17 February 2020.

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19 February 2020

The number of dividend cuts announced through the midpoint of the first quarter of 2020 is generally on track with the number that had been declared by this point of time in the first quarter of 2019. The following chart shows how the cumulative total of dividend cuts in 2020-Q1 compares with the year-ago quarter of 2019-Q1, where the pace of dividend cuts in 2020-Q1 falls well within the "relatively healthy" portion of the chart.

Cumulative Total of Dividend Cuts in U.S. by Day of Quarter, 2019-Q1 vs 2020-Q1, Snapshot on 14 February 2020

Through 14 February 2020, we've counted a total of 24 dividend cuts announced through our two near-real time sources of dividend declarations, Seeking Alpha and the Wall Street Journal. We've broken that full list up into two different categories, one for firms that pay variable dividends to their shareholders, with the amount of the dividends directly linked to their revenues and earnings, and the other for firms that fix the amount of their dividend payments independently of their business performance.

Here's the first part of the list of dividend cuts announced by the variable dividend paying firms through the halfway point of 2020-Q1.

Eight of these firms hail from the oil and gas industry, where the number of firms is consistent with the typical month-to-month noise we see among variable dividend payers, which is often tied to the fluctuating prices of oil and gas. Of the remaining firms, two are in the mining industry, one is a technology firm, and one is in the financial industry.

Here's the list for the firms that set their dividends independently of their earnings and cash flow:

Seven of these firms are in the U.S. oil and gas industry, but this list is significant in that several are midstream producers, whose businesses involve transporting oil and gas from wells (upstream) to end users (downstream), usually through pipelines, where the firms are cutting their dividends to address high debt loads on their books, which is an indication they are not seeing the revenues they had hoped to gain by investing in their infrastructure.

Beyond that total, we count two firms in the manufacturing sector of the U.S. economy, two are real estate investment trust, and there is one firm each from the mining and technology sectors.

Of these, the two manufacturing firms U.S. Steel (NYSE: X) and Spirit AeroSystems (NYSE: SPR) because we're starting to see some of the anticipated fallout from the global recession in the automotive industry, where we would advise paying close attention to debt-burdened firms like Ford (NYSE: F), and also to firms affected by Boeing's ongoing problems in the aerospace industry, which is now rippling through the company's supply chain, and which may take years to recover, even if Boeing (NYSE: BA) soon receives FAA approval to resume production of its troubled 737 MAX commercial transport aircraft.


18 February 2020

The U.S. Federal Reserve has something of a love-hate relationship with its latest liquidity injection policy. The Fed keeps having to supply money markets with more and more liquidity, which has contributed to boosting the S&P 500 (Index: SPX) to four new record highs in the last week, but Fed officials are signaling they really want to stop.

That may be why investors appear to now be focusing strongly on 2020-Q4 in setting today's stock prices, as suggested by the dividend futures-based model that underlies our alternative futures spaghetti forecast chart for stock prices:

Alternative Futures - S&P 500 - 2020Q1 - Standard Model - Snapshot on 14 Feb 2020

This brings us to something we missed in the data in the previous edition of our S&P 500 chaos series, where we took a cautious note in reading the week's data:

This is where we have to add a note of caution, because we're dealing with a rapidly evolving situation in the markets. If you look at the alternative futures chart above, if investors shift their attention fully toward 2020-Q3 in setting current day stock prices, the S&P 500 could continue to rise by as much as 250-300 points. But, if China's and the Fed's liquidity injections work to stabilize markets as intended, the FedWatch tool's probability estimates may only have caught investors in the process of shifting their focus more fully back toward 2020-Q4, which is consistent with the S&P 500's recent trajectory, which would also mean that stock prices are within a few percent of where they might go.

What we missed is that the reason investors would now be fixing their focus more tightly on 2020-Q4 in setting current day stock prices is because they are betting that this distant future quarter will see the Fed cut short term interest rates for a second time in 2020. According to the CME Group's FedWatch tool, investors are now giving a 46% probability they will follow a quarter point rate cut in 2020-Q3 with another in 2020-Q4.

CME Group FedWatch Tool Probabilities of Federal Funds Rate Changing at Future FOMC Meeting Dates, Snapshot on 14 February 2020

Consequently, unless the arrival of new information leads investors to alter that expectation, we think the liquidity operations that Fed officials hate will be with us for quite some time longer than they would like.

In any case, here are the more significant market-moving headlines we found in the past week's news stream:

Monday, 10 February 2020
Tuesday, 11 February 2020
Wednesday, 12 February 2020
Thursday, 13 February 2020
Friday, 14 February 2020

Over at the Big Picture, Barry Ritholtz succinctly summarizes all the positives and negatives he found in the past week's economics and market-related news.

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About Political Calculations

Welcome to the blogosphere's toolchest! Here, unlike other blogs dedicated to analyzing current events, we create easy-to-use, simple tools to do the math related to them so you can get in on the action too! If you would like to learn more about these tools, or if you would like to contribute ideas to develop for this blog, please e-mail us at:

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