Political Calculations
Unexpectedly Intriguing!
November 30, 2016

We're well into what might be described as the third phase of the second U.S. housing bubble.

That might seem like a bold statement, but it's something that really becomes evident when you track the trailing twelve month averages of median new home sale prices against median household income in the U.S. on a monthly basis.

Trailing Year Average of U.S. Median New Home Sale Prices vs Trailing Year Average of U.S. Median Household Income, 2000-12 through 2016-10

The primary factor fueling the decoupling of median new home sale prices and median household income in the United States away from its long term relationship is a shortage condition for homes in the U.S., particularly in regions that have adopted policies that have largely closed access to meaningful real estate development within them.

[Note: If you follow the links in the above paragraph, you'll find they point to posts at Kevin Erdmann's Idiosyncratic Whisk blog - he will have a book capturing much of his analysis on the topic coming out sometime in 2017!]

As for being in the third phase of that second housing bubble, you can see where we're at in the following chart that illustrates the three main trends for the trailing year average of reported median new home sale prices in the U.S. since July 2012.

Trends in Trailing Twelve Month Average of U.S. Median New Home Sale Prices, 2012-07 thru 2016-10

The initial inflation phase was kicked off by major investors who snapped up properties as fast as they could in the period from July 2012 through July 2013, which saw median new home prices rise at an average rate of $2,476 per month. The second phase came as a number of the investors behind the first phase began seeing less opportunity to realize easy gains, which resulted in their slowing their activities - that phase ran from August 2013 through September 2015, where median new home sale prices in the U.S. rose at an average rate of $1,511 per month.

The third phase began in October 2015 and has continued to the present, which has more closely resembled the kind of transactions that defined the established trends for new home sales in the U.S. before the onset of the first U.S. housing bubble in November 2001. During the third phase, median new home sale prices have been rising at an average rate of $1,034 per month.

With home prices rising by $5.32 for every $1 increase in median household income, this third phase may qualify as a post-bubble trend, although we recognize that this rate of increase is somewhat faster than the $3.60-$4.07 rate that median new home sale prices went up for every $1 increase in median household income in the 32 years from 1967 through 1999.

Finally, we should note that to really be a "bubble", median new home sale prices would have to fall significantly at some point in the future, since a bubble itself has two main phases by definition - an inflation phase where prices escalate and a deflation phase where they collapse. Since July 2012, we've only seen median new home sale prices escalate in what we're calling the second U.S. housing bubble, so whether it is a true bubble has yet to be determined.

References

Sentier Research. Household Income Trends: October 2016. [PDF Document]. 29 November 2016. [Note: We have converted all the older inflation-adjusted values presented in this source to be in terms of their original, nominal values (a.k.a. "current U.S. dollars") for use in our charts, which means that we have a true apples-to-apples basis for pairing this data with the median new home sale price data reported by the U.S. Census Bureau.]

U.S. Census Bureau. Median and Average Sales Prices of New Homes Sold in the United States. [Excel Spreadsheet]. Accessed 29 November 2016.

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November 29, 2016

We're going to ask a question today that has many answers. But before we ask it, consider the following chart, which shows the sales (or revenues) per share of the firms that make up the S&P 500 index for each quarter from 2000-Q1 through a preliminary estimate for 2016-Q3.

S&P 500 Sales per Share, 2000-Q1 through 2016-Q3

Here are some things to consider about the information shown in the chart:

  • The post-recession revenue (sales) per share trends shown are both linear and parallel to each other.
  • Share buybacks by S&P 500 firms since 2009 have reduced the number of outstanding shares over time, which would tend to boost the sales per share figure indicated in the chart above so that it is progressively higher than it would otherwise be if the number of shares for the S&P 500 were held constant.

Now for the question: What changed after 2012 to cause the outcome of stalled-out growth for the sales per share of the firms of the S&P 500?

References

Standard & Poor. S&P 500 Index Earnings. [Excel Spreadsheet]. Accessed 28 November 2016.

National Bureau of Economic Research. U.S. Business Cycle Expansions and Contractions. [Online Document]. Accessed 28 November 2016.

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November 28, 2016

We're catching up with two weeks worth of action in the S&P 500, so let's start with what stood out during Week 3 of November 2016.

First and foremost, our prediction from five weeks ago appears to have held throughout nearly every trading day of the last five weeks, with the S&P 500 actual values consistently falling within 3% of the red-dotted line that we sketched on top of our standard forecasting model to anticipate its future trajectory at that time, as almost indicated by the hand-drawn red-shaded region (since we drew it slightly narrower than intended).

