Political Calculations
Unexpectedly Intriguing!
31 March 2017

We frequently feature U.S. patents that have some pretty interesting illustrations inside of them, but until today, we've never seen one that has illustrations that might be considered to be the equivalent of a video game "Easter egg" inside of it.

But that's exactly what we found lurking within U.S. Patent No. 7,744,313, which was issued on 29 June 2010, and which features inventors Jeffrey Terai and James M. McDole's concept for a "Fixed Security Barrier".

Their invention is really a sort of fishing net for catching boats, which we'll pause to let them explain in the patent's abstract description:

A barrier for stopping unwanted watercraft and subsurface intruders from entering into a port or off-shore structure is provided. In one embodiment, the invention is a barrier comprised of a vertical net structure supported from the sea floor. The barrier comprises vertical supports and a net assembled between the vertical supports with a system of ropes and energy absorbing devices. The structural components of the barrier are designed and configured in a manner as to absorb and displace the kinetic energy generated by an explosive laden small watercraft traveling at a high rate of speed. In another embodiment, invention is a barrier system installed around the perimeter of a water side or offshore facility. This barrier system comprises a bottom founded perimeter fence having a gate system and a series of barriers comprised of a vertical fence structure supported from the sea floor. This barrier system is designed to control access and to protect a water side or offshore facility from potential terrorism threats.

Like many security-oriented patents, the text of the patent is pretty dry. But what stands out about U.S. Patent 7,744,313 is the inventors' series of figures that illustrate how their invention would work, which are reminiscent of the kind of flipbook animation that bored students often do with either the pages of old-school textbooks or with Post-It notes. It's not the sort of thing that you would expect to see in an officially-issued U.S. Patent.

So we've put together a flipbook animation of Figures 7A through 7F from U.S. Patent 7,744,313, in which unseen terrorists speeding toward an unseen high value target in their speedboat are rudely interrupted by the inventors' defensive net. Enjoy!

U.S. Patent 7,744,313 Figures 7A through 7F

And that's how you go fishing for boats!

Other Stuff We Can't Believe Really Exists


30 March 2017

How much money is the City of Philadelphia collecting from its controversial soda tax? And how does that compare with the amount of money was Philadelphia's mayor and city council counting on collecting from their excise tax on naturally-sweetened, calorie-laden beverages and artificially-sweetened, diet beverages being distributed to the city's grocery and convenience store retailers?

If we go by the claims of the city's officials, they're collecting more revenue than they expected.

For the second consecutive month, the city's Department of Revenue announced the funds collected from the Philadelphia Beverage Tax brought in more money than its projections predicted.

The Revenue Dept. said February yielded $6.4 million in soda tax revenue, and also upped the initial figure for the first month of the year to $5.9 million from $5.7 million, an anticipated adjustment.

The Mayor's Office estimated soda tax revenue in February to amount to $6.3 million, according to the Kenney Administration's budget proposal.

At a total of $12.3 million in revenue collected so far, the beverage tax does not appear to be deterring consumers at the rate that city officials expected nor at the levels Pepsi and others claim.

Back on 15 February 2017, Philadelphia's City Manager Anna Adams indicated that the city would only take in $2.3 million from the soda tax during the entire month of January, which proved to be far below the $5.7 million that it was initially reported to have collected just a week later. That latter figure proved to be just under 97% of the final revised revised figure of $5.9 million that was reported on 23 March 2017.

Given those latter two numbers, that $2.3 million looks like Philadelphia's city manager was playing the same kind of deceptive shell game that corporate CEOs do with deliberately lowballing their company's earnings estimates to make their actual earnings appear better by comparison.

But is that the game that Philadelphia's public officials are playing?

To find out, we would need to know much money that the City of Philadelphia could legitimately have expected to collect through its soda tax in January 2017, and to determine that value, we would need to have a good idea of the quantity of beverages that would be distributed to be sold in the city during that month.

The Philadelphia Inquirer has previously reported that for Philadelphia's new soda tax to perform as the city's officials desire, the city would need to collect an average of $7.7 million per month from the shipments made to the city's beverage distributors.

But, as we recently demonstrated, the actual total value of shipments by beverage manufacturers follows a very regular and very predictable seasonal pattern. That is an important bit of information for two reasons:

  1. Because the taxes collected from Philadelphia's soda tax would be directly proportional to the total value of the beverages being shipped to the city's retailers by local beverage distributors.
  2. Because the seasonal pattern can tell us how much the city would typically need to collect in soda taxes during each month for it to hit its desired tax revenue levels.

With that being the case, we'll assume that the average seasonal pattern that applies for the total value of beverages shipped each month in the U.S over the years from 1992 through 2016 applies for the City of Philadelphia. The chart below illustrates that seasonal pattern.

Value of Beverage Manufacturers' Shipments by Month as Average of Annual Average Value of Shipments, 1992-2016

Our next step will be to visualize how that seasonal pattern would translate into a reasonably approximation of the amount of tax revenue that the city might collect each month from its new soda tax. The following chart reveals what we discovered when we plotted those desired tax revenue levels with respect to the city's actual monthly soda tax collections to date.

Desired vs Actual Estimates of Philadelphia's Monthly Soda Tax Collections, 2017 - Snapshot Final Data for January 2017 with Preliminary Data for February 2017

Based on the seasonal pattern for U.S. beverage distribution, Philadelphia's "Desired Soda Tax Revenues" per month are indicated in blue. Averaged together, the monthly desired tax revenue works out to be $7.7 million. This is the data that corresponds to what Philadelphia's city officials expect to collect from the city's soda tax.

