Political Calculations
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January 28, 2020

As of the end of 2019, the total market capitalization of the S&P 500 was $26,759,686,786,884. Or if you prefer, approximately $26.76 trillion!

S&P 500 Market Capitalization, 1988-Q1 through 2019-Q4

That's 14.8 times the value of the S&P 500's market capitalization at the end of 1988-Q1, when the index' market cap stood at $1.81 trillion. From 1988-Q1 to 2019-Q4, the S&P 500's total market capitalization has doubled three times, completing its first doubling period in 7 years from 1988-Q1 to 1995-Q1, taking another 2.5 years to double again by 1997-Q3, and then another 16 years to double a third time in 2013-Q3.

Measured a little differently, the market cap of the S&P 500 as a percentage of the U.S. Gross Domestic Product at the end of 2019-Q3, the most recent quarter for which we have a somewhat finalized estimate, was 114.7%. That's close to the highest the S&P 500's total market cap has been since the days of the Dot-Com Bubble, when that figure peaked at 126.8% in the first quarter of 2000.

S&P 500 Market Capitalization as Percentage of U.S. GDP, 1988-Q1 through 2019-Q3

We won't know until the end of March 2020 how the S&P 500's market cap compares to the size of the U.S. economy through the end of 2019, when the estimate for the United States' GDP in 2019-Q4 is somewhat finalized.

References

Silverblatt, Howard. Standard & Poor Index Earnings and Estimates. [Excel Spreadsheet]. 16 January 2020. Accessed 23 January 2020.

U.S. Bureau of Economic Analysis. GDP and Personal Income Interactive Data. National Income and Product Accounts. Table 1.1.5. Gross Domestic Product. [Online Database]. Accessed 23 January 2020.

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January 27, 2020

The S&P 500 (Index: SPX) completed the Lévy flight event it began last week, with investors fully shifting their forward-looking focus to 2020-Q4 in setting current day stock prices, as expected.

Alternative Futures - S&P 500 - 2020Q1 - Standard Model - Snapshot on 25 Jan 2020

As of the close of trading on Friday, 24 January 2020, the level of the S&P 500 was 3,295.47.

The shift in focus was completed as the outbreak of a new SARS coronavirus in China came to dominate headlines during the week. Here are the market-moving headlines we noted during the holiday-shortened trading week of January 2020:

Tuesday, 21 January 2020
Wednesday, 22 January 2020
Thursday, 23 January 2020
Friday, 24 January 2020

Elsewhere, Barry Ritholtz lays out a short list of the positives and negatives he found in the past week's economics and market-related news. Between us and him, did we catch them all during the MLK holiday-shortened trading week?


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January 24, 2020
Figure being scanned by lasers - Source: Unsplash - David Anderson - https://unsplash.com/photos/FahhGNl16iM

Artificial intelligence, or AI for short, is creeping into all sorts of real world applications. Want to order cat food from Amazon? Ask Alexa via your smart speaker! Want to take the drudgery out of driving on a long road trip? Have your self-driving car do the driving for you!

Now, if you followed the links in the preceding paragraph, you've not only found stories involving how people are using AI-equipped devices today, you've also found some cautionary tales where machine learning hasn't been as up to the tasks to which it has been put, and certainly not as advertised. Some of these stories are kind of funny. Others have very tragic endings.

That's because many of the most successful AI systems that have been developed to date have limitations that aren't well understood. A recent article in Quanta explores some of those limitations and how researchers are turning toward mathematical physics to address them:

The revolution in artificial intelligence stems in large part from the power of one particular kind of artificial neural network, whose design is inspired by the connected layers of neurons in the mammalian visual cortex. These “convolutional neural networks” (CNNs) have proved surprisingly adept at learning patterns in two-dimensional data — especially in computer vision tasks like recognizing handwritten words and objects in digital images.

But when applied to data sets without a built-in planar geometry — say, models of irregular shapes used in 3D computer animation, or the point clouds generated by self-driving cars to map their surroundings — this powerful machine learning architecture doesn’t work well. Around 2016, a new discipline called geometric deep learning emerged with the goal of lifting CNNs out of flatland.