Alternative Futures - S&P 500 - 2016Q4 - Standard Model with Connected Dots Overlay - Snapshot 2016-11-18

In the chart above, our hand drawn forecast applies only to the period where our standard model would be affected by the echo effect from past volatility (indicated as the brown-shaded region), which is an artifact of our model's use of historic stock prices from 1 month, 12 months and 13 months earlier in its projections of future stock prices. To work around the echo effect, we literally connected the dots corresponding to the trajectory associated with 2016-Q4 on both sides of the period we identified over five weeks ago where we anticipated that our model's projections would be less accurate than usual.

Back then, we assumed that investors would keep their forward-looking focus fixed on the very near term future defined by the expectations associated with the current quarter of 2016-Q4, which we predicted would be the case as investors would be greatly influenced by their concerns over the Fed's plans to hike short term interest rates, where that concern would largely trump (pun intended) any noise introduced by the U.S. national elections.

Speaking of which, the outcome of the elections contributed quite a lot of noise, where the actual trajectory of the S&P 500 swung from the low end of our forecast range to the high end. That said, coming out of Week 3 of November 2016, it would appear that investors remained focused on 2016-Q4 in setting current day stock prices.

Here are the headlines that we identified as being relevant to the stock market in Week 3 of November 2016:

Monday, 14 November 2016
Tuesday, 15 November 2016
Wednesday, 16 November 2016
Thursday, 17 November 2016
Friday, 18 November 2016

Now, let's move into Week 4 of November 2016, where something more interesting happened in a Thanksgiving holiday-shortened week.

For investors focusing on the future, the big news of the week was the shift in how far forward investors were looking, which changed from 2016-Q4 to 2017-Q2 during the course of the week.

Alternative Futures - S&P 500 - 2016Q4 - Standard Model - Snapshot 2016-11-25

As for what caused that shift, here are the news items for which we took special note of during Week 4 of November 2016.

Monday, 21 November 2016
Tuesday, 22 November 2016
Wednesday, 23 November 2016
Friday, 25 November 2016

Elsewhere, Barry Ritholtz divided the economic and market news into its positives and negatives for both Week 3 and Week 4 of November 2016.

Looking forward over the next couple of weeks, in the absence of more fundamental events to change expectations or new noise events, we think that the S&P 500 will continue running to the high side of our forecast range for 2017-Q2, which shows the effects of a small echo from late 2015. That said, pay attention to the difference in where stock prices can be expected to go if investors focus on either 2017-Q2 or 2017-Q3. Unless the expectations for the change in the growth rate of future dividends for 2017-Q3 improves significantly, any news that would reasonably delay the expected timing of the Fed's next rate hike out of 2017-Q2 would likely coincide with a significant downward change in U.S. stock prices.

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November 25, 2016

Following the national holiday of Thanksgiving on Thursday, Black Friday represents the busiest shopping day for U.S. consumers, many of whom will take advantage of a second day off work to start their Christmas shopping.

But what if money is tight this year? How can cash strapped Americans stay on a budget while shopping for everyone they're going to give gifts to this year and not run over?

That dilemma then carries down to each individual gift purchase, as our hypothetical shopper must also work how much to spend on a gift for each person they'll give one to this holiday season. Surely there must be a super smart way to work out how much to spend on each gift for each person on their shopping lists before they go out on a Black Friday buying binge!

You're in luck! Brain Candy and Beyond IQ author Garth Sundem shows how to apply math to solve this problem of gift-giving with a budget:

Obviously, that kind of math is hard to work out while on the go, so we're happy to present a tool to help you make your shopping list! Just enter the indicated data below into our generic list below and we'll work out how much you should target spending on each. (For the default amount of spending, we've entered the amount that polling firm Gallup reports that Americans plan to spend on Christmas gifts in 2016, but if that's not what you plan to spend, change it!):

Gift Buying on a Budget
Input Data Amount
Your Christmas Gift Budget
Individual for Whom You're Shopping Their Importance to You
[On a Scale of 1 to 10]
Person A
Person B
Person C
Person D
Person E
Person F
Person G
Person H
Person I
Person J
Your Christmas Shopping Budget per Gift Recipient
Calculated Results Values
Person A
Person B
Person C
Person D
Person E
Person F
Person G
Person H
Person I
Person J

We'll leave it as an exercise to you to identify just who Person A, B, C, etc. are when you print out the list and take it with you while shopping.

But if you're more the type to take advantage of Cyber Monday deals, we can accommodate you there too - here are a number of the Black Friday deals that Amazon is offering in 2016:

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November 24, 2016

The U.S. Consumer Product Safety Commission wants you to cook up safety this holiday season and to that end, they've used U.S. taxpayer dollars to produce the following instructive 3 minute, 42 second-long video demonstrating how to take two potentially dangerous or disastrous situations that you may face while cooking this Thanksgiving and make them worse - one indoor and one outdoor. For connoisseurs of U.S. government-produced safety videos, the frozen turkey is carefully deployed into the fully heated "outdoor" deep fryer at the 52 second mark....