The city's Actual Soda Tax Revenues per month are shown in red. Based on final data for January 2017 and preliminary data for February 2017, Philadelphia's soda tax collections are so far falling anywhere from 6% to 10% short of Philadelphia's city officials desired level of revenue.

This chart also illustrates just how preposterous Philadelphia City Manager Anna Adams' estimate of $2.3 million for the city's soda tax collections in January 2017 was.

Going back to the seasonal pattern data, one factor that we're not taking into account is actual temperature. Since beverage consumption is influenced by temperatures, where greater quantities of beverages are consumed during months characterized by higher temperatures, Philadelphia's weather may affect the city's soda tax collections. That may indeed be the case for February 2017, which was unusually warm in Philadelphia, with average temperatures more than 9 degrees Fahrenheit (5 degrees Celsius) above the month's typical temperatures.

Given its track record to date, in a funny way, Philadelphia's city officials may be counting on global warming to get the tax revenues they desire from their new soda tax!

Previously on Political Calculations

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29 March 2017

A lot of business and economic data shows seasonal patterns. One example of data that shows a remarkably seasonal pattern is the total value of shipments delivered by beverage manufacturers to their distributor customers, which often precedes the actual consumption of these bottled and canned drinks by their end consumers in anywhere from a matter of days to several weeks.

The following chart shows that data for U.S. beverage manufacturers for each month from January 1992 through January 2017.

Total Value of Beverage Manufacturers' Shipments, Not Seasonally Adjusted,  January 1992-January 2017

In this chart, we can see that the not seasonally-adjusted data follows a remarkable similar pattern from year to year, which has strongly persisted over time even as the rolling 12-month average of these shipments has more than doubled from 1992 through 2016. Perhaps more remarkably, we can see the overall magnitude in the swings from one season to the next grow in size as the total value of shipments has increased.

To better visualize that seasonal variation, we calculated the mean value of beverages shipped each month by their U.S. manufacturers in each year, then calculated the percentage that each month's total shipment value represents with respect to that average value, where a value of 100% would coincide with a month's shipments being equal to the annual average for the year in which it occurred. The results of that math are shown in the following chart.

Value of Beverage Manufacturers' Shipments by Month as Average of Annual Average Value of Shipments, 1992-2016

With the data indexed with respect to each year's average total shipment value, we see that the seasonal swings are really pretty consistent over time, where the increasing magnitude indicated by the first chart is a result of the same size percentage swings being applied to the increased overall value of shipments.

At the same time, we see that January in each year marks the lowest number of shipments, coming in at roughly 85% of the average monthly value recorded during the year. The value of shipments then predictably increases through March, holds flat in April, then resumes increasing before typically peaking in June. Much of this pattern coincides with rising temperatures in the U.S. as the weather transitions from winter to spring and then to summer, which represents the peak period for manufactured beverage consumption in the U.S.

In July, the total value of beverage shipments falls in each year before rising to peak once more in August, which is then followed by a relatively steady decline in each following month through December. The cycle then repeats with the value of beverage shipments crashing to their January lows in the next year.

The June-July-August peak-dip-peak is also remarkable in showing the advance delivery of beverages ahead of their periods of peak consumption, where June shipments supply consumption during the U.S.' Fourth of July holiday and the August shipments are supporting consumption during the period of the U.S. Labor Day holiday, which falls on the first Monday in September each year.

This data is something that we dug up as part of another project, which we will get to in the very near future. For now, we thought it was perhaps interesting enough to present on its own as a data visualization exercise, and because major U.S.-owned beverage manufacturers like Coca Cola (NYSE: KO), PepsiCo (NYSE: PEP), Dr. Pepper Snapple Group (NYSE: DPS), National Beverage Corporation (NASDAQ: FIZZ) and a considerable number of smaller public firms, private firms and foreign-owned beverage manufacturers with U.S. production facilities could use a hug!

Data Source

U.S. Census Bureau. Beverage Manufacturing: U.S. Total, Not Seasonally Adjusted Value of Shipments [Millions of Dollars], Period: 1992 to 2017. Manufacturers' Shipments, Inventories, and Orders. [Online Database]. Data Extracted on: 28 March 2017.

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28 March 2017

We're tied up with other projects today, but for some quick fun, we threw together the following chart showing the trajectory of the Dow Jones Industrial Average (DJIA) since 8 November 2016 (a.k.a. "The Age of Trump"), where we've noted some unusual winning and losing streaks in just the past two months....

Longest Winning and Losing Streaks in the Dow Jones Industrial Average From 8 November 2016 Through 27 March 2017

Going back to 2 May 1885, there have been a total of 4 winning streaks where the DJIA closed higher than its previous day's close over 12 consecutive trading days, with the fourth just having ended on 27 February 2017.

By contrast, there have been 43 losing streaks where the DJIA closed lower than it's previous day's closing value on 43 separate occasions since 2 May 1885, where the current losing streak through yesterday, 27 March 2017 may not yet be over.

Should the current losing streak extend a ninth day, it will mark just the twelfth time in the last 132 years where the Dow has dipped for that many days in a row.

Data Source

Williamson, Samuel H. "Daily Closing Values of the DJA in the United States, 1885 to Present," MeasuringWorth, 2017. URL: http://www.measuringworth.com/DJA/. Accessed 28 March 2017.

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27 March 2017

We've had a remarkably good run in forecasting where the S&P 500 was likely to go over the past several months, but that almost perfect track record came to an end on Tuesday, 21 March 2017 when investors suddenly shifted their forward-looking attention away from 2017-Q2 to focus more strongly upon the more distant future of 2017-Q3.

