Political Calculations
Unexpectedly Intriguing!
October 22, 2019

Net wealth is looking like it will be a hot topic in the upcoming 2020 election season in the United States, with several candidates proposing to tax high net worth households to only partially pay for the explosion in federal government spending they are also proposing.

How at risk are you might be to these newly proposed taxes on wealth depends on what your household's net worth is and how your rank in net worth among all Americans. To help answer the first part of that question, we'll direct you to Bankrate's Net Worth Calculator, which will add up the value of your assets and subtract out your liabilities to estimate your household's net worth, assuming you haven't already done that math.

After you have that figure, plug it into the following tool, where we'll estimate your household's percentile ranking among all U.S. households, based on data for 2016 that was recently published by the U.S. Census Bureau. Hopefully, you'll be entering a positive number, but if not, our tool can handle if your household is underwater and you have to enter a negative value. If you're reading this article on a site that republishes our RSS news feed, please click through to our site to access a working version of the tool.

Household Net Wealth Data
Input Data Values
Your Household Net Worth

Estimated Household Net Worth Percentile Ranking
Calculated Results Values
Your Household's Net Worth Percentile

Now you have a fairly good sense of what percentage of all 129,600,000 U.S. households have a net worth that is either less than or equal to yours! The chart below shows our model for the distribution of net worth in the United States and how it compares to the data recorded by the U.S. Census.

Estimated U.S. Distribution of Net Worth, 2016

Overall, the tool is generally accurate to within about 1.5 percentiles of the reported data, which is pretty good given how we modeled the data, where we spliced two very different regressions to generate our results.

In addition to the Census Bureau's wealth distribution data, the Federal Reserve's Survey of Consumer Finances provides an alternate source of data for household net worth in the U.S. The SCF is conducted every three years, where the data for 2019 will likely become available in late October 2020.

If you want to find out how your household ranks to the nearest half percentile according to the Federal Reserve's survey data, be sure to check out Don't Quit Your Day Job's Net Worth Percentile Calculator. Better still, if you want to find out how your household's net worth ranks among people in your age group, DQYDJ's Net Worth by Age Calculator has you covered!

Finally, we discovered that Microsoft (NYSE: MSFT) chairman Bill Gates had the highest reported net worth in 2016 thanks to the value of the shares he owns in the company he founded, which at $75 billion, would place him just off the top right end of the chart. The second highest net worth for an American belongs to Warren Buffett, whose accumulated net wealth over the last 49 years as the head of Berkshire Hathaway (NYSE: BRK.A, BRK.B) would appear on the chart at $60.8 billion (equivalent to a natural logarithm of 24.8).

References

U.S. Census Bureau. Net Worth and Asset Ownership of Households: 2016. [Excel Spreadsheet]. 25 September 2019. Accessed 19 October 2019.

Forbes. The Full List Of Every American Billionaire 2016. [Online Article]. 1 March 2016.

Clementi, F.; Gallegati, M.; and Kaniadakis, G. A generalized statistical model for the distribution of wealth. Journal of Statistical Mechanics: Theory and Experiment, 2012, P12006. https://doi.org/10.1088/1742-5468/2012/12/P12006. [Ungated PDF Document]. 6 December 2012.

Hozo, Stela Pudar; Djulbegovic, Benjamin; and Hozo, Iztok. Estimating the mean and variance from the median, range, and the size of a sample. BMC Medical Research Methodology, volume 5, Article number: 13 (2005). https://doi.org/10.1186/1471-2288-5-13. 20 April 2005.

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October 21, 2019

For a market that got almost every bit of positive news it could have been hoping for in the week before last, the S&P 500 (Index: SPX) didn't carry any momentum into the third week of October 2019.

The proof can be found in the latest update to our spaghetti forecast chart for the index, where we find the S&P 500 skimmed along and just below the top edge of the redzone forecast range we added to the chart just last week.