Now, researchers have delivered, with a new theoretical framework for building neural networks that can learn patterns on any kind of geometric surface. These “gauge-equivariant convolutional neural networks,” or gauge CNNs, developed at the University of Amsterdam and Qualcomm AI Research by Taco Cohen, Maurice Weiler, Berkay Kicanaoglu and Max Welling, can detect patterns not only in 2D arrays of pixels, but also on spheres and asymmetrically curved objects. “This framework is a fairly definitive answer to this problem of deep learning on curved surfaces,” Welling said.

Already, gauge CNNs have greatly outperformed their predecessors in learning patterns in simulated global climate data, which is naturally mapped onto a sphere. The algorithms may also prove useful for improving the vision of drones and autonomous vehicles that see objects in 3D, and for detecting patterns in data gathered from the irregularly curved surfaces of hearts, brains or other organs.

That's pretty cool stuff! If gauge theory sounds vaguely familiar, it may be because mathematician Karen Uhlenbeck made the news last year when she was awarded the Abel prize in mathematics, in part for her work in the field. If you're not already familiar with the concept of gauge invariance, or equivariance as physicists prefer to call it, the following video provides a blissfully short introduction:

If you want to know more about gauge equivariant convolutional networks and how they apply to deep machine learning, Michael Kissner's easy guide is a good place to begin exploring the topic.

Getting back to the main story, the math behind gauge CNNs are showing real promise in the applications to which they have been placed:

A gauge CNN would theoretically work on any curved surface of any dimensionality, but Cohen and his co-authors have tested it on global climate data, which necessarily has an underlying 3D spherical structure. They used their gauge-equivariant framework to construct a CNN trained to detect extreme weather patterns, such as tropical cyclones, from climate simulation data. In 2017, government and academic researchers used a standard convolutional network to detect cyclones in the data with 74% accuracy; last year, the gauge CNN detected the cyclones with 97.9% accuracy. (It also outperformed a less general geometric deep learning approach designed in 2018 specifically for spheres — that system was 94% accurate.)

Beyond weather monitoring, gauge CNNs may find use in advancing how AI-vision systems used in self-driving vehicles see the world, with their capabilities of processing what these systems are seeing in three-dimensions improving their safety performance.

The future is seemingly determined to have self-driving cars and other AI-powered devices in it. Having those things work well enough to be unremarkable is the real challenge.

Image credit: unsplash-logoDavid Anderson

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January 23, 2020

Existing home sales gained strength in the fourth quarter of 2019, with the estimated market capitalization of the existing home market in 49 states for which we have data appearing to have finally surpassed its previous March 2018 peak in November 2019.

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

State level data for existing homes sales lags the national data, which reports indicate leapt to their highest level in nearly two years in December 2019. Looking at recent trends for the five largest states for existing home sales, we find uptrends in Texas, Florida, New York and New Jersey, which we find has been prompted by increases in both the number of sales and in sale prices.

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

Given what has been reported for the national level data, we'll be excited to see what the data for these markets will look like through December when that data becomes available next month.

We've taken the available state level data and have built up regional level data from it. The next two charts show the aggregate value of existing home sales for the U.S. Census Bureau's Midwest and Northeast regions from January 2016 through November 2019:

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

The next two charts show the same data for the for the U.S. Census Bureau's South and West regions from January 2016 through November 2019.

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

The aggregated state level data confirms growing strength in the Northeast and South regions for the U.S. existing home market. In the Northeast, while New Jersey had the biggest gains, all states but Maine showed month-over-month increases. In the South, South Carolina and Louisiana lagged behind the general growth that took place in all the other states in the region in November 2019, where Maryland and the District of Columbia turned in very strong performances.

Our analysis is based on the existing homes sales and price data published by Zillow's research team, where they also provide data that drills down into the metropolitan level. Check it all out if you get the chance!


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January 22, 2020

Two years ago, we announced the end of a relative period of order in the U.S. stock market. Two years later, there are signs that the chaotic period that followed may now be coming to an end. The following chart showing the daily value of the S&P 500 versus the index' trailing year dividends per share points to that possibility:

S&P 500 Index Value vs Trailing Year Dividends per Share, 30 September 2015 Through 17 January 2020

We're a bit reluctant to start drafting the statistical equilibrium thresholds that apply during relative periods of order in the U.S. stock market, mainly because the new order now taking form has arisen only because the U.S. Federal Reserve began buying large quantities of short-term debt securities issued by the U.S. Treasury in its return to a quantitative easing-like policy in October 2019, which is something it really doesn't want to do, but has to because it badly misjudged the markets' needs for liquidity.

