Political Calculations
Unexpectedly Intriguing!
August 31, 2016

A little over a week ago, the Wall Street Journal suggested that the U.S. stock market was especially quiet, which is to say that U.S. stock prices aren't very volatile lately.

Another way to describe that situation is that order in stock prices may finally have returned after having last broken down on 20 August 2015.

So we put that proposition to the test, with the results visually presented below. As best as we can tell, some degree of order returned to the S&P 500 sometime in late March 2016, where we're starting the clock for a new period of order as of the last day of the first quarter of 2016, 31 March 2016.

S&P 500 Index Value vs Trailing Year Dividends per Share, 30 September 2015 through 26 August 2016

Since then, we've seen one outlier affecting the overall trend, which corresponds to the reaction of global stock markets to the outcome of the Brexit referendum, in which voters in the United Kingdom directed that nation's politicians to begin exiting from the European Union.

Other than that outlier, which the U.S. stock prices recovered from quickly, we can confirm that there has indeed been very little in the way of volatility in the U.S. stock market. Additional credit may also be given to the rise of oil prices since mid-February 2016, which has boosted the business prospects of the U.S. oil industry, and also the apparent stabilization of China's economy, which both had been major contributors to chaos in the U.S. stock market during much of the latter portion of 2015 and early 2016.

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August 30, 2016

Four years ago, in July 2012, an influx of investors in the U.S. real estate market began buying up homes at an accelerated rate, which prompted a sharp rise in median new home sale prices, which lasted for a year.

During that time, the trailing twelve month average of median new home sale prices rose at a rate of $2,476 per month. After July 2013 however that phase of rapid inflation in median new home sale prices came to an end and the trailing year average of median new home sale prices then began escalating at the slower rate of $1,511 per month as the investor activity that had fueled the previous escalation in prices began to abate. That amount is about 39% slower than what was recorded in the year from July 2012 through July 2013.

Going by data that has been revised over the past nine months, that appears to have generally continued through September 2015. Since then, it now appears the the pace of escalation of the trailing twelve month average of median new home sale prices has slowed again, this time to about $934 per month. Coincidentally, that figure is about 38% slower than the typical pace that was seen between July 2013 and September 2015.

Trends in Trailing Twelve Month Average of Median U.S. New Home Sale Prices, July 2012 through July 2016

If you look closely at the trailing year average for median new home sale prices, you'll see that the transition to the more recent deceleration isn't as cleanly defined as the prior one. We can make an argument that the newest trend may have started to take hold back in April 2015, where a significant step upward in September 2015's sale prices might well represent the dying gasp of the previous trend as the newer trend became dominant.

Since the most recent four months of data is still subject to revision, it's too early to tell if that similarity in the rate at which the median new home sale prices stepped down from faster growth to slower growth will be sustained, but it certainly is interesting to catch it in this snapshot in time!

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August 29, 2016

For a market that hasn't experienced much in the way of volatility lately, Friday, 26 August 2016 was a welcome departure for observers. Unfortunately, if you only tracked the closing price of the S&P 500 from day to day, you wouldn't have noticed!

To see what we mean, let's flip our usual presentation of charts around and show what our modified model of the alternative future for S&P 500 stock prices projected through Week 4 of August 2016:

Alternative Futures - S&P 500 - 2016Q3 - Modified Model 01 - Snapshot 2016-08-26

From the beginning of the week to the end of the week, it would appear that investors kept their forward-looking focus on 2017-Q2 in setting the level of the S&P 500 throughout the week, well within the typical range of volatility we would expect. More to the point however, there was very little movement in the level of the S&P 500 from one day's closing value to the next all throughout the week.

So far, our modified model for projecting future stock prices is providing a more accurate forecast than our standard model, whose projections have been skewed by the echoes of past volatility from last year's market reaction to the meltdown of China's stock markets.

Alternative Futures - S&P 500 - 2016Q3 - Standard Model 01 - Snapshot 2016-08-26

All that said, let's focus on the headlines that influenced the week's market action.

Monday, 22 August 2016
Tuesday, 23 August 2016
Wednesday, 24 August 2016
Thursday, 25 August 2016
Friday, 26 August 2016
  • Futures flat as investors count down to Yellen speech - this is Reuters' headline before the market opened, before Fed Chief Yellen spoke at Jackson Hole. The S&P 500 would go on to open at 2,175.10, some 2.63 points higher than it closed the day before.
  • Bullard agnostic on timing of Fed hike; cites tech stocks - note the overall neutral tone, although St. Louis Fed president Bullard is concerned that stock prices are valued too high. This is how a Fed official can say something without necessarily affecting the market.
  • Fed's Yellen says case for interest rate hike has strengthened - and yet, stock prices responded as if investors were reassured that any such action would not take place at any time before 2017, which prompted stock prices to rise. This reaction helps illustrates why we place such emphasis on things said by Fed officials - they have the ability to affect stock prices by shifting the forward-looking focus of investors, either forwards or backwards. In this case, Yellen's comments refocused them toward a more distant future. The S&P 500 would peak at a high of 2,187.94 for the day.
  • Wall Street declines after Fischer's hawkish stance on rates - And then the Fed's #2 weighed in, reeling the forward-looking focus of investors back toward the nearer term future. Along the way, stock prices got whipsawed from positive to negative territory, just as we should expect would happen! The S&P 500 dipped to a low of 2,160.39.
  • Wall Street slips in wake of comments by top Fed officials - and yet, by the end of the day, the market closed just slightly below where it opened, with the S&P 500 down 3.43 points to finish at 2,169.04.