Alternative Futures - S&P 500 - 2019Q4 - Standard Model with Redzone Forecast with 50/50 Split Between 2019Q4 and 2020Q1 from 8 Oct 2019 to 8 Nov 2019 - Snapshot on 18 Oct 2019

That range assumes that the S&P 500 will track along with investors roughly equally splitting their forward looking focus between the current quarter of 2019-Q4 and the upcoming future quarter of 2020-Q1 in setting today's stock prices. Should the trajectory of the S&P 500 break outside of the redzone range, that would be an indication that investors have focused much more strongly on one of these two quarters.

Much of that dynamic has hinged on investor expectations for what the Federal Reserve will do in upcoming months with interest rates and its new T-bill buying policy. Right now, investors are betting the Fed will announce it will cut the Federal Funds Rate by a quarter point at its meeting at the end of this month, which could be the last cut for a while if the rate change probabilities indicated by the CME Group's FedWatch Tool is any indication:

CME Group FedWatch Tool Probabilities of Federal Funds Rate Changing at Future FOMC Meeting Dates, Snapshot on 18 October 2019

And then, there's that little matter of corporate earnings, which will be rolling out over the next several weeks, as well as other economic news that might alter the focus of investors. Here are the more significant headlines we plucked out of the news stream during the week that was.

Monday, 14 October 2019
Tuesday, 15 October 2019
Wednesday, 16 October 2019
Thursday, 17 October 2019
Friday, 18 October 2019

Barry Ritholtz found five positives and five negatives and no political noise that might affect the market in the past week's economics and market-related news.

If only there were no political noise to have to filter out. Wouldn't that be something!

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October 18, 2019

Are prime numbers randomly distributed, or is there an underlying structure that can predict where they will occur?

If you can definitively answer that question, say by either proving or disproving the Riemann Hypothesis, which says that yes, there is such an underlying structure, there's a million dollar prize waiting for you.

But if you just want to explore some the patterns that can be found in how prime numbers are distributed, you might find the following 22-and-a-half minute video from 3Blue1Brown right up your alley!

As a bonus, in addition to an introduction to Dirichlet's prime number theorem, you'll also find out why the fraction 355/113 is a particularly good rational approximation for the value of pi!

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October 17, 2019

In 2017, the average rent in the U.S. was "too damn high". In 2018, according to the Consumer Expenditure Survey, it was even higher.

Average Annual Home Ownership and Rent Expenditures per U.S. Household Consumer Unit, 1984-2018

Remarkably, the rent rose even as the cost to own, as measured by the average annual combined cost of mortgage principal and interest payments, fell. From 2017 to 2018, the average household consumer unit expenditure for rent increased from $4,167 to $4,269, while the cost to own declined from $5,104 to $5,002.

Before going any further, we should recognize the data reported by the Consumer Expenditure Survey is spreading all these payments out over all household consumer units in the United States. In 2018, there were an estimated 131,439,000 household consumer units, where 48,632,430 (37%) paying rent, an equal number of homeowners making mortgage payments, and 34,174,140 homeowners (26%) with no mortgage payments.

Doing the math for renters, multiplying the average $4,167 in annual rent payments by 131,439,000 household consumer units, we estimate the total rent paid in 2018 adds up to $547.7 billion. Dividing that result by the estimated 48,632,430 rent payers, we find the average annual rent is $11,262. Dividing by 12 gives an average monthly rent of $938.51, which is indeed slightly higher than the $935 per month figure we previously calculated for 2017.

Doing the almost identical math for homeowners making mortgage payments, we estimate aggregate mortgage payments to be $657.5 billion, with the average annual total of mortgage payments working out to be $13,519, which corresponds to an average monthly principal and interest mortgage payment of $1,126.58.

The average monthly rent paid in the United States in 2018 is 83% of the cost of a simple mortgage payment that omits any homeowners' insurance payments or property taxes that might be included with it.

In case you're wondering what ever happened to Jimmy McMillan, the founder of the Rent Is Too Damn High political party in New York who ran for state governor in 2006 and 2010, he has retired from politics, but since the rent keeps rising, we can only wonder if he is considering making a comeback!