Alternative Futures - S&P 500 - 2017Q1 - Standard Model - Snapshot on 24 March 2017

While we had anticipated that the volatility of the S&P 500 would be very likely to increase, the timing of the "screeching halt" to which the S&P 500's extraordinarily low level of volatility over the last several months came as its "history-setting trading streak" ended on 21 March 2017 caught us by surprise. You can see that surprise in the chart above where we had shown our short-term forecast range (the red-lined box) extending through 22 March 2017.

However, the dynamics that coincided with that sudden shift were those that we described in our notes last week. At present, we think that investors are still splitting their attention between 2017-Q2 and 2017-Q3 in setting current-day stock prices, but are currently placing a higher weighting on 2017-Q3 in doing so, which given where stock prices were, meant that stock prices would fall.

Right now, given the level of stock prices between the two alternative trajectories that the S&P 500 would be following if investors were exclusively focused on either 2017-Q2 or 2017-Q3 in setting their future expectations, we think that investors are now placing a 62% probability that the Fed will be compelled by poor economic data to delay its next intended increase short term U.S. interest rates into that more distant quarter.

The significant difference in the future expectations for 2017-Q2 and for 2017-Q3 is what will lead the S&P 500 to experience higher-than-recent levels of volatility during the next several months, where good news will lead investors to focus in the nearer term (as the Fed will be more likely to next hike interest rates sooner), and where bad news will lead investors to focus on the more distant future, where the Fed will delay its next rate hike.

Next week, we'll show you what that potential trading range will look like over 2017-Q2. Until then, here are the headlines that we identified as noteworthy over Week 4 of March 2017.

Monday, 20 March 2017
Tuesday, 21 March 2017
Wednesday, 22 March 2017
Thursday, 23 March 2017
Friday, 24 March 2017

For a bigger picture of the week's more notable news, Barry Ritholtz lists the week's positives and negatives for the U.S. economy and markets. (Spoiler alert: the week had more negatives than positives!)

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24 March 2017

Not long ago, Core77's Rain Noe ordered a battery from Staples that was delivered to his home in a box that was way bigger than the battery enclosed inside.

It turns out that delivery was the unintended consequence of a decision by Staples, one that actually saves the company quite a lot of money, to both standardize the size of the corrugated (cardboard) packaging in which in ships goods to its online customers and to automate as much of the packaging operations at its fulfillment center as it can. A Core77 reader found a two minute-long video that describes how Staples fulfillment center packages the goods it ships.

So how did this choice by Staples lead to such a seemingly wasteful mismatch between the size of ordered good and the size of the packaging in which it was delivered? Rain explains:

... it appears that Staples has chosen the sizes of corrugated Z-fold most common to their order, with my tiny battery being an anomaly.

But that's not the end of the story. Packsize, the maker of the automated packaging equipment that Staples uses, recognizes the opportunity it has to benefit in the market from continuing to minimize the waste that results from shipping products to customers in oversize packages by better tailoring its on-demand packaging product line to produce "right-sized" boxes.

And that's the future of packaging. As for Packsize, the company often contracts with its customers to provide them its packaging machines at no cost, where its revenue comes from selling the Z-fold corrugated cardboard packing material used by the machines to the companies that acquire them. Or as Rain notes:

It looks like the razor-and-handle business model works well here.

Speaking of which, if you weren't already familiar with the BBC's 50 Things That Made the Modern Economy series of podcasts, here are links to a few of its episodes that directly overlap with the modern business of packing and shipping:


23 March 2017

Beginning in 2014, millions of lower income-earning Americans became eligible to have fully government-subsidized health insurance coverage through the U.S. government's Medicaid welfare program thanks to the expansion of eligibility for that program provided for by the Affordable Care Act (ACA), which is more popularly known as Obamacare. Unfortunately, that expanded access to health care may very well have caused an increase in death rates due to drug overdoses in the United States to such a degree that the overall estimated life expectancy of Americans has declined.

The influence of Obamacare's expansion of health care provided through Medicaid can be seen by comparing the death rates due to drug overdoses in the 28 states (and the District of Columbia) that chose to expand the enrollment of their state's Medicaid programs with the death rates in the 22 states that chose to not expand their state's Medicaid enrollment as part of the Affordable Care Act in the years before and after its implementation. In the following chart, we've indicated the highest, lowest, average (mean) and median death rates recorded among the individual states that participated in the Medicaid expansion.

Age-Adjusted Death Rates per 100,000 Population Due to Drug Overdoses in 28 Medicare Expansion States and District of Columbia, 2010-2015

Starting with the lowest death rates per 100,000 population reported among the Medicaid-expansion states, which mostly applies for North Dakota (for which no reliable data was available for 2011, where Iowa's data marks the low end of the scale in that year), we see that the pre-Medicaid expansion trend was essentially flat from 2010 through 2013, followed by a sharply rising trend in 2014 through 2015.

At the high end of the scale, where the data applies for the state of Washington), we see an overall rising trend from 2010 through 2013 (with a spike in 2011, which may be highly relevant in this discussion because Washington was one of six states to implement the early expansion of its Medicaid program in that year), followed by a much sharper increase from 2014 through 2015.

That overall pattern of slowly rising trend in 2010-2013 and much more sharply increasing rate of deaths from drug overdoses after Obamacare's wider expansion of Medicaid enrollments in these states from 2014 through 2015 is also evident in the mean (average) and median death rates recorded among the individual states in this grouping.

But what about the states that didn't expand their Medicaid enrollments as part of the Affordable Care Act? The following chart looks at the similar highest, lowest, mean and median data for death rates per 100,000 population from drug overdoses for these 22 states in the years before and after the implementation of Obamacare.