How long that might last remains to be seen. For our part, we'd like to see a more typical level of 'orderly' volatility in stock prices before we overlay any statistical thresholds based on the developing period of relative order in the market.

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January 21, 2020

The S&P 500 (Index: SPX) continued its record-setting rally, breaking through the 3,300 level and closing the week at a new record high closing value of 3,329.62. That new record comes as investors appear to be shifting their forward-looking focus, as anticipated, from 2020-Q1 toward 2020-Q4 as indicated on the following chart.

Alternative Futures - S&P 500 - 2020Q1 - Standard Model - Snapshot on 17 Jan 2020

Through the close of trading on Friday, 17 January 2020, the dividend futures-based model that underlies the alternative futures chart above indicates that investors are now dividing their attention between 2020-Q1 and 2020-Q4, with stock prices rising, but not as fast as the model suggests they would have if investors had remained fully focused on 2020-Q1.

The direction that the S&P 500 will take going forward will depend upon how closely investors might fix their attention on either 2020-Q1 or 2020-Q4 as they make their current day investment decisions, where our thinking is that 2020-Q4 will demand more of their focus given the elevated probability of that more distant future quarter coinciding with the timing of the Federal Reserve's next action for setting short term interest rates in the U.S. The CME Group's FedWatch tool continues to show investors are giving better-than-even odds of a quarter point rate cut taking place during 2020-Q4.

CME Group FedWatch Tool Probabilities of Federal Funds Rate Changing at Future FOMC Meeting Dates, Snapshot on 17 January 2020

On a side note, we often describe shifts in the forward-looking focus from one point of time in the future to another as the quantum part of the quantum random walk that stock prices periodically follow. Unlike the nearly instantaneous quantum leaps that take place with subatomic particles however, stock prices tend to take somewhat longer to move from one alternative trajectory to another, which we have come to associate with stock prices undergoing a Lévy flight phenomenon. The often outsized changes that take place at these times are what give stock prices their fat-tailed distributions, where large changes in stock prices occur more often than would be predicted by a normal distribution.

This latest shift qualifies as the S&P 500's latest Lévy flight event, although in this case, even though there is a comparatively large gap between the alternative trajectories associated with investor expectations for 2020-Q1 and 2020-Q4, the alternative trajectories the describe the potential paths the S&P 500 might follow are such that the overall change in stock prices will be considerably smaller than it would otherwise have been if the timing of the shift in investor focus had occurred either a week earlier or a week later. In a sense, the S&P 500 is following a path that takes the least amount of energy for it to follow as it transitions from one level to another.

Let's get to the news of the week that was. The following headlines capture the more significant market-moving events that occurred during the second full week of January 2020.

Monday, 13 January 2020
Tuesday, 14 January 2020
Wednesday, 15 January 2020
Thursday, 16 January 2020
Friday, 17 January 2020

But wait, there's more! Barry Ritholtz provides a bigger picture of what's going on in the world by listing the positives and negatives he found in the past week's economics and market-related news!

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January 17, 2020

How long could a Roman emperor expect to survive after taking power?

Sculpture of Roman Emperor Tiberius Claudius Caesar Augustus Germanicus in Vatican City - Source: Unsplash - https://unsplash.com/photos/veHGlVkU4qQ

That's a challenging question to answer because the majority of Roman emperors met violent ends. However, new research suggests such results weren't as unpredictable or as random as you might first think.

In modern engineering, the concept of reliability describes the probability that an item will still be operational at some point of time in the future.

Usually, reliability is applied to things like electrical circuits and mechanical devices, but Joseph Saleh has applied the concept to politics, and more specifically, to mathematically describe the survival function of Roman emperors.

"It's interesting that a seemingly random process as unconventional and perilous as the violent death of a Roman emperor--over a four-century period and across a vastly changed world--appears to have a systematic structure remarkably well captured by a statistical model widely used in engineering. Although they may appear as random events when taken singularly, these results indicate that there may have been underlying processes governing the length of each rule until death."