Data Sources

U.S. Bureau of Labor Statistics and U.S. Census Bureau.  Consumer Expenditure Survey.  Multiyear Tables.  [PDF Documents: 1984-1991, 1992-1999, 2000-2005, 2006-2012, 2013-2018]. Reference Directory: https://www.bls.gov/cex/csxmulti.htm. 10 September 2019. 

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October 16, 2019

Americans collectively spent over $447.5 billion of their own money to cover the cost of unsubsidized health insurance in 2018, an increase of 0.8% from the more than $443.8 billion spent in 2017. At an average annual cost of $3,405 per household consumer unit however, 2018's spending represents the first year over year decline in this category of consumer expenditures since 1996, falling from 2017's average cost per household consumer unit of $3,414.

Average Annual Expenditures for Health Insurance per Household Consumer Unit, 1984-2018

This data comes from Consumer Expenditure Survey (CEX), which shows the continuing effect of the skyrocketing cost of health insurance caused by the passage and implementation of the Affordable Care Act during the Obama administration. In this case, the rapid escalation of health insurance costs to higher income earning households that could previously afford coverage may be prompting them to choose to become uninsured, which would contribute to the indicated reduction in average cost.

From 2017 to 2018, the number of individual uninsured Americans rose by 1.9 million, from 25.6 million to 27.5 million, which the U.S. Census Bureau reveals a very large portion of this increase was uniquely concentrated in households earning well above the median household income of $61,392 for 2018:

Between 2017 and 2018, overall health insurance coverage decreased 1.0 percentage point for people in families with income from 300 to 399 percent of poverty and 0.8 percentage points for people in families with income at or above 400 percent of poverty. During this time, the overall health insurance coverage rate did not statistically change for any other income-to-poverty group.

Here's how that translates into the number of affected individuals:

The number of uninsured Americans in households with income above 400 percent of the poverty line increased by 1.1 million from 2017 to 2018, and the number of uninsured in households with income above 300 percent of the poverty line — about $75,000 for a family of four — increased by 1.6 million.

Households in this category, which includes many headed by small business owners and self-employed income earners who don't have access to other employer-provided health insurance coverage, are largely choosing to go from paying top dollar or near-top dollar for health insurance to no such coverage, even though they would still be subject to additional income taxes from the Affordable Care Act's mandate penalty. For these higher income earning households, since the rising cost of coverage they must pay has been driven up by the requirements of the Affordable Care Act to the point where it far exceeds the value of tax subsidies available to them, dropping the amount they pay for family coverage from nearly $20,000 to $0 per household is having a noticeable effect on the overall average cost paid out of pocket by all American households for health insurance, which is being captured by the CEX data for 2018.

The Trump administration has introduced reforms aimed at arresting the skyrocketing cost of health insurance for these households in late 2018 to address their specific needs, which may not show an effect until the 2019 data becomes available.

Data Sources

U.S. Bureau of Labor Statistics and U.S. Census Bureau.  Consumer Expenditure Survey.  Multiyear Tables.  [PDF Documents: 1984-1991, 1992-1999, 2000-2005, 2006-2012, 2013-2018]. Reference Directory: https://www.bls.gov/cex/csxmulti.htm. 10 September 2019. 

U.S. Census Bureau. Health Insurance Coverage in the United States: 2018. [PDF Document]. 10 September 2019.

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October 15, 2019

How much, and on what, does the average American household spend in a year?

The Consumer Expenditure Survey (CEX) for 2018 provides the latest answer to that question, where we now have 35 years worth of annual data to see how consumer spending has changed over time. Produced as a joint product of the U.S. Bureau of Labor Statistics and the U.S. Census Bureau, the CEX compiles the information collected through tens of thousands of surveys, diaries and interviews with by U.S. households, or "consumer units" as the BLS' data jocks affectionately calls them, which provides a tremendous amount of insight into how Americans allocate their limited resources. Which in 2018, meant spending an average of $61,224 for an estimated 131,439,000 household/consumer units.