Age-Adjusted Death Rates per 100,000 Population Due to Drug Overdoses in 22 Non-Medicare Expansion States, 2010-2015

Starting again with the trend for lowest death rates attributed to drug overdoses in the non-Medicaid expansion states in the years from 2010 through 2015, we see here that the trend may be described as being somewhere between flat and slightly rising.

The same observation holds true for the states recording the highest rates of death due to drug overdoses.

However, when we look at the data for the mean and median drug overdose death rates in this grouping of states, we see a slow increase in the period from 2010 through 2013, followed by a more rapid increase in the years from 2014 through 2015 for the median data, but a slower increase in the average death rate recorded in these states during these latter two years, where the average dropped below the median value in 2015.

In our final chart, we'll use animation to more directly compare what happened between the median and avarage death rates in both groups of states. If you're reading this article on a site that republishes our RSS news feed, you may want to click through to our site to see the animation (assuming you've also enabled JavaScript on your web browser).

Mean and Median Age-Adjusted Death Rates per 100,000 Population Due to Drug Overdoses in Medicaid-Expansion and Non-Expansion States, 2010-2015

The key observation to take away from this comparison is that the increase in death rates due to drug overdoses in Obamacare's Medicaid expansion states has accelerated much faster in 2014 and 2015 than what was observed in the non-Medicaid expansion states.

At this point, we do need to point out the statistical truism that correlation is not necessarily causation. For instance, it could be that the states that were more likely for economic reasons to experience increasingly higher rates of deaths from drug overdoses perhaps influenced them to choose to join in the Affordable Care Act's expansion of their states' Medicaid programs, where they hoped to cash in on the additional funding provided for Medicaid by the ACA from the federal government.

However, the data does suggest that the practices of the Medicaid program are a significant contributing factor, where the health care provided by the U.S. government is directly responsible for the increase, where we can confirm that both federal and state-level Medicaid officials have been very specifically responding to the increases in drug overdose-related death rates in the U.S. by restricting the prescription of the opioid-based medications at the center of the nation's increase in overdose deaths.

As rates of prescription painkiller abuse remain stubbornly high, a number of states are attempting to cut off the supply at its source by making it harder for doctors to prescribe the addictive pills to Medicaid patients.

Recommendations on how to make these restrictions and requirements were detailed in a “best practices” guide from the federal Centers for Medicare and Medicaid Services....

Some states’ efforts to curtail prescribing predated CMS’ bulletin. But the advisory added new fuel to the trend. States such as New York, Rhode Island and Maine adopted new prescription size limits this year, and West Virginia will require prior authorization starting next year. In the 2016 fiscal year, 22 states either adopted or toughened their prescription size limits, and 18 did so with prior authorization.

The goal is to make physicians think twice before prescribing highly addictive opioids — a change many say is necessary, especially within the state-federal health insurance program for low-income people. After all, research indicates Medicaid beneficiaries are prescribed opioids at twice the rate of the rest of the population, and are at three to six times greater risk of an overdose.

Unfortunately, the problem of the federal government-provided health care programs in contributing to the nation's increase in drug overdose death rates is not limited to the Medicaid welfare program, where similar patterns in increased opioid addiction and drug overdose death rates are being seen among Americans who receive care from the Veterans Administration (VA) and the Indian Health Service (IHS), which are the U.S. government's single payer-style health care programs.

The expansion of "free" health care provided for by the U.S. federal government through these programs and through Obamacare's expansion of the Medicaid welfare program may very well have backfired by contributing to the nation's increase in drug overdose death rates and the decline in American life expectancy.


U.S. Centers for Disease Control and Prevention. National Vital Statistics System, Mortality. CDC WONDER. [Online Database]. Atlanta, GA: US Department of Health and Human Services, CDC; 2016. [Note: Deaths were classified using the International Classification of Diseases, Tenth Revision (ICD–10). Drug overdose deaths were identified using underlying cause-of-death codes X40–X44, X60–X64, X85, and Y10–Y14.]

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22 March 2017

Since we first uncovered the importance of dividend futures for anticipating future stock prices, we've been keeping track of that data on a regular basis.

Doing that has been a valuable exercise, not just because of our primary use for the data, but also because it gives us the ability to recreate what the future expectations of investors were on any trading day in the past for which we have the data.

For the Chicago Board of Exchange's dividend futures contracts, that history extends back to when they were first rolled out back in early 2010. We thought it might be interesting to compare what the future for dividends in each quarter looked like on the first day that the dividend futures contract for a given future quarter went into effect (one year before the futures contracts for it expires on the third Friday of the month ending the quarter for which it applies) and the value of the amount of dividends paid out during that quarter on the last day before its dividend futures contract expired.

The results are presented in the following chart:

First Day Forecast and Last Day Values Recorded for CBOE Dividend Futures, 2011-Q1 through 2017-Q1, with Forecast Values Through Quarters Ending in 2018-Q1

The chart begins with a comparison of the results between the dividend futures data for 2011-Q1 that was first published in March 2010 and the value of the dividend futures contract indicating the amount of the S&P 500's dividends per share would be paid out by the time that the dividend futures contract for 2011-Q1 expired on the third Friday of the March 2011.

Aside from the CBOE's dividend futures contracts' first year and a half, where the final day's dividend values had risen well above their initially recorded values, we see that there has generally been pretty close agreement between the initially forecast value and the value recorded on most dividend futures contracts' final day.

There are however two exceptions that stand out. The first is the event we've describes as "The Great Dividend Raid of 2012", where if you want to find out more about how the fear of higher dividend tax rates that were set to take effect in 2013 caused dividend payouts (and stock prices) in 2012-Q4 to surge so much higher than their initially forecast values, be sure to follow the link!