The following chart from Saleh's paper shows the mixture Weibull survivor function he was able to map to the available empirical data for how long ancient Rome's emperors lived after they assumed the purple.

Mixture Weibull survivor (reliability) function of Roman emperors, and the nonparametric results, Figure 4, Saleh 2019

Looking at the nonparametric estimation of the average remaining lifespans of Roman emperors, shown as the heavy black line in the chart above, Saleh offers several observations:

  1. Emperors faced a significantly high risk of violent death in the first year of their rule. This risk remained high but progressively dropped over the next 7 years. This is reminiscent of infant mortality in reliability engineering, a phase during which weak components fail early on after they have been put into service, often because of design or manufacturing defects for example. Roman emperors therefore experienced a form of infant mortality;
  2. The reliability or survivor function stabilizes by the 8th year of rule. The emperors could lower their guard a bit if they made it to 8 years...
  3. ... but not for long: the risk of violent death picks up again after 12 years of rule. This suggests that new mechanisms or processes drove another round of murder. This is reminiscent of wear-out period in reliability engineering, a phase during which the failure rate of components, especially mechanical items, increases because of fatigue, corrosion, or wear-out. Roman emperors therefore also experienced wear-out mortality.

We've built the following tool to estimate the likely survival potential of a generic Roman emperor from Saleh's math. 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.

Time After Assuming Power
Input Data Values
Elapsed Time after becoming a Roman Emperor [years]

Probability of Survival
Calculated Results Values
Survivor Function (Probability of Lasting "X" Years)

In the tool, we've arbitrarily capped the maximum number of years a Roman emperor might survive to 45 years, which corresponds to the Emperor Augustus' reign, the longest on record.

Saleh's paper also provides a chart showing the hazard function, or failure rate, for Roman emperors, which reveals a unique pattern.

Failure rate of Roman emperors (parametric fit of the time-to-violent-death), Figure 5, Saleh 2019

This pattern is the familiar "bathtub curve" that characterizes how many real world components behave in reliability analysis. Saleh provides an interesting interpretation of how this pattern applies to the lives of Roman emperors:

  1. The decreasing failure rate early on, the signature of infant mortality, reflects as noted previously a prevalence of weak emperors who were incapable at the onset of their rule to handle the demands of their environment and circumstances. The fact that the failure rate was decreasing though suggests a competition between antagonistic processes, on the one hand those that sought to violently eliminate emperors (elimination), and on the other hand those that reflected the emperors learning curve to better protect themselves and perhaps eliminate their opponents (preservation). Examples abound in Roman history of this competition. Up to the first 12 years of one's rule, the preservation processes steadily improved their performance, and the situation can be casually summarized as "whatever didn't kill them [the Roman emperors] made them stronger" or less likely to meet a violent death;
  2. The increasing failure rate after 12 years of rule, the signature of wear-out failures, reflects as noted previously an uptake in failures through degradation with time, fatigue, or increased harshness in their circumstances. A growing mismatch between capabilities and demands under changing (geo-)political circumstances. This can be due to a number of reasons discussed previously. The fact that the failure rate was increasing after this 12-year mark suggests again a competition between the same antagonistic processes noted in (i), and this time the preservation ones were on the losing end of this competition. This result can be causally summarized as "whatever didn't kill them made them weaker" after a 12-year rule.

The existence of the pattern means that the probability of how long a Roman emperor might last is the result of both random chance and deterministic factors, rather than just chance alone, as perhaps best imagined by the "roll of the dice" Julius Caesar figuratively cast before crossing the Rubicon on his way to taking power as Rome's age of emperors began.

Part of what makes Saleh's analysis so intriguing is that the same concept can be applied to other nations, or forms of government, that have developed in the centuries since the fall of the Roman empire. It will be interesting to discover what patterns they might share with the Roman emperors.

Image Credit: unsplash-logoiam_os

References

Saleh, J.H. Statistical reliability analysis for a most dangerous occupation: Roman emperor. Palgrave Communications 5, 155 (2019). doi: 10.1057/s41599-019-0366-y.