We've visualized the major categories of consumer spending by the American households surveyed each year from 1984 through 2018. In our first chart below, we've presented the average amount spent for housing, transportation, food, financial products, health care, clothing, entertainment, charitable contributions, and education.

Major Categories of Average Annual Expenditures per Consumer Unit, 1984-2018

The next chart illustrates the same major expenditure categories as a percent of the average annual expenditures of a U.S. household/consumer unit:

Percent Share of Average Annual Expenditures per Consumer Unit, 1984-2018

Finally, we'll update one of our favorite charts to show how consumer spending has evolved during over the 35 years from 1984 through 2018. Tthe expenditures shown on the bottom of the chart, in shades of purple, show those expenditures whose share among the total has increased over time, while the expenditures shown toward the top of the chart, in shades of green, show the household expenses whose share of total spending has fallen.

Major Categories of Consumer Spending as Share of Average Annual Expenditures per Consumer Unit, 1984-2018

Here's the list of major categories of consumer expenditures whose share has risen from 1984 through 2018:

  • Housing
  • Financial Products (Life Insurance, Pension Savings & Social Security)
  • Health Care (Health Insurance and Medical Expenses)
  • Charitable Contributions
  • Education

And here's the list of major categories of consumer expenditures whose share has declined over the 34 years for which the data is available:

  • Apparel and Other Products
  • Food and Alcoholic Beverages
  • Transportation
  • Entertainment

In upcoming days, we'll dig deeper into the latest data for consumer expenditures to explore changes that stand out over time. Stay tuned!

Data Sources

U.S. Bureau of Labor Statistics and U.S. Census Bureau.  Consumer Expenditure Survey.  Multiyear Tables.  [PDF Documents: 1984-1991, 1992-1999, 2000-2005, 2006-2012, 2013-2018]. Reference Directory: https://www.bls.gov/cex/csxmulti.htm. Accessed 10 September 2019. 

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October 14, 2019

The S&P 500 (Index: SPX) rose sharply at the end of the second week of October 2019, boosted by news of the Fed's plan to buy $60 billion in Treasury bills each month well into 2020 and also the "phase one" announcement of a partial trade deal between the U.S. and China.

The following spaghetti forecast chart indicating the potential trajectories the S&P 500 might take depending upon how far into the future investors might be compelled to focus their forward-looking attention during 2019-Q4 shows that latest action:

Alternative Futures - S&P 500 - 2019Q4 - Standard Model with Redzone Forecast with 50/50 Split Focus on 2019-Q4 and 2020-Q1 from 8 October 2019 through 8 November 2019 - Snapshot on 11 Oct 2019

We've added a redzone forecast to our chart, which is based on our assumption that investors are roughly equally splitting their forward-looking attention between 2019-Q4 and 2020-Q1 in setting today's stock prices, which we also assume will largely continue through 8 November 2019. This particular redzone forecast closely coincides with the trajectory that might apply if investors were to shift their attention to the much more distant time horizon of 2020-Q3, but we as yet see no evidence in the flow of new information shaping investor expectations that may be the case.

The Fed's action appears to have followed an emergency videoconference meeting of the Open Market Committee on 4 October 2019. With the practical assessment that the Fed has decided to re-initiate its Great Recession-era quantitative easing policies in all but name, investors have reconsidered the likelihood of the Fed also continuing to reduce the Federal Funds Rate in both 2019-Q4 and 2020-Q1. As of the close of trading Friday, 11 October 2019, investors seem to be betting on just one quarter point rate cut in 2019-Q4, coming as early as the end of October 2019, with a less-than-50% probability of another rate cut in 2020-Q1.

CME Group FedWatch Tool Probabilities of Federal Funds Rate Changing at Future FOMC Meeting Dates, Snapshot on 11 October 2019

That change in expectations would account for the apparent split in focus between 2019-Q4 and 2020-Q4 in the alternate futures spaghetti forecast chart above, where the odds of a rate cut in 2020-Q1 are in flux. The headlines we believe reflect the flow of new information that contributed to shaping investor expectations during the trading week ending Friday, 11 October 2019 are below:

Monday, 7 October 2019
Tuesday, 8 October 2019
Wednesday, 9 October 2019
Thursday, 10 October 2019
Friday, 11 October 2019

The Big Picture's Barry Ritholtz outlined six positives and six negatives from the past week's economics and market-related news. One of the negatives is political noise, so on the whole, a net positive week!