The other exception applies to the quarter whose dividend futures contract just expired last Friday, 2017-Q1, which has coincided with much of what the financial media has called the "Trump Rally", where the better than initially forecast results appear to have been fueled by improved earnings.

Sometime in the near future, we'll use our historic data on future expectations to take a closer look at the so-called Trump Rally.

Data Sources

EODData. Implied Forward Dividends March (DVMR). [Online Database]. Accessed 21-Mar-2017.

EODData. Implied Forward Dividends June (DVJN). [Online Database]. Accessed 21-Mar-2017.

EODData. Implied Forward Dividends September (DVST). [Online Database]. Accessed 21-Mar-2017.

EODData. Implied Forward Dividends December (DVDE). [Online Database]. Accessed 21-Mar-2017.

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21 March 2017

How well can dividend forecasts for individual stocks predict their future prices?

That's a question that we took on earlier this year, where we resuscitated an old stock price prediction technique to see how well it might work to identify potential investment opportunities for five companies, whose stocks had been identified as strong candidates for dividend increases during 2017-Q1. The five stocks and their dividend increase forecasts were:

  • Comcast (NASDAQ: CMCSA) - 15% increase
  • Home Depot (NYSE: HD) - 14% increase
  • Cisco Systems (NASDAQ: CSCO) - 15% increase
  • TJX Companies (NYSE: TJX) - 16% increase
  • Vulcan Materials (NYSE: VMC) - 25% increase

We then took annual dividend data and the stock prices recorded for each of these firms on the first day of trading in each year going back over the last several years, and plotted a relationship between them. We then used that relationship to identify if any stock prices in early 2017 were either potentially overvalued or undervalued with respect to the trends we identified. And then we sat back until we had results to see how the stock prices for each changed after they declared their dividends during the quarter.

We now have the results for 4 of the 5 companies, where we're only awaiting TJX's next dividend declarations to officially close the books on this experiment. The following chart shows the historical data we used to generate the trendlines for each stock, their forecast annual dividend for 2017, where their stock prices went on the day they declared their dividends and also where they were as of the close of trading on 20 March 2017.

Projected Trends for Stocks Forecast to Boost Dividends in 2017 Forward Annual Dividends Per Share vs January Price Per Share, Update 2017-03-20

In our original forecast, we had only identified two stocks that offered any sort of investment potential based on the value of their stock prices with respect to their trendlines: Home Depot (NYSE: HD), which looked to be undervalued, and Vulcan Materials (NYSE: VMC), which looked to be overvalued.

In the nearly two months since that analysis, we see that both assessments were correct. In VMC's case, we see that as VMC's dividend came in as expected, its stock price has moved downward to converge with its trendline. In Home Depot's case however, we see that company's dividend hike was far in excess of what had been forecast for it as the company reported a blowout quarter, where its stock price has risen, but not as strongly as its trendline would have suggested it might. And in truth, it has been diverging away from its historic trendline even as it has risen, which perhaps suggests that older trend may no longer be in effect and needs to be redrawn.

There was one real surprise for the quarter where these five stocks were concerned. Bucking the best-laid plans of professional dividend forecasters, Comcast (NASDAQ: CMCSA) pulled a fast one and split its stock on a 2:1 ratio, halving its dividend and very nearly also its stock price! The fun part of that change is that the stock price appears to have continued to track along its trendline, although it is now on the high side of it, suggesting that the stock is somewhat overvalued. Meanwhile, the stock price for Cisco Systems (NASDAQ: CSCO) behaved almost exactly as expected, keeping comparatively close to both its forecast and its historic trendline.

After TJX has declared its next dividend, which we think will be in April, we'll update the original version of this post on our site.

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20 March 2017

The third week of March 2017 was an up week for the S&P 500, where the index closed at 2,378.25 on Friday, 17 March 2017, just 5.65 points (0.24%) above where it closed at the end of the prior week of trading.

In the middle of the week that was Week 3 of March 2017, the Fed followed through and did exactly what it had been strongly signalling that it would do over the last several weeks, and boosted short term interest rates by a quarter percent, putting the Federal Funds Rate into a range between 0.75% and 1.00%.

From our perspective, both events were expected, where stock prices continued to fall within the range we first predicted two weeks ago (but didn't explain until last week).

Alternative Futures - S&P 500 - 2017Q14 - Standard Model with Connected Dots Between 2017-03-03 and 2017-03-21 - Snapshot on 17 March 2017

That doesn't mean however that we weren't surprised. For the Fed to have drawn such a strong focus where it would be using its influence to affect the expectations that investors have for the future, we should have seen a stronger upward movement in U.S. stock prices on Wednesday, 15 March 2017 than what the market saw.

That's because nearly all the Fed's officials have been doing everything they can to set the expectation that they will be hiking U.S. interest rates again in the very near future, which would put the timing of their next hike sometime in 2017-Q2, and which would most likely be announced at the end of the FOMC's June 2017 meeting.

Given where stock prices have been, since the expectations for the change in the year over year growth rates for S&P 500 dividends would place the S&P 500 higher than it currently is, for the Fed to have been successful in setting that focus would have coincided with a much larger jump in stock prices than what was recorded on Wednesday, 15 March 2017. At the very least, it should have moved to the middle of our echo-effect adjusted forecast range (indicated by the red-lined box on the chart above).

We would have expected that result because we have observed exactly that kind of behavior in stock prices following previous FOMC meetings where the Fed has directed the attention of investors to focus upon specific points of time in the future. So much so that we've used the timing of the FOMC's announcements to check the calibration of our dividend futures-based forecasting model.