Here's a screenshot of Friday's Fed official-inspired volatility for the S&P 500:

S&P 500 on Friday, 26 August 2016

You can see the market's reaction to Janet Yellen's comments from Jackson Hole, Wyoming at and after 10:00 AM, while the reaction to Stanley Fischer's comments came as he spoke later that morning, at and after 11:30 AM. As a general rule of thumb, it typically takes some 2-4 minutes for the market's reaction to news it wasn't expecting to become noticeable as a change in trajectory for stock prices. For further reading, ZeroHedge has an interesting take on the markets' real time reactions to the major Fed officials' Jackson Hole speeches, which touches on why they responded bullishly to Yellen's seemingly otherwise mildly hawkish comments (a hint of more quantitative easing monetary policy!), as well as an overall post-mortem.

Meanwhile, Barry Ritholtz has succinctly summarized the positives and negatives for the week's market and economic news, which we recommend surveying to get a broader sense of the general investing climate that existed during the week.

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August 26, 2016

Several weeks ago, Political Calculations launched a new series, "Examples of Junk Science", where we are specifically focusing on where flawed analysis and the misuse of data leads to the spread of false or misleading information in the fields of investing, finance and economics.

More than that, we're also seeking to present explanations for why the examples of flawed analysis and misuse of data that we present lead to results that are not valid, which we hope will be beneficial for analysts who seek to make positive contributions in these fields, where the ultimate goal is be to improve the general quality of analysis that is produced altogether. We hope to do that in part by highlighting real examples so you can more quickly recognize them as the pseudoscientific junk they are so you can directly challenge the people who propagate them.

Today's example of junk science is representative of what may quite possibly be the most common way that analysis is twisted to present a misleading picture of the economy. Coincidentally, it is also perhaps the simplest: stating changes in values as a percentage of a percentage.

This is a technique that can greatly exaggerate the significance of a claim, which often will fall apart when put into a more appropriate and relevant context. As such, this kind of data mishandling falls under the precision category of the checklist for how to detect junk science.

How to Distinguish "Good" Science from "Junk" or "Pseudo" Science
Aspect Science Pseudoscience Comments
Precision If numbers are presented in support of a scientific explanation, they must be stated with the precision and accuracy required by their level of significance as determined by known measurement error in the data from which are derived, neither more nor less. Pseudoscience practitioners will often present numbers with a level of precision and accuracy that exceeds that supported by the known accuracy of real world data in order to give the appearance of greater validity for their claims. A recent example of pseudoscientific deception by precision include certain economists suggesting that "a Keynesian multiplier of 1.57" specifically applies for government stimulus spending, when a wide range of studies suggest the actual multiplier may be "anywhere from 0 to 1.5" (note the difference in the number of decimal places and potential range of values!)

Today's example isn't so much about decimal places as it is representative of the kind of deception that results when precisely calculated values are placed into an inappropriate context. Let's go over the details....


A chart similar to the one below was included in a blog post under the heading “Bank C&I Loan Charge-Offs Soaring Again”. This chart caught my attention because it seems to indicate that bank C&I (Commercial and Industrial) loan charge-offs are happening at one of the fastest rates of the past 30 years — the sort of rate that would be consistent with the US economy being in recession.

CI_YOYpercent_190716

The problem is that the above chart shows the percentage change of a percentage, which opens up the possibility that what is in reality a small increase is being made to look like a large increase. For example, an increase from 1% to 2% over the course of a year in the proportion of loans charged-off would be a 100% increase if expressed as a year-over-year percentage change in the percentage of charge-offs, whereas all you’ve actually got is a 1% increase in the total proportion of loans that have been charged-off.

The next chart is based on exactly the same data, but instead of displaying the year-over-year percent change in the percentage of C&I loans that have been charged off it simply displays the percentage of C&I loans that have been charged off. This is not just a more correct way of looking at the data, it is a way that has not given any false recession signals over the past 30 years.

CI_percent_190716

The first chart’s message is: an economic recession is either in progress or imminent. The second chart’s message is: the US economy is not in recession and is presently not close to entering recession.

The same data, opposite messages.