It's Thanksgiving. Let's be careful out there.

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November 23, 2016

Do you remember the effect that the epidemic of avian influenza had upon the U.S.' poultry industry in 2015?

If not, here is a brief recap of how scary things were, especially in the states where most farm-raised turkeys are produced:

Experts say the Avian Flu hits birds hard and fast.

"Sometimes the first symptom you see is dead birds," said Dr. Bill Casto of USDA Veterinary Services. "It'll sweep through a commercial house, and in two hours they can go from all healthy birds to 75% mortality."

Dr. Casto says 5.7 million birds died in Iowa alone.

By the end of summer in 2015, the outbreak of the H5N2 avian flu virus had caused an estimated loss of 50 million chickens and turkeys in the U.S., as it became the "largest animal disease outbreak in the history of North America".

It was so bad that going into November 2015, grocery stores across the nation were warning Americans that their supply of turkeys for Thanksgiving would be at risk of running out before Thanksgiving.

Fortunately, something magical happened that ensured that every American family who wanted to have a turkey for Thanksgiving 2015 would be able to have one. See if you can pick up on what that magical event was in the following passage:

The experts say if you have your heart set on turkey for Thanksgiving, talk to your grocer ahead of time.

"Just check with them," said Roy McCallister of the USDA's Homeland Security Unit. "Make sure they have in stock what you want. The quantity is somewhat reduced but it's not eliminated There's still a quantity of turkeys on the market."

But the price will be higher than usual.

"Yes, as an average, the price of turkey, chicken, even eggs has increased due to the lack of production that has been caused by Avian flu," McCallister said.

The increased price of turkeys communicated to the American consumers who still greatly demanded them just how much more scarce they had become, leading a good number to adapt to the situation by changing the menu for their Thanksgiving holiday dinner to incorporate other, less relatively costly proteins in place of the traditional turkey. At the same time, every American family that was willing to put a higher value on having that traditional turkey at the center of their Thanksgiving dinner instead of those other options could have one just by sacrificing a bit more of what's in their wallets.

The end result of that communication through price tags was sufficient to ensure that the available supply of turkeys in U.S. grocery stores would be enough to fully make it through the Thanksgiving holiday, where one savvy shopper snapped the following picture of a real Black Friday deal!

After Thanksgiving Black Friday Turkey Sale
And thus, 2015 became known as "Almost The Year Without Enough Turkey for Thanksgiving". We're sure it will soon become an animated holiday classic, complete with really catchy musical numbers. Something along these lines!...

Everyone knows you're out of ideas, Hollywood. Call our agent!

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November 22, 2016

As a holiday tradition, Thanksgiving has been around for 395 years. And though the first Thanksgiving celebration likely didn't feature turkey at the center of the dining festivities, in the years since, the idea of serving turkey as the main protein for Thanksgiving dinner has become firmly established.

So much so that 88% of Americans who responded to a survey conducted and reported by the National Turkey Federation reported eating turkey on Thanksgiving.

There may be quite some sampling bias there, seeing as we wouldn't expect the National Turkey Federation to be polling very many non-turkey eaters, but ever since Norman Rockwell immortalized a gigantic turkey being served at an American family's Thanksgiving dinner in 1943, that really is what Americans have come to anticipate being served at their Thanksgiving dinners.

And in 1943, that was quite something, because the price that U.S. turkey farmers would have received for their birds in November of that year was $0.327 per pound, which in terms of 2016 U.S. dollars, means that 1943's Thanksgiving turkey would have cost the equivalent of $4.54 per pound.

Unit Price per Pound for U.S. Farm-Raised Turkeys, October 1909 - September 2016

The story of turkey prices before World War 2 isn't greatly different. In equivalent 2016 U.S. dollars, turkey prices ranged between $3.00 and $4.50 per pound in the two decades from 1909 to 1929, before plummeting by an inflation-adjusted dollar a pound during the Great Depression, before rising in price during the rationing years of World War 2.

What those inflation adjusted prices tell us is that serving turkey at Thanksgiving was an expensive proposition for most American families in the years before, during and immediately after World War 2. In fact, U.S. turkey prices peaked at $0.514 per pound in December 1948, the equivalent of $5.15 per pound in 2016, before something genuinely remarkable happened.

The price of farm-raised U.S. turkeys began to fall dramatically and mostly steadily in the sixty years from 1950 through 2009, to the point where the price of turkeys ranged between $0.41 and $0.81 per pound during the first decade of the 21st century, whether adjusted for inflation to be in terms of 2016 U.S. dollars or not.