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January 16, 2020

China is, by far and away, the "world's largest emitting country" where the emissions of carbon dioxide are concerned. In 2019, it appears that China managed to raise both the ceiling and the floor for the amount of human-produced CO₂ that was discharged into the Earth's atmosphere through its efforts to stimulate its domestic economy.

Year-Over-Year Change in Parts per Million of Atmospheric Carbon Dioxide, January 1960 - December 2019

In 2018, China's output of CO₂ surpassed the contributions of the world's next three largest carbon dioxide emitting countries, the U.S., India, and Russia, so it went into 2019 building from an elevated platform, even as its economy was slowing.

In 2019, China was suspected of "U-turning back towards dirty 'coal-fired power'", as the country is reported to be "still building an insane number of new coal plants", adding over five times as much coal-fired power generating capacity within its borders as the rest of the world did outside of them.

China is also responsible for funding the development of 300 CO₂-emitting coal-powered generating plants around the world as part of its "Belt-and-Road" checkbook diplomacy initiative.

References

National Oceanographic and Atmospheric Administration. Earth System Research Laboratory. Mauna Loa Observatory CO2 Data. [File Transfer Protocol Text File]. Updated 7 January 2019. Accessed 12 January 2019.

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January 15, 2020

The rate of growth of new home sale prices in the U.S. has slowed significantly from historic rates. The following chart shows one of our favorite ways to measure that growth rate, tracking the amount of time it has taken for the median new home sale price in the United States to double since January 1963, the earliest that monthly data for the statistic is available.

Median and Average Monthly U.S. New Home Sale Prices, January 1963 through November 2019

Starting from January 1963, the median sale price of a new home sold in the U.S. has sustainably doubled four times, with the amount of time it has needed taking 134 months, 104 months, 167 months, and 217 months to do so, with the most recent doubling period ending in October 2014.

In the next chart, we've zoomed in on the period since January 2000 for average and median new home sale prices, where we find that new home sale prices hit a peak in December 2017 before slowly declining in the months since.

Median and Average Monthly U.S. New Home Sale Prices, January 2000 through November 2019

What makes this period different from earlier periods where new home sale prices have declined is the absence of recession. In fact, median household income has been generally rising after adjusting for inflation since the end of 2016, where the combination with falling median new home sale prices means that new homes have been becoming more affordable in the U.S. The following chart shows the ratio of the trailing twelve month averages of median new home sale prices and median household income since 1967.

Ratio of Trailing Twelve Month Averages for Median New Home Sale Prices and Median Household Income, Annual: 1967 to 2017 | Monthly: December 2000 to November 2019

For the affordability of new homes sold in the U.S., the last two years have been unique among all the years in the last five and half decades for which we have data.

References

U.S. Census Bureau. Median and Average Sales Prices of New Homes Sold in the United States. [PDF Document]. Accessed 12 January 2020.

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January 14, 2020

As promised, we're taking our first look forward for the S&P 500 (Index: SPX) through the end of 2020-Q1. The following chart shows what the dividend futures-based model we use to project the future for the S&P 500 says that future may look like during the next few months, depending upon which future quarter investors fix their focus upon:

Alternative Futures - S&P 500 - 2020Q1 - Standard Model - Snapshot on 10 Jan 2020

Through the end of 10 January 2019, the level of the S&P 500 is consistent with investors focusing on the current quarter of 2020-Q1 in setting current day stock prices, but we suspect that may be about to change, where we think the most likely outcome will be that investors will shift their focus toward the distant future quarter of 2020-Q4.

The reason why we're thinking that is because Fed officials invested a lot of effort during the past week to try to convince investors that they would not be changing interest rates in 2020.

But according to the CME Group's FedWatch tool, they didn't succeed. The following snapshot from after the close of trading on Friday, 10 January 2020 shows that investors are betting the Fed will cut the Federal Funds Rate by a quarter point in 2020-Q4, which provides a strong incentive for investors to focus on this point of time in the future.

CME Group FedWatch Tool Probabilities of Federal Funds Rate Changing at Future FOMC Meeting Dates, Snapshot on 10 January 2020

Given what we've observed in how stock prices work, we would see that shift take place with stock prices mostly moving sideways. If we're wrong, and investors continue to focus on 2020-Q1 in setting current day stock prices, we'll see the S&P 500 rise sharply instead.