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October 11, 2019

It's a topic we touch upon from time to time as it relates to things like how stock prices behave or how the economy works, but emergence is much more than that. Quanta Magazine's short 3-and-a-half-minute video from their In Theory series provides a taste of what it is and where else it is seen:

Quanta's John Rennie explains the wonderful way complex order comes into being:

Nature is filled with such examples of complex behaviors that arise spontaneously from relatively simple elements. Researchers have even coined the term “emergence” to describe these puzzling manifestations of self-organization, which can seem, at first blush, inexplicable. Where does the extra injection of complex order suddenly come from?

Answers are starting to come into view. One is that these emergent phenomena can be understood only as collective behaviors — there is no way to make sense of them without looking at dozens, hundreds, thousands or more of the contributing elements en masse. These wholes are indeed greater than the sums of their parts.

Another is that even when the elements continue to follow the same rules of individual behavior, external considerations can change the collective outcome of their actions. For instance, ice doesn’t form at zero degrees Celsius because the water molecules suddenly become stickier to one another. Rather, the average kinetic energy of the molecules drops low enough for the repulsive and attractive forces among them to fall into a new, more springy balance. That liquid-to-solid transition is such a useful comparison for scientists studying emergence that they often characterize emergent phenomena as phase changes.

If you have a spare hour and 13 minutes, you can hear economists Don Boudreaux, Mike Munger, and Russ Roberts discuss how emergent orders arise in human activities via an EconTalk podcast!

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October 10, 2019

Based on recent changes in the trends for the U.S. Treasury yield curve and the level of the Federal Funds Rate, the probability the national U.S. economy will enter into recession sometime in the next twelve months appears to have peaked during the last month and has begun to recede.

That's tough to tell from the Recession Probability Track, which still shows an 11% probability the U.S. economy will enter into recession between 9 October 2019 and 9 October 2020, but that's due to our rounding the probability to the nearest whole percentage point. When we round the recession odds to one decimal point, we find the percentage probability peaked at 11.3% just before our last update three weeks ago, which has since declined to 10.5% as of 9 October 2019.

U.S. Recession Probability Track Starting 2 January 2014, Ending 9 October 2019

That's primarily due to the effect of the Fed's two quarter point reductions of the Federal Funds Rate since 31 July 2019, which is starting to have an effect on the recession odds that may be determined using the methodology laid out by the Federal Reserve Board's Jonathan Wright in a 2006 paper.

Meanwhile, in the last several days, the Federal Reserve has all but thrown in the towel for resisting further interest rate cuts and has launched a new program of quantitative easing to buy large quantities of U.S. Treasury securities and mortgage-backed securities from government-supported enterprises like Fannie Mae and Freddie Mac to stimulate the economy, just like it did during the Great Recession, although Fed Chair Jerome Powell is working overtime to keep from calling it "QE". Perhaps the funniest thing we've read in some time on the topic of the Federal Reserve's monetary policies is Mark Orsley's article "First Rule of QE Club, Don't Call It QE" over at ZeroHedge - looks like Tyler Durden has a new catch phrase!

One important thing to remember at this point is what Wright's methodology is forecasting - the probability that the National Bureau of Economic Research will someday declare that a recession began sometime in the twelve month period from 9 October 2019 to 9 October 2020. Should the NBER ever make such a declaration, it could come as early as sometime in late 2020, or it could come sometime in 2021. To provide a frame of reference, in late March 2007, Wright's method forecast a peak 50% probability the NBER would someday declare the U.S. would enter recession sometime between April 2007 and April 2008. The NBER didn't make a public declaration of recession until December 2008, when they announced what would become known as the Great Recession had begun after the U.S. economy peaked in December 2007.