So what gives now in Week 3 of March 2017? Why would stock prices appear to be behaving differently now than they have on the occasion of the Fed's previous announcements? Why would stock prices stay so much lower than what we would expect from even our echo-effect adjusted forecast?

We think the key to understanding what's going on with stock prices came out during Janet Yellen's post-FOMC announcement press conference, where Bloomberg's Kathryn Hays asked a pointed question:

KATHLEEN HAYS. Chair Yellen, Kathleen Hays. Oh excuse me, Kathleen Hays from Bloomberg. I'm going to try to take the opposite side of this because, on this question about market expectations and how the markets got things wrong, and then how you say the Fed suddenly clarified what it already said. But, for example, if the--if you look at the Atlanta Fed's latest GDP tracker for the first quarter, it's down to 0.9 percent. We had a retail sales report that was mixed, granted the, you know, upper divisions of previous months make it look better, but the consumer does not appear to be roaring in the first quarter, kind of underscoring the wait-and-see attitude you just mentioned. If you look at measures of labor compensation, you note in this statement that they're not moving up. And, in fact, they are--and if you look at average--there are so many things you can look at. And you, yourself, have said in the past that the fact that that is happening is perhaps an indication there's still slack in the labor market. I guess my question is this, in another sense, what happened between December and March? GDP is tracking very low. Measures of labor to compensation are not threatening to boost inflation any time fast. The consumer is not picking up very much. Fiscal policy--we don't know what's going to happen with Donald Trump. And, yet, you have to raise rates now. So what is the, what is the motivation here? The economy is so far from your forecast, in terms of GDP, why does the Fed have to move now? What is this signal, then, about the rest of the year?

CHAIR YELLEN. So, GDP is a pretty noisy indicator. If one averages through several quarters, I would describe our economy as one that has been growing around 2 percent per year. And, as you can see from our projections, we, that's something we expect to continue over the next couple of years. Now that pace of growth has been consistent with a pace of job creation that is more rapid than what is sustainable if labor force participation begins to move down in line with what we see as its longer run trend with an aging population. Now, unemployment hasn't moved that much, in part because people have been drawn into the labor force. Labor force participation, as I mentioned in my remarks, has been about flat over the last 3 years. So, in that sense, the economy has shown, over the last several years, that it may have had more room to run than some people might have estimated, and that's been good. It’s meant we've had a great deal of job creation over these years. And there could be, there could be room left for that to play out further. In fact, look, policy remains accommodative. We expect further improvement in the labor market. We expect the unemployment rate to move down further, and to stay down for the next several years. So, we do expect that the path of policy we think is appropriate is one that is going to lead to some further strengthening in the labor market.

KATHLEEN HAYS. Just quickly then, I just want to underscore. I want to ask you, so following on that, you expect it to move. What if it doesn't? What if GDP doesn't pick up? What if you don't see wage measures rising? What if you don't, what if the core PCE gets stuck at 1.7 percent, would you, is it your view, perhaps, that if there's a risk right now in the median forecast for dots, that it's fewer hikes this year rather than the consensus or more?

CHAIR YELLEN. Well, look, our policy is not set in stone. It is data dependent and we're, we’re not locked into any particular policy path. Our, you know, as you said, the data have not notably strengthened. I, there's noise always in the data from quarter to quarter. But we haven't changed our view of the outlook. We think we're on the same path; not, we haven't boosted the outlook projected faster growth. We think we're moving along the same course we've been on, but it is one that involves gradual tightening in the labor market. I would describe some measures of wage growth as having moved up some. Some measures haven't moved up, but there's some evidence that wage growth is gradually moving up, which is also suggestive of a strengthening labor market. And we expect policy to remain accommodative now for some time. So we're, we’re talking about a gradual path of removing policy accommodation as the economy makes progress, moving toward neutral. But we're continuing to provide accommodation to the economy that's allowing it to grow at an above-trend pace that's consistent with further improvement in the labor market.

We're kind of in a unique position in that we recognized, very early in 2017-Q1, that the Fed could get away with hiking short term interest rates as they did during Week 3 of March 2017, with almost no negative effect from shifting the forward-looking focus of investors from 2017-Q2 (where it had been focused) to the nearer term future of 2017-Q1 because the expectations for changes in the year over year growth rate of dividends per share in 2017-Q1 have been nearly identical with the expectations for 2017-Q2 for months. That similarity between those sets of expectations has meant very little impact to stock prices for investors shifting their focus back and forth between the two quarters.

But that's also occurred as the lagging effects from the 2014-2016 economic slowdown have become more pronounced in the economic data, as described by Kathryn Hays in her question to Fed Chair Janet Yellen. That deterioration in the economic data is increasingly leading investors to set their attention toward 2017-Q3, which given where stock prices are today, would tend to weight them down, where investors are betting that the negative economic data will cause the Fed to back off from hiking short term interest rates again until that more distant future quarter.

The result then is a split in the forward-looking focus of investors, where they would appear to now be dividing their focus between 2017-Q2 and 2017-Q3 in setting today's stock prices, with the outcome of stock prices that have been mostly moving sideways to slightly upward.

In this environment however, otherwise minor news about that economic data that would ordinarily not be of much concern to investors can have an outsized impact. Good numbers on the economy will lead investors to favor 2017-Q2 for the timing of the Fed's next rate hike, with stock prices rising as a result. Bad numbers will tend to direct investors to focus on 2017-Q3, sending stock prices lower as they bet the Fed will delay its next rate hike.