Since 2009, the prices for U.S. farm-raised turkeys has trended upward, where as of September 2016, they've clocked in at $0.88 per pound.

Still, after adjusting for inflation, that's about 22% of what they would have cost one hundred years ago! And what that change in cost over time suggests is that dramatically falling prices may be a major contributing factor to why turkey has become the centerpiece of choice for Americans celebrating Thanksgiving - especially in the years since World War 2.

Data Sources

U.S. Department of Agriculture. National Agriculture Statistics Service. Quick Stats: Survey - Animals & Products - Poultry - Turkeys - Price Received, Measured in $/LB - National - US Total - All Years - Monthly. [Online Database]. Accessed 21 November 2016.

U.S. Bureau of Labor Statistics. Consumer Price Index - All Urban Consumers. Series ID: CUUR0000SA0 - 1913-2016. [Online Database]. Accessed 21 November 2016.

U.S. Department of Commerce and Labor. Bulletin of the Bureau of Labor. Wholesale Prices. No. 87. March, 1910. No. 93. March, 1911. No. 99. March, 1912. No. 114. May, 1913. PDF Documents.

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November 21, 2016

It's time once again to celebrate the most American of all holidays, Thanksgiving, which here at Political Calculations means we turn our site over to all things turkey-related during what for most Americans will be a food-filled week of festivities!

And what better way to kick off Thanksgiving Week 2016 than by focusing on what 7 in 10 Americans say must be at the center of the dining tables in order to have a truly traditional Thanksgiving dinner this Thursday, the turkey!

But that bird on this year's table will be a lot different than the ones that may have occupied the middle of American dining tables during the first 340 years of the holiday's history. It will be more than double the size!

In the United States, a male wild turkey will grow to a weight of somewhere between 14 and 17 pounds, while a female turkey will only grow to be 8 to 10 pounds.

And until the early 1960s, turkeys raised on U.S. farms weren't much bigger than that!

A lot has changed in the last 55 years. Today, the average domestic live turkey inspected by the USDA has doubled in weight in the years from 1961 to the present, rising from 15 pounds in 1961 to just over 30 pounds as of 2013, where it has held roughly steady at that level in the years since.

The Growing Domestic Farm-Raised U.S. Turkey, January 1960 - September 2016

That's an amazing increase in the productivity of U.S. turkey producers, which is all the more remarkable in there having been so little change in the preceding 340 years! That kind of increase in the productivity of American farmers is also a big reason for why the cost of a "traditional" Thanksgiving dinner in 2016 will be lower than both last year and also 20 years ago after adjusting for inflation (HT: Mark Perry).

AEI Carpe Diem: Cost of a Classic Thanksgiving Dinner for 10 People, 1986 to 2016

Without the increase in productivity, nearly all food prices would be considerably higher in real terms today, so if you're looking for something to be thankful for this week, there's something to cheer!

Data Source

U.S. Department of Agriculture. All Meat Statistics. Historical. [Excel Spreadsheet]. Updated 27 October 2016.

Previously on Political Calculations

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November 18, 2016

Have you ever needed to cut a square or rectangular hole into drywall? Such as you might need if you needed to install the electrical box for a light switch?

For most people, that's something that would take drilling at least two round holes at the opposite corners of the square or rectangle you intend to cut, large enough to insert the blade of a saw, which you would then proceed to use very carefully and very slowly to cut out a square or rectangular plug from the drywall. When you're done cutting the hole, in addition to a hole in your wall in the rough shape of a square or rectangle and a drywall plug that you might have accidentally dropped into the wall, you might also have tears or broken drywall to repair in addition to a coating of gypsum dust to clean up from yourself, the wall and the floor.

Wouldn't it be cool if you could have just cut a perfectly square or rectangular hole in the wall to do the job more efficiently, more effectively and, let's be honest, better?

Now you can! Inventor Teklemichael Sebhatu has a U.S. patent pending tool to do the job. (Via Core77):

The future has gotten to be a lot cooler than it used to be! And by future, we mean 2017:

The QuadSaw was patented and developed by Genius IP and is set for release in the UK in the summer of 2017. The company is said to have models sized for the electrical boxes used in the US. The tool is expected to sell for £199 (about $220 USD). That's a lot to spend for a hole cutting attachment but could be worth it to the electrician who regularly installs old work (retrofit) boxes in drywall.

Just in case you're looking for ideas of what to get such a person for their birthday or for Christmas next year!

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November 17, 2016

Every three months, we take a snapshot of the expectations for future earnings in the S&P 500 at approximately the midpoint of the current quarter, shortly after most U.S. firms have announced their previous quarter's earnings. Today, we'll confirm that the trailing year earnings for the S&P 500 has indeed rebounded off its bottom, which was set in March 2016.