There are certainly worse ways to be wrong! In any case, if you want to get a better idea of how we do what we do, others have done a good job of explaining the logic behind what it takes to follow the quantum random walk of stock prices!

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January 13, 2020

The S&P 500 (Index: SPX) reached up to set another new record high close during the first full week of January 2020, hitting its new peak of 3,274.70 on Thursday, 9 January 2020, before dropping back slightly to close the trading week at 3,265.35.

Since our alternative futures spaghetti forecast chart for 2019-Q4 captures the past week, let's update it one last time before we close the books on 2019 for good!

Alternative Futures - S&P 500 - 2019Q4 - Standard Model with Redzone Forecast Focused-on-2020Q3-Between 26-Nov-2019 and 07-Jan-2020 - Snapshot on 10 Jan 2020

Looking back over all of 2019, we can confirm that the dividend futures-based model we've developed to anticipate the S&P 500's future trajectory was useful, even during the periods where the model was affected by the echoes of past volatility the U.S. stock market experienced in 2018, which arises as an issue because the model uses historic stock prices as the base reference points from which it projects into the future, which is shown as the tan-shaded regions of the following chart displaying the entire year.

Alternative Futures - S&P 500 - 2019 - Final Snapshot on 10 Jan 2020

The red-shaded zones in this chart represent the refined forecasts we added to the standard model to account for the echo effect, where we used a very simple method of "connect-the-dots" to essentially connect points of the forecasts on opposite sides of an echo-affected period after making assumptions for what future quarter would hold investors' attention during these periods and how much volatility the S&P 500 could potentially experience.

We also tested using dynamic adjustments to allow the redzone forecast ranges to adapt with changes in future expectations, where we fixed one end of these ranges to a past level of the S&P 500 and allowed the opposite end to float along with the model's standard forecast trajectories. Before 2019, we've simply fixed both ends of the redzone forecasts we've added to our charts, but the very long duration of the first echo-affected period we faced in 2019 made that approach impractical for developing relatively accurate projections.

That was challenging during 2019. As you can see in the chart, the first echo-affected period spanned almost all of the first quarter and extended into the second quarter. The fourth quarter was almost as difficult.

And yet, we managed to pull it off. Fortunately, we will get a bit of a breather before we might need to add another redzone forecast to the alternative futures charts we'll present for 2020, where it looks like we'll have to deal with comparatively short volatility echo-affected periods in 2020-Q2 and in 2020-Q3.

That's enough talk about 2019. Let's review the major market-moving headlines that stood out to us during the first full trading week of 2020, where we find it was relatively quiet, except for Thursday when the Fed's minions were especially active in trying to set the expectations they want investors to have this year.

Monday, 6 January 2020
Tuesday, 7 January 2020
Wednesday, 8 January 2020
Thursday, 9 January 2020
Friday, 10 January 2020

Barry Ritholtz succinctly summarized the positives and negatives he found in the economics and market-related news during the past week. Do check it out to get a bigger picture of all that was going on in the first full week of January 2020!

This post is part of our SP 500 chaos series, where once a week, we not only update what the future for the S&P 500 looks like, we also reference news articles related to the topics that were influencing investor expectations during the previous week, which you might find useful if you should ever need to reconstruct what investors were considering at various points in the past.

Speaking of which, we'll return to looking forward in time later this week when we introduce the alternative futures spaghetti forecast chart for 2020-Q1!

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January 10, 2020

After achieving a new record high in October 2019, median household income in the United States dipped slightly in November 2019, falling to $66,043 from the previous month's initial estimate of $66,465, according to Sentier Research.

The following chart shows the nominal (red) and inflation-adjusted (blue) trends for median household income in the United States from January 2000 through November 2019, with November 2019's estimate marking the top of both scales. The inflation-adjusted figures are presented in terms of constant November 2019 U.S. dollars.

Median Household Income in the 21st Century: Nominal and Real Estimates, January 2000 to November 2019

Year over year, median household income rose by 3.9%, or 1.8% after adjusting for inflation, from the figures recorded in November 2018.