That dynamic is setting up just as the S&P 500 is reaching a near record period of relatively low volatility, in which it hasn't seen a decline of at least 1% during the past 108 trading days. We think that's going to change in a very noticeable way in the very near future, especially as the underlying trajectories for the basic path of future stock prices is also about to change from generally upward to generally downward (more on that next week!) Until then, get ready to hang onto your hats!

The other news that stood out to us during the Week that Was Week 3 of March 2017 is linked below!

Monday, 13 March 2017
Tuesday, 14 March 2017
Wednesday, 15 March 2017
Thursday, 16 March 2017
Friday, 17 March 2017

Elsewhere, Barry Ritholtz recaps the week's positives and negatives for the U.S. economy and markets.

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17 March 2017

One of the more surprisingly popular posts that we've featured this year was "A Centrist's Guide to Media Bias and Usefulness", in which we featured a chart that ranks general news reporting web sites by the information quality of their reporting and a more accurate indication of their relative position on today's political spectrum than an earlier version that had gone viral.

So we were intrigued when we found that RealClearScience's Ross Pomeroy and Tom Hartsfield had joined with the American Council on Science and Health's Alex Berezow (formerly of RealClearScience) to create a similar chart for science news web sites.

Although here, they adapted the measure of information quality to focus more on how strongly evidence-based the stories covered by the science news sites they evaluated were, which they then assessed against a non-political spectrum that assesses how accessible or compelling each sites' stories would be to a general audience. Here's the chart they developed:

ACSH-RCS Infographic: Evidence-Based Science Reporting vs General Readability, 2017

Overall, their rankings are pretty solid, though we might tweak the placement of some of the sources (we would rank Wired slightly higher on the "compelling" measure for example, although that's attributable to the kinds of science news stories that we find interesting.

As for what sites we'd like to see added to the rankings, we would put Phys.org on the list, although it has considerable overlap with LiveScience as a news aggregator, it covers more fields of science. Its downside is that a lot of the stories it picks up read like press releases, so for a lot of stories, it would only fall in the "sometimes compelling" categorization.

We'd also look to add more maths into the mix, which though it falls outside the focus on evidence-based science, where mathematics news is concerned, there are considerably fewer sites that generate anything close to what might make for compelling reading by a general audience. And really, we'd recommend just three for that kind of consumption: Quanta and +Plus, for written word reporting, and BBC Radio 4's More or Less, which is presented in audio format.

Previously on Political Calculations


16 March 2017

Richard Feynman, offering amazing insight, back in 1981:

HT: Luboš Motl.

Sadly, not much has changed for social sciences in the intervening three and a half decades, where if the series of fresh junk science examples that we compiled during the last six months of 2016 is any indication, the same problems that Feynman noted back in 1981 are still pervasive in these fields - and the reason why is still the same: it takes much less effort to generate junk science than the care and attention it takes to generate the real thing!


15 March 2017

It has been just over a year since we last featured an update to our chart that correlates the change in atmospheric carbon dioxide with the relative health of the Earth's economy.

We haven't been holding back because we didn't want to share what we were seeing - we were holding back because large El Niño-related wildfires in Indonesia in 2015 flooded the Earth's atmosphere with extra carbon dioxide, which meant that we couldn't tell the difference between the additional contribution of CO2 from wildfires and that contributed by productive human activities in telling how much that each was influencing the atmospheric CO2 data.

But we're now coming up on when that extra CO2 will have fully dissipated from the atmosphere, leaving it clean enough to more clearly communicate how the global economy is faring. Speaking of which, here is what we see today when we measure the trailing twelve month average of year-over-year change in the parts per million of atmospheric carbon dioxide as measured at the Mauna Loa Observatory.

Trailing Twelve Month Average of the Year Over Year Change in the Parts Per Million of Atmospheric Carbon Dioxide, January 1960 through March 2017

In this chart, we've begun to see the trailing year average in the rate at which the amount of carbon dioxide in the Earth's air is changing begin to decline, which will become more dramatic over the next several months once we get past the one-year anniversary of when the contribution of 2015's Indonesia wildfires to atmospheric CO2 measured at Mauna Loa peaked in May 2016.

The next chart looks at the raw year-over-year change in atmospheric CO2, where we can confirm that those levels have dropped to levels that are consistent with the upper end of the range where they've been through much of the current century. [Note: The spike in the recorded data for January 2017 appears to be something of an anomaly.]

Year Over Year Change in the Parts Per Million of Atmospheric Carbon Dioxide, January 1960 through March 2017

We think that is in part because the economies of both China and the U.S. have begun growing more strongly than they were a year ago, where China's economy in particular appears to be growing much faster.

That's significant because China's economy is, by a wide margin, the world's largest producer of emissions of carbon dioxide. If China's economy is truly growing, it shows up in the Earth's atmosphere in a big way.

Data Source

National Oceanographic and Atmospheric Administration. Earth System Research Laboratory. Mauna Loa Observatory CO2 Data. [File Transfer Protocol Text File]. Updated 6 March 2017. Accessed 12 March 2017.


14 March 2017

The U.S. Census Bureau's data for goods and services exported to the U.S. from China and from the U.S. to China in January 2017 suggests that both economies are growing much more strongly than they were a year earlier.

At least, that's what is suggested by the sharp rise in the exchange-rate adjusted year-over-year growth rate of the value of goods and services traded between the two nations in the following chart!

Year Over Year Growth Rate of Exchange Rate Adjusted U.S.-China Trade in Goods and Services, January 1986 - January 2017

Before we continue, we should recognize that this data partially overlaps this year's weeklong Chinese New Year/Spring Festival holiday, which because the timing of the holiday is based on a lunar calendar, can greatly affect the year-over-year growth rate calculation for when the holiday falls entirely in the month of January or the month of February. This year, the Chinese holiday is straddling between the two months, where in 2016, it fell entirely within February.