No matter how you slice it, the presentation of a percentage change of a percentage is always highly misleading, even if mathematically correct. At the very least, it is more than one step removed from the actual numbers used to calculate the original percentage, which is the appropriate context in which the percentage can be understood.

Believe it or not, the use of percentage changes of percentages in place of the more accurate and direct presentation of percentage changes of numbers can have legal consequences, as Republican members of Arizona's state legislature discovered in 2008.

Judge orders rewrite of sales tax analysis

PHOENIX — A state judge ordered late Friday that the description of a proposed tax increase for transportation be rewritten to exclude a calculation of how much the levy will increase.

The description is to be put into pamphlets mailed to the home of every registered voter.

Maricopa County Superior Court Judge Edward Burke said it may be "mathematically correct" to say that Proposition 203, which would boost state sales taxes from 5.6 cents on every dollar spent to 6.6 cents, equals a 17.8 percent increase in what people will pay in sales taxes.

But the judge said such calculations "are likely to mislead many voters."

He accepted the arguments by Paul Eckstein, the attorney for tax backers, that the figure was deliberately inserted by Republican lawmakers to convince voters to reject the levy.

Mike Braun, who represents the Legislative Council, told Burke the law requires the panel to not only explain each ballot measure but also how approval would affect existing law.

He said it would be "helpful" to tell voters that whatever they are paying in state sales taxes would increase by 17.8 percent if Proposition 203 is adopted.

Eckstein, however, told the judge the accuracy of the calculation is beside the point.

"An analysis can be 100 percent accurate, and it can be unfair and misleading," he said.

Burke agreed.

For example, he said, raising the tax rate from one cent per dollar to two cents has an "absolute percentage increase" of 1 percentage point. But it has a "relative percentage increase" of 100 percent.

"Many voters are likely to confuse relative and absolute percent increases," the judge wrote.

As you can see, the components for the state sales tax example has a direct parallel component in the loan charge-offs example. The problem, as described by the judge in this case in non-mathematical terms, comes when the absolute percentages represented by the math above, the percentages of numbers, are confused with relative percentages, the percentage changes of percentages. The presentation of the relative percentages is misleading because their calculation is too many steps removed from the original base values from which the original percentages were calculated, which breaks the link to the specific context to which they are relevant.

In the case of today's example of junk science, it's the difference between suggesting that the risk of recession is imminent or of there being a comparatively low risk of recession in the near term. Given how different those outcomes are, expressing calculated values in the correct context is essential to avoiding the consequences that would come from taking the wrong actions in response to misleading information.

References

Saville, Steve. You can make statistics say whatever you want. The Speculative Investor. 19 July 2016. Republished with permission.

Political Calculations. How To Detect Junk Science. [Online Article]. 19 August 2009.

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August 25, 2016

With so much destructive flooding in Louisiana during the past week, we couldn't help but think back to this past June, when the region around Houston, Texas experienced a tremendous amount of rainfall that also led to significant flooding and lots of property damage.

But one resident in the town of Rosharon, Texas beat the flood, using some remarkable technology developed in Louisiana for the purpose of preventing property damage. The following video tells the tale.

At $8,300, the cost of the residential AquaDam may seem high, but for those who live in flood prone areas, where flooding can cause property damage that can exceed several hundred thousand dollars, it may be a pretty reasonable investment. And the best part is that with the right combination of pumps, homeowners could actually use the same water that might otherwise destroy the value of their homes to protect them.

The question of whether its worth the cost however comes down to a combination of how much risk there is of property damaging flooding and the impact of how costly it could be. To estimate if it is worth the cost, as a general rule of thumb, you can multiply the cost of potential property damage by the probability that flooding will damage the property over an extended period of time. If that number is greater than or equal to the cost of using technology like the Aqua Dam to mitigate against the potential for damage, then it makes sense to invest in the technological solution.

It's not quite that cut and dried however, because there is a lot to be said for peace of mind if that number only falls short by a relatively small percentage, where choosing to invest in technology to overcome such a risk would come down to a personal choice.

The same principles apply for adaptation to climate change, where one could argue that this kind of technology could be more cost-effectively applied to provide additional protection to a levee, which if overtopped by flood waters, could easily result in millions of dollars of damage to an entire community. That may be especially true when the alternative course of action may involve an even more costly project to build up the levee.

Then again, the cost of the kind of technology to deal with such hazards may be so prohibitive that property owners may simply choose to buy extra insurance protection to pay for what it costs to clean up and recover after the damage happens, if and when it does.

And that's the rub. It all comes down to placing a bet on the statistical likelihood of something bad happening, which may or may not when it matters most to you. How much are you willing to wager to win on a choice like that? And how much are you willing to risk losing if you fall on that other side of the bet?

Update: Matt Kahn applies a little more complex math than what we described above to identify what kinds of people would choose to live in areas prone to flooding in the absence of government subsidized insurance protection (and by extension, since the math would be similar, technological protection).

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