Analyst's Notes

Our alternate methodology for estimating median household income from data reported by the U.S. Bureau of Economic Analysis would put the figure at $66,090 for November 2019, which is less than 0.1% different from Sentier Research's initial estimate for the month.

Sentier Research's estimate for December 2019 should be available toward the end of the month, where we'll present a year-end wrap up for median household income in 2019 sometime in early February 2020.

References

Sentier Research. Household Income Trends: January 2000 through November 2019. [Excel Spreadsheet with Nominal Median Household Incomes for January 2000 through January 2013 courtesy of Doug Short]. [PDF Document]. Accessed 27 December 2019. [Note: We've converted all data to be in terms of current (nominal) U.S. dollars.]

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. [Online Database (via Federal Reserve Economic Data)]. Last Updated: 11 December 2019. Accessed: 11 December 2019.

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January 9, 2020

According to international trade data reported by the U.S. Census Bureau, the United States' overall trade deficit with the rest of the world shrank to its lowest level in three years in November 2019:

The U.S. trade deficit has fallen by more than 8 percent, to $43.1 billion, reaching its lowest level in President Donald Trump's administration, government figures showed Tuesday.

The report showed the deficit decreased in November, the most recent month for which the government has figures, by $3.9 billion -- a decline of 8.2 percent....

The analysis said the lion's share of the shrinking trade deficit resulted from a reduction in the gap with China -- which decreased $2.2 billion to $25.6 billion in November. Imports from China decreased by $800 million.

That's all this particular article had to say on the topic, but it misses a much bigger story. In addition to the decrease in the goods the U.S. imports from China, U.S. exports to China spiked up in November 2019, fueled by China's suppressed appetite for U.S.-grown soybeans and its undiminished appetite for pork, with China turning to global markets to address the impact of African Swine Fever on its domestic hog population, which has created a massive shortage of the popular protein within the country.

You can see both factors at work on the right-hand side of the following chart, which tracks the year-over-year exchange rate-adjusted growth rate of goods traded between the U.S. and China from January 1986 through November 2019:

Year Over Year Growth Rate of Exchange Rate Adjusted U.S.-China Trade in Goods and Services, January 1986 - November 2019

Most of that year-over-year effect for the spike in U.S. exports to China in November 2019 relies upon the very low level of U.S. exports of soybeans back in November 2018, when Chinese buyers were persuaded by China's retaliatory tariffs to effectively boycott purchases of the U.S. crop - a boycott that cracked in November 2019 when China began to waive its punitive tariffs in anticipation of a trade deal with the U.S. government. At the same time, the U.S. government had not yet fully implemented its tariffs on Chinese exports to the U.S., so the volume of those goods remained high through the end of 2018. A year later, with U.S. tariffs now fully applied, Chinese exports to the U.S. have fallen sharply because they are now costing U.S. businesses and consumers much more than a year ago.

In any case, that's a more complete version of what happened during November 2019 that led to the lowest monthly trade deficit of the last three years being recorded in the U.S.

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January 8, 2020
Washington Evening Star - 3 December 1936 - Page C-4

Dividend paying firms in the U.S. stock market closed out 2019 on a muted note, with the fourth quarter seeing both fewer dividend rises and fewer dividend cuts. Not uncoincidentally, the same can be said of 2019 as a whole.

Here's our tally of dividend metadata for 2019-Q4, where we've compared the fourth quarter's figures to both the preceding quarter, 2019-Q3, and to the year-ago-quarter of 2018-Q4:

  • A total of 5,106 U.S. firms declared dividends in December 2019, an increase of 1,733 over the 3,373 recorded in November 2019. That figure is also 828 lower than what was recorded a year ago in December 2018.
  • 135 U.S. firms announced they would pay a special (or extra) dividend to their shareholders in December 2019, an increase of 58 over the number recorded in November 2019 and 44 lower than what was recorded a year ago in December 2018.
  • 126 U.S. firms announced they would boost cash dividend payments to shareholders in December 2019, a decrease of 24 from the 150 recorded in November 2019, and a decrease of 12 from the 138 dividend rises declared back in December 2018.
  • A total of 26 publicly traded companies cut their dividends in December 2019, an increase of 2 over the number recorded in November 2019 and also a decrease of 14 from the 40 recorded in December 2018.
  • 1 U.S. firm omitted paying their dividends in December 2019, a decrease of 3 from the number recorded in November 2019. That figure is also the same as the total recorded in December 2018.