Forecasts for S&P 500 Trailing Twelve Month Earnings per Share, 2010-2017, Snapshot on 11 November 2016

The rebound in the S&P 500's earnings per share can largely be attributed to one factor: the rebound of oil prices following their bottoming in early February 2016.

Looking forward, we suspect that the forecast improvement in earnings currently indicated by Standard & Poor is in large part predicated on a very robust recovery in the U.S.' oil production sector. However, we suspect that forecast recovery would itself be highly dependent upon oil prices continuing to rise, where S&P is projecting that the energy sector's future earnings per share will double in 2017 over their 2016 levels, leading earnings growth across the entire S&P 500 index.

Data Source

Silverblatt, Howard. S&P Indices Market Attribute Series. S&P 500 Monthly Performance Data. S&P 500 Earnings and Estimate Report. [Excel Spreadsheet]. Last Updated 11 November 2016. Accessed 17 Novbember 2016.

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November 16, 2016

According to international trade data reported by the U.S. Census Bureau, the U.S. economy appears to have unexpectedly slowed after having shown signs of slow but steady improvement in recent months.

That reversal can be seen in the following chart, in which we've tracked the year over year growth rate of the exchange rate adjusted value of goods and services between the U.S. and China from January 1986 through September 2016, the most recent month for which data is available at this writing.

Year Over Year Growth Rate of Exchange Rate Adjusted U.S.-China Trade in Goods and Services, January 1986 - September 2016

The growth rate gives us an indication of the relative health of each nation's economy. A simple reading of whether the growth rate is positive or negative tells us if the value of goods and services imported by the world's largest trading partners can tell us whether the receiving nation's economy is growing or if it is experiencing recessionary conditions. Meanwhile, the trends in the growth rate over time can tell us whether the importing nation's economy is strengthening (rising trend) or weakening (falling trend).

In the chart above, we can see that China's economy is experiencing both positive economic growth and is benefitting from a rising trend, which has largely been in place since December 2015.

Chinese imports to the U.S. economy is telling a different story. After having fallen during much of 2015, the year over year growth rate of China's exports to the U.S. bottomed in negative territory in early 2016 before beginning a slow trend of improvement, which lasted through August 2016.

And then, in September 2016, that appears to have suddenly reversed.

Although just one data point, what makes that single observation such a concern is that the value of goods and services that the United States imports from China follows an annual cycle, which typically peaks in either September or October of each year, which corresponds to the shipment of goods that will support the annual Christmas holiday shopping season. The following chart shows just the value of U.S. imports from China from January 1985 through September 2016.

Value of Goods and Services Imported by the U.S. from China, January 1985 - September 2016

For the U.S. economy, the key thing to recognize is that this recessionary indication developed in 2015 and despite showing some signs of recovery in 2016, it worsened in the months immediately preceding the U.S. election. If that development continued in October 2016, which we'll find out when that data is released next month, it is possible that the apparent sudden slowing U.S. economy in the months before the election could have tipped the election scales in Donald Trump's favor, as Hillary Clinton failed to either recognize or address the nation's developing economic issues during the campaign's final months.

Data Sources

Board of Governors of the Federal Reserve System. China / U.S. Foreign Exchange Rate. G.5 Foreign Exchange Rates. Accessed 3 June 2016.

U.S. Census Bureau. Trade in Goods with China. Accessed 3 June 2016.


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November 15, 2016

The rate at which distressed U.S. firms are announcing dividend cuts has increased significantly over the last two weeks, which suggests that whatever momentum that the U.S. economy had coming out of the third quarter of 2016 is dissipating.

A little over two weeks ago, we reported that the pace of dividend cuts in 2016-Q4 was similar to that of 2016-Q3, at least through the same point of time in the quarter. More significantly, that pace was very close to the threshold where we would consider the private sector of the U.S. economy to be relatively healthy.

That began changing on 31 October 2016, where since that date, a significant increase in the number of distressed firms in the U.S. has taken place, which is something we first noted on Election Day. The following chart, showing the number of cumulative dividend cuts announced in the U.S. by the day of the quarter in 2016 for 2016-Q1, 2016-Q2, 2016-Q3 and 2016-Q4 through 14 November 2016, reveals that the U.S. economy is experiencing recessionary conditions, which are bordering near the threshold that corresponds to some degree of contraction occurring within the economy.

Cumulative Announced Dividend Cuts in U.S. by Day of Quarter in 2016, 2016-Q1 v Q2 v Q3 v Q4, Snapshot on 14 November 2016

Compared to the same point of time in the year ago quarter of 2015-Q4, as shown in the next chart, we see that 2016-Q4 is markedly more negative.