Consequently, we anticipate that the data for the goods that China imports from the U.S. for February 2017 will show significantly higher growth. There is evidence that indicates that this is indeed happening, where China's preliminary trade data is showing such a remarkable surge in imports that they are outnumbering China's exports for the month:

China's imports surged 38.1 percent from a year earlier, the biggest increase since February 2012, official data showed on Wednesday, while exports unexpectedly fell 1.3 percent.

That left the country with a trade deficit of $9.15 billion for the month, the General Administration of Customs said.

Most analysts, however, attributed the rare trade gap to distortions caused by the long Lunar New Year celebrations, which began in late January this year but fell in February in 2016. Many businesses shut for a week or more and factory production and port operations can be significantly affected.

Looking at the U.S. side of the trade ledger, the preliminary data for February 2017 also suggests a year-over-year increase, which is expected to be followed by larger volumes of exports in March and April:

For January, the most recent month for which data is available, ports in the report handled what was cited as an “unusually high” 1.67 Twenty-Foot Equivalent Units (TEU), which was up 6.5 percent compared to December and was up 12.5 percent annually. This high volume level came on the heels of Asia-based factories shutting down in advance of the Lunar New Year.

The report pegged February at 1.61 million TEU for a 4.2 percent annual increase. But the larger volumes appear to be slated for March and April, which are estimated to hit 1.46 million TEU (a 10.6 percent annual gain) and 1.59 million TEU (a 10.1 percent annual gain), respectively. May is projected to be up 2.9 percent annually at 1.67 million TEU, and June is expected to see a 5.5 percent annual increase at 1.66 million TEU.

Some of the news reports on trade that we're seeing are also providing some estimates of how much the "border adjustment" tax being considered by the Trump administration would affect the trade balance, which we'll take a closer look at in upcoming weeks.

If you want to get there before us, you're more than welcome to do so - here's a tool that we built earlier this year to do some of the "tariff meets supply and demand" math, which you can pair with the appropriate trade source data.

Data Sources

Board of Governors of the Federal Reserve System. China / U.S. Foreign Exchange Rate. G.5 Foreign Exchange Rates. Accessed 12 March 2017.

U.S. Census Bureau. Trade in Goods with China. Accessed 12 March 2017.


13 March 2017

The S&P 500 in Week 2 of March 2017 ended just ever-so-slightly below where it opened the week. After generally trending downward on the week, the S&P 500 recovered much of what the index had lost on Friday, 10 March 2017.

Our alternative futures chart updates the trajectory that the S&P 500's closing prices took in the second week of March 2017 against the backdrop of what our dividend futures-based standard model projected, based on a combination of historical stock prices and the expectations that investors have for the amount of dividends per share to be paid out through specific points of time in the future.

Alternative Futures - S&P 500 - 2017Q1 - Standard Model with Connected Dots Between 2017-03-01 and 2017-03-21 - Snapshot on 2017-03-10

That brings us to the one thing that really drew your eyes in this week's updated chart - that red box outlining where the S&P 500's closing stock prices on each day of the week fell. The one that we teased at the end of last week's edition!

Like the previous red-shaded box, the new red-lined box represents our prediction of the range into which the S&P 500 will close over the indicated period of time during the indicated period of time, which runs from 3 March 2017 through 21 March 2017. We've drawn both boxes on top of our standard model's projections in order to account for the effect of the echo of past volatility on our standard model's projections, which arises as a result of our model's use of historic stock prices as the base reference points from which we project future stock prices.

Unlike the previous red-shaded box shown on the chart, the starting point is not aligned with the projected trajectory that corresponds with the future quarter that we believe investors have fixed their forward-looking attention upon in making their current day stock prices, which we believe to be the current quarter of 2017-Q1 based on the Fed's recent concerted efforts to convince market participants that it will next hike short term interest rates in the U.S. before the end of the quarter.

In doing that, we're trying something new, which draws upon our observation that the trajectory of stock prices are very much like a kinematics problem from physics. In such a problem, the actual trajectory that a body in motion that is being acted upon by external forces might take is anchored by its starting position.

So what we've done is to shift the forecast range that corresponds to where our standard model projects for investors fixing their attention on 2017-Q1 (and also 2017-Q2, since that future quarter's expectations are nearly identical to those of 2017-Q1) downward, to adjust for the starting value of the period of time covered by our prediction. We should note that if investors should shift their forward-looking attention to a different point of time in the future with different expectations associated with it, such as 2017-Q3 or 2017-Q4, stock prices will move outside the predicted range indicated by the red-lined box.

One week into this particular forecasting experiment, it seems to be working, but we have another week and a half to go. Until then, here are the headlines that caught our attention during the second week of March 2017....

Monday, 6 March 2017
Tuesday, 7 March 2017
Wednesday, 8 March 2017
Thursday, 9 March 2017
Friday, 10 March 2017

For a bigger picture of the week that was, Barry Ritholtz lists the week's positives and negatives for the U.S. economy. If you're a fan of business and finance-oriented podcasts, you might consider checking out Bloomberg's Masters in Business series, which Barry hosts. Speaking of podcasts, we would also strongly recommend Russ Roberts' Econtalk (which recently posted its rankings of its most popular sessions for 2016, and also the BBC's brilliant 50 Things That Made the Modern Economy, which is presented by More or Less' Tim Harford and which features excellent production values while packing an amazing amount of information into its 9-minute-long episodes.

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About Political Calculations

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