And since we're covering the end of the year, here is how the number of dividend increases and decreases compares between this year and last.

  • There were 1,808 announced dividend rises during 2019, down from the 2,118 announced in 2018.
  • U.S. firms announced 309 dividend reductions in 2019, down from the 366 declared in 2018.
  • A total of 29 dividend declarations in 2019 involved firms omitting dividend payments to their shareholders, down from 37 such announcements in the previous year.

The following chart shows the monthly total of dividend rises and reductions from January 2004 through December 2019.

Number of Public U.S. Firms Increasing or Decreasing Their Dividends Each Month, January 2004 through December 2019

With the number of dividend increases down quarter-over-quarter and year-over-year, suggesting an overall negative environment for investors, in truth the fourth quarter of 2019 ended on a mixed note, as the both the number of dividend cuts fell and the relative magnitude of net dividend increases rose compared to the year-ago quarter of 2018-Q4:

S&P Dow Jones Indices today announced that indicated dividend net increases (increases less decreases) for U.S. domestic common stocks were $10.6 billion during Q4 2019, up 44.2% from Q4 2018's $7.4 billion increase.

For Q4 2019, aggregate increases amounted to $11.97 billion, up 1.0%, from Q4 2018's $11.85 billion. Aggregate dividend cuts, however, decreased 69.9% to $1.35 billion from $4.48 billion for Q4 2018, a period which included the $3.82 billion General Electric dividend cut.

For the year ending 2019, net dividends rose $45.4 billion, compared to a gain of $58.4 billion for 2018, as increases were $56.6 billion versus $66.5 billion, and decreases were $11.1 billion compared to $8.1 billion for the prior period.

Focusing on the companies that announced dividend cuts during 2019-Q4, we find our near-real time sampling during the quarter mainly consists of firms in the U.S. oil and gas production sector (24), followed by financial firms and Real Estate Investment Trusts (9), manufacturing (2), mining (2), media (2), and finally (1) firm each from the chemical, services, consumer goods, and restaurant industries. Here's the list we compiled from Seeking Alpha and the Wall Street Journal, starting with the firms that set their dividends independently of their earnings and cash flow:

And here's the rest of the list, which is made up of firms that pay variable dividends, often as a percentage of their earnings. This portion of the list contains a number of firms that pay monthly dividends, where firms may show up more than once in the tally:

This portion of the list also contains a large percentage of firms from the oil and gas industry whose dividend payouts are very sensitive to changes in oil prices, where falling prices (and revenues) can indicate distressing conditions within the sector, where these firms are much like the proverbial canaries in a coal mine.

The following chart shows how the cumulative number of dividend cuts for all the firms we tracked in our near-real time sampling played out during 2019.

Cumulative Number of Dividend Cuts by Day of Quarter, 2019 (Final)

Going just by dividend cuts, 2019 was a relatively healthy year for the dividend-paying firms of the U.S. stock market. 2019-Q1 edged out 2019-Q2 as the least well-off quarter of the year, while 2019-Q3 was the best quarter of the year by a wide margin.

Looking forward, the manufacturing sector may be the one to pay the closest attention to in 2020, where the automotive industry within the sector will likely see 2019's global economic slowdown catch up to them, with U.S. Steel's dividend cut in 2019-Q4 perhaps being the first supporting industry domino to fall and where Boeing's continuing production problems with the 737-MAX commercial transport aircraft likely to take a bite out of the aerospace industry.

References

Standard and Poor. S&P Market Attributes Web File. [Excel Spreadsheet]. 31 December 2019. Accessed 2 January 2020.

If you're wondering about the image featured at the top of this post, we thought it would be fun to feature the excerpt from a vintage ad in this post. That may be because we've recently rewatched Trading Places, where an investment in Frozen Concentrated Orange Juice plays a meaningful role in the plot of the 1983 movie. In any case, the image credit belongs to Washington D.C.'s defunct Evening Star newspaper from 3 December 1936, where the ad appeared on page C-4. The source is the Library of Congress' Chronicling America: Historic American Newspapers archive.

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