Cumulative Announced Dividend Cuts in U.S. by Day of Quarter, 2015-Q4 v 2016-Q4, Snapshot on 14 November 2016

The important thing to recognize from this data is that the number of dividend cuts that take place during set time period represents a near-real time indication of the relative health of the U.S. economy. As such, it is a slightly lagging indicator, where building levels of distress that has already occurred is finally recognized and communicated to people outside the firms where it has already taken place.

With that being the case, the distress that 2016-Q4's dividend cuts is communicating suggests that its confirmation of a fading U.S. economy was a contributor to the outcome of last week's elections in the U.S.

That distress appears to be more broadly based than in 2015. Through mid-November in 2015-Q4, U.S. firms in seven industries had announced dividend cuts, with those in the oil sector accounting for 44%.

But in 2016-Q4, we find that through 14 November 2016, some 11 industries are represented, with the financial sector leading the way, accounting for nearly one-third of all dividend cuts announced to date. The oil production sector of the U.S. economy is in second place, accounting for just under a quarter of all dividend cuts announced to date.

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November 14, 2016

The second week of November 2016 was everything a bored market watcher could hope for, and more! Unexpected news: check! Massive swings in intraday daily stock price volatility: check! Blown prediction about Week 2 of November 2016: check!

Or was it? You be the judge....

Halfway through our hand-drawn forecasting experiment, the actual trajectory of the S&P 500 is running to the low side of our forecast range, which we think will continue in the absence of a noise event (there's an election in the U.S. sometime next week, right?) or a more fundamental change in the expectations for the future with respect to the S&P 500's dividends.

Well, we certainly got a noise event! Here's how things stand on our alternative futures chart, where four weeks ago, we projected that the closing value of the S&P 500 on each trading day would track along in the red-shaded area indicated on the chart, at least in the absence of a significant noise event or a more fundamental shift if the future expectations of investors.

Alternative Futures - S&P 500 - 2016Q4 - Standard Model with Connected Dots Overlay - Snapshot 2016-11-11

We've got one week to go before our red-shaded forecast expires. Who knows what other noise we might see before the next week is over?

Speaking of which, here are some of the headlines that captured the "fun" of Week 2 of November 2016.

Monday, 7 November 2016
Tuesday, 8 November 2016
Wednesday, 9 November 2016
Thursday, 10 November 2016
Friday, 11 November 2016

As always, Barry Ritholtz divided the week's economic and market news into its positives and negatives.

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November 11, 2016

When we launched this series back in July 2016, we never expected that we would find ourselves crossing into U.S. election analysis, and yet, thanks to the political polls of 2016, here we are!

In today's example of junk science, we're looking at several factors that tick off different boxes on our checklist for how to detect junk science, which include, but at this early date, are not limited to the following items (if you're reading this article on a site that republishes our RSS news feed that doesn't neatly render the following table, please click here to access the version of this article that appears on our site:

How to Distinguish "Good" Science from "Junk" or "Pseudo" Science
Aspect Science Pseudoscience Comments
Inconsistencies Observations or data that are not consistent with current scientific understanding generate intense interest for additional study among scientists. Original observations and data are made accessible to all interested parties to support this effort. Observations of data that are not consistent with established beliefs tend to be ignored or actively suppressed. Original observations and data are often difficult to obtain from pseudoscience practitioners, and is often just anecdotal. Providing access to all available data allows others to independently reproduce and confirm findings. Failing to make all collected data and analysis available for independent review undermines the validity of any claimed finding. Here's a recent example of the misuse of statistics where contradictory data that would have avoided a pseudoscientific conclusion was improperly screened out, which was found after all the data was made available for independent review.
Models Using observations backed by experimental results, scientists create models that may be used to anticipate outcomes in the real world. The success of these models is continually challenged with new observations and their effectiveness in anticipating outcomes is thoroughly documented. Pseudosciences create models to anticipate real world outcomes, but place little emphasis on documenting the forecasting performance of their models, or even in making the methodology used in the models accessible to others. Have you ever noticed how pseudoscience practitioners always seem eager to announce their new predictions or findings, but never like to talk about how many of their previous predictions or findings were confirmed or found to be valid?
Falsifiability Science is a process in which each principle must be tested in the crucible of experience and remains subject to being questioned or rejected at any time. In other words, the principles of a true science are always open to challenge and can logically be shown to be false if not backed by observation and experience. The major principals and tenets of a pseudoscience cannot be tested or challenged in a similar manner and are therefore unlikely to ever be altered or shown to be wrong. Pseudoscience enthusiasts incorrectly take the logical impossibility of disproving a pseudoscientific principle as evidence of its validity. By the same token, that scientific findings may be challenged and rejected based upon new evidence is taken by pseudoscientists as "proof" that real sciences are fundamentally flawed.