Normally, when the Fed releases the minutes of its previous Open Market Committee meetings, we've featured an alternate scenario analysis that factors in the effects of what had been the Fed's previous policy of quantitative tightening, which would make the effective Federal Funds Rate much higher, boosting the odds of recession starting between September 2019 and October 2020 as high as 54.5%. Like the 'official' Wright odds of 11% (or a one-in-nine probability), that alternative estimated risk of recession has dipped fractionally in the six weeks since we estimated that probability, but would still round to about 54%.

The Recession Probability Track is based on Jonathan Wright's recession forecasting method using the level of the effective Federal Funds Rate and the spread between the yields of the 10-Year and 3-Month Constant Maturity U.S. Treasuries. If you would like to run your own recession probability scenarios, as we recently did after factoring in all the quantitative tightening the Fed achieved prior to its policy reversal in July 2019, please take advantage of our recession odds reckoning tool.

It's really easy. Plug in the most recent data available, or the data that would apply for a future scenario that you would like to consider, and compare the result you get in our tool with what we've shown in the most recent chart we've presented above.

If you would like to catch up on any of the analysis we've previously presented, here are all the links going back to when we restarted this series back in June 2017.

Previously on Political Calculations

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October 9, 2019

Preliminary and revised data for the new home sales market indicates it continued to grow robustly in August 2019, continuing its strong rebound of recent months.

The following chart shows the strength of that rebound as measured by the trailing twelve month average of the new home sales market capitalization, which has come as mortgage rates in the United States have dropped dramatically since the end of 2018 to their lowest levels since 2016.

Trailing Twelve Month Average U.S. New Home Sales Market Capitalization, January 1976-August 2019

Both average sales prices and the number of sales rose in August 2019's initial estimates, where the combination has pushed the market capitalization of new homes to their highest levels since 2007. The effects of the rebound are particularly visible in California's largest-in-the-nation real estate markets, which are still far below the state's early 2018 peak and has occurred even though the nation's third largest real estate market by market cap, Texas, has experienced a downtrend during 2019.

Calculated Risk's Bill McBride believes 2019 is on track to become the best year for new home sales in the U.S. since 2007. In our next chart, we've calculated the average market cap for the U.S. new home sales market for each year from 1975 through the year-to-date in 2019, in both nominal and inflation-adjusted terms, to confirm that's the case.

Average Annual U.S. New Home Sales Market Capitalization, 1975 - 2019 YTD (through August 2019)

In nominal terms, 2019 is on track to be the best year since 2006, which marked the beginning of the deflation phase of the first U.S. housing bubble. In inflation-adjusted terms, 2019 is on track to be the best year since 2007.

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October 8, 2019

Something surprising showed up in the international trade data between the U.S. and China during August 2019. For the first time since the end of March 2018 when both nations entered into a tariff war with each other, the year over year growth rate of U.S. exports to China has turned positive, indicating growth rather than shrinkage.

You can see for yourself in the following chart illustrating the year-over-year exchange rate-adjusted growth rate of U.S. exports to China and of China's exports to the U.S. from January 1986 through August 2019.

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

It would be nice if that were a genuinely positive development, sparked by a resurgence of organic growth in China's domestic economy, but it reflects two other things instead:

  • The effectiveness of China's deliberate strategy of targeting goods such as soybeans produced in states where President Trump won narrow margins in the 2016 election with punitive tariffs, which succeeded in lowering U.S. exports so much by August 2018 that it takes relatively little to show positive year over year growth.
  • China's dire need to deal with the impact of the African Swine Flu epidemic on its domestic hog production, which was amplified by unintended consequences arising from its leaders' tariff war strategy.

We drilled down into the Census Bureau's detailed trade data, where we found two major positive contributions to August 2019's year-over-year export growth:

  • U.S. exports of soybeans to China were 20.6 times higher in August 2019 than August 2018, rising from $46 million to $945 million.
  • U.S. exports of pork meat to China rose by a factor of 8.5, from $10.5 million to $89 million.