Given what happened in the U.S. on Election Day 2016 and what happened earlier in the year on Brexit Vote Day, the one clear message of 2016 that voters are sending is that political polling is badly broken.

One example of how badly is given by FiveThirtyEight's Nate Silver, who presented the following analysis indicating how the 2016 Presidential election in the United States was expected to turn out based on the aggregation of numerous state polls across the United States, which had peaked in favor of candidate Hillary Clinton on the eve of Election Day:


538 - Who will win the presidency? 7 November 2016 22:08

Based on such analysis and its propagation throughout the media, many Americans went into and through Election Day with the firm expectation that Hillary Clinton would soon be officially elected to be the next President of the United States.

As we now know however, that expectation was widely off the mark. And the reason that so many Americans were caught flat footed when reality arrived late on 8 November 2016 is because they erroneously placed too much importance on the results of polling and analysis that was fundamentally flawed and which would never pass scientific muster.

Alex Berezow of the American Council on Science and Health argues that's because political poll analysis like this example lacks even the most basic scientific foundation, where the models behind them cannot be falsified:

Earlier, we published an article explaining why there is no such thing as a scientific poll. In a nutshell, because polling relies on good but sometimes inaccurate assumptions, it is far more art than science. As we noted, "Tweaking [voter] turnout models is more akin to refining a cake recipe than doing a science experiment." Still, since American pollsters are good at their jobs, polls tend to be correct more often than not.

Recently, pollsters and pundits have tried to up their game. No longer content with providing polling data, they now want to try their hand at gambling, as well. It has become fashionable to report a candidate's "chance of winning." (ESPN does this, too. Last week, the network predicted that the Seattle Sounders had a 94% chance to advance to the semi-finals of the MLS Cup. I am grateful this prediction ended up being correct.)

However, these predictions are thoroughly unscientific. Why? Because it is impossible to test the model.

Let's use the soccer match as an example. The only way to know if ESPN's prediction that Seattle had a 94% chance of advancing to the semi-finals is accurate is to have Seattle and its opponent play the match 100 (or more) times. If Seattle advances 94 or so times, then the model has been demonstrated to be reasonably accurate. Of course, soccer doesn't work like that. There was only one game. Yes, the Sounders advanced, so the prediction was technically correct, but a sample size of one cannot test the model.

The exact same logic applies to elections. As of the writing of this article, Nate Silver gives Hillary Clinton an absurdly precise 70.3% chance of winning. (No, not 70.2% or 70.4%, but exactly 70.3%.) If she does indeed win on Election Day, that does not prove the model is correct. For Mr Silver's model to be proven correct, the election would need to be repeated at least 1,000 times, and Mrs Clinton would need to win about 703 times.

Even worse, Mr Silver's model can never be proven wrong. Even if he were to give Mrs Clinton a 99.9% chance of winning, and if she loses, Mr Silver can reply, "We didn't say she had a 100% chance of winning."

Any model that can never be proven right or wrong is, by definition, unscientific. Just like conversations with the late Miss Cleo, such political punditry should come with the disclaimer, "For entertainment purposes only."

Starts With a Bang's Ethan Siegel points his finger at a different problem that such poll-based analysis has that renders their conclusions to be invalid: the inherent inconsistencies from systemic errors in data collection.

A systematic error is an uncertainty or inaccuracy that doesn't improve or go away as you take more data, but a flaw inherent in the way you collect your data.

  • Maybe the people that you polled aren't reflective of the larger voting population. If you ask a sample of people from Staten Island how they’ll vote, that’s different from how people in Manhattan — or Syracuse — are going to vote.
  • Maybe the people that you polled aren't going to turn out to vote in the proportions you expect. If you poll a sample with 40% white people, 20% black people, 30% Hispanic/Latino and 10% Asian-Americans, but your actual voter turnout is 50% white, your poll results will be inherently inaccurate. [This source-of-error applies to any demographic, like age, income or environment (e.g., urban/suburban/rural.)]
  • Or maybe the polling method is inherently unreliable. If 95% of the people who say they’ll vote for Clinton actually do, but 4% vote third-party and 1% vote for Trump, while 100% of those who say they’ll vote for Trump actually do it, that translates into a pro-Trump swing of +3%.

None of this is to say that there’s anything wrong with the polls that were conducted, or with the idea of polling in general. If you want to know what people are thinking, it’s still true that the best way to find out is to ask them. But doing that doesn't guarantee that the responses you get aren't biased or flawed....

I wouldn't go quite as far as Alex Berezow of the American Council on Science and Health does, saying election forecasts and odds of winning are complete nonsense, although he makes some good points. But I will say that it is nonsense to pretend that these systematic errors aren't real. Indeed, this election has demonstrated, quite emphatically, that none of the polling models out there have adequately controlled for them. Unless you understand and quantify your systematics errors — and you can’t do that if you don’t understand how your polling might be biased — election forecasts will suffer from the GIGO problem: garbage in, garbage out.

In economics, these are problems that can affect the contingent valuation method (CVM), which is often used to determine how people value the things for which markets do not exist to trade, such as for the preservation of environmental features like biodiversity. In CVM, surveys (polls) are used to ask people how much they would be willing to pay for that feature, where the collected responses are then used to give an indication of how people view its value. All of the problems of polling exist in contingent valuation, where there can be very big differences between what people might say a thing is worth to them (their stated preference) and the actual choices they make with respect to it (their revealed preference), as might be seen in the differences in the results of a pre-election poll and the results of an actual election.

Economist John Whitehead, who knows his way around the problems of the contingent valuation method, weighs in on the factors that may very well have skewed the results of 2016's political polling:

I can think of one technical reason the polls were wrong. The low response rate polls were subject to sample selection bias. Let's say that only 13% of the population is responds to the survey (13% is the response rate in the Elon University Poll). If the 80% that doesn't respond is similar except for observed characteristics (e.g., gender, age, race, political party) then you can weight the data to better reflect the population. But, if the 87% that doesn't respond is different on some unobservable characteristic (e.g., "lock her up") then weighting won't fix the problem. The researcher would need other information about nonrespondents to correct it (Whitehead, Groothuis and Blomquist, 1993). If you don't have the other information then the problem won't be understood until actual behavior is revealed.

Which is to say that you'll have a lot of people who obsess over the reports of pre-election polling, who might be banking on them in setting their expectations for the future, that will ultimately have their hopes dashed when reality turns out to be very different from their expectations. All because the polls and reporting upon which they relied for their outlook were so inherently flawed that they also had no idea of how disconnected from reality their expectations had become.

In many cities around the United States, and particularly within those regions where people counted on a Clinton victory to retain the benefits of their political party's power over the rest of the nation, that disappointment has sometimes turned into protests, discriminatory threats and outright rioting.

Much of which could have been avoided if Americans had trustworthy political polling results and analysis to more properly ground their expectations. Instead, we're discovering that junk science in political polling and punditry and their role in setting irrational expectations has a real cost in physical injuries and property damage within their own communities.

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November 10, 2016

We most certainly got a very noisy display of fireworks after the U.S. election results came in! The following chart updates the one we last showed after the market close on 7 November 2016, the day before the election....

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Unfortunately, they were mostly the kind of fireworks that you could only see at night, where virtually all of the negative reaction occurred in the overnight futures markets, which created some really powerful buying opportunities as the election losers in the market threw a tantrum as the election outcome became known.

Here are the day's major market-reaction headlines to take you a 150 point swing in the value of the S&P 500:

The S&P 500's thinly traded futures dropped to a low of 2028.15 around midnight on 8 November 2016, then began rebounding. By the end of the regular trading day on 9 November 2016, the S&P 500 had risen to be as high as 2170 before slightly slipping to finally close the trading day at 2163.26.

Why did stock prices boom instead of bust? Economist Scott Sumner paid close attention to the news of which stocks did best in the post-election rally and came up with a hypothesis for why the election's outcome would have mattered so much to the firms behind the day's biggest gains in stock prices.

It seems like a “growth reaction” to me, at least after the initial decline. Investors now expect faster economic growth. Why more growth? It might be tax reform. It might be infrastructure (although monetary offset applies there.) But one thing I heard a lot today (on CNBC) was deregulation. Private prison stocks soared. Coal stocks rose. Bank stocks rose. Biotech soared. Many of these are clearly related to regulation. We often forget how many regs the Obama administration had imposed, and Hillary was promising still more (her talk of drug price controls had been crushing biotech.) Probably small business readers are thinking, “you forgot, I didn’t”.

On a final note, we're not kidding in describing Paul Krugman's words as "now immortal". Much as Irving Fisher was his day's most publicly notable economist, Krugman is most certainly today's. Irving Fisher's immortal quote about stock prices, which he spoke in the days immediately before the Black Friday Stock Market Crash that is considered by many to be the inaugural event of the Great Depression, is:

Stock prices have reached what looks like a permanently high plateau. I do not feel there will be soon if ever a 50 or 60 point break from present levels, such as they have predicted. I expect to see the stock market a good deal higher within a few months.

Markets are different today than they were in 1929. They don't wait anywhere near as long to prove the pronouncements of otherwise brilliant economists to be wrong. It's a big part of what makes them so fascinating to observe!

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