In mid-September 2019, China announced it would lift its punitive tariffs on U.S.-produced soybeans and pork. In late-September 2019, China's government confirmed its "importers have agreed to buy American soybeans and pork".

References

Board of Governors of the Federal Reserve System. China / U.S. Foreign Exchange Rate. G.5 Foreign Exchange Rates. Accessed 4 October 2019.

U.S. Census Bureau. Trade in Goods with China. Accessed 4 October 2019.

U.S. Census Bureau. U.S. Trade Online. Accessed 4 October 2019. 


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October 7, 2019

Two weeks ago, the S&P 500 (Index: SPX) fell because investors believed the prospect of the Fed cutting rates in the first quarter of 2020 had been taken off the table as the chances the Fed would cut rates during the fourth quarter of the current year became an even money bet.

What a difference a week makes. By the end of the first week of October 2019, not only had the odds of a quarter point rate cut in 2019-Q4 almost been locked in, the probability of another quarter point rate cut in 2020-Q1 was back in play. Here's a snapshot of those rate cut probabilities as given by the CME Group's FedWatch Tool at the close of trading on Friday, 4 October 2019:

CME Group FedWatch Tool Probabilities of Federal Funds Rate Changing at Future FOMC Meeting Dates, Snapshot on 04 October 2019

Given the math behind how stock prices work, that shift in the forward-looking focus of investors was accompanied by a rebound in stock prices off their lows for the week.

Alternative Futures - S&P 500 - 2019Q3 - Standard Model - Snapshot on 4 Oct 2019

We find the level of the S&P 500 is now consistent with investors splitting their attention between 2019-Q4 and 2020-Q1, as hypothesized. In the chart above, the level of the S&P 500 is close to the trajectory coinciding with investors focusing on 2020-Q3, but we find no evidence in the flow of news for the week that investors are focusing any part of their attention on that very distant future quarter. Logically, because investors have been alternately shifting their attention back and forth between 2019-Q4 and 2020-Q1 during 2019-Q3, in the absence of any evidence supporting a shift in focus toward 2020-Q3, it makes more sense that the shifts between 2019-Q4 and 2020-Q1 has continued.

It certainly isn't for a lack of looking for evidence supporting a shift in attention toward 2020-Q3 on our part. Here are the market moving headlines we identified during the first week of October 2019, where we find lots of evidence in support of the investors looking at either 2019-Q4 or 2020-Q1, but not any further into the future.

Monday, 30 September 2019
Tuesday, 1 October 2019
Wednesday, 2 October 2019
Thursday, 3 October 2019
Friday, 4 October 2019

Elsewhere, Barry Ritholtz extracted six positives and six negatives from the past week's economics and market-related news, where unicorns made both sides of the ledger!

As a bonus, Barry also interviewed Robert Shiller for his Masters In Business podcast on Bloomberg, where he cited Shiller's seminal 1981 paper "Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends?". It's pretty amazing how far we've come in the last 38 years, where in 1981, Shiller didn't have access to dividend futures and thus couldn't quantify the volatility that arises from investors rationally shifting the time horizon in which they make their current day investing decisions between different points of time in the future, which to Shiller, seemed like irrational behavior.

Next week, we'll roll our spaghetti forecast chart of the quantum trajectories the S&P 500 might take depending upon which point in the future investors fix their attention upon forward through the end of the fourth quarter of 2019. For this edition of our S&P 500 chaos series, it made sense to look backward at 2019-Q3 one last time....

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Materials on this website are published by Political Calculations to provide visitors with free information and insights regarding the incentives created by the laws and policies described. However, this website is not designed for the purpose of providing legal, medical or financial advice to individuals. Visitors should not rely upon information on this website as a substitute for personal legal, medical or financial advice. While we make every effort to provide accurate website information, laws can change and inaccuracies happen despite our best efforts. If you have an individual problem, you should seek advice from a licensed professional in your state, i.e., by a competent authority with specialized knowledge who can apply it to the particular circumstances of your case.