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September 26, 2016

The third full week of September 2016 saw the month's second Lévy flight event, which began in the aftermath of the Federal Reserve's FOMC meeting on Wednesday, 21 September 2016. We can see the result of that event, which coincides with investors appearing to have shifted their forward-looking focus from 2017-Q1 back out to the more distant future quarter of 2017-Q2 in the following chart.

Alternative Futures - S&P 500 - 2016Q3 - Modified Model 01 - Snapshot on 2016-09-24

That shift occurred after the Fed indicated that it would once again hold off on increasing short term interest rates in the U.S., where investors had pulled in their forward-looking attention to the nearer term a little over two weeks earlier, as several Fed officials suggested they would support hiking those rates at the Fed's September meeting.

We don't think that investors were focused so much on 2017-Q1 as much as they were roughly evenly splitting their focus between the very near term quarter of 2016-Q3 and the distant future quarter of 2017-Q2. With the Fed's confirmation that they would not be hiking U.S. short term interest rates in 2016-Q3, investors quickly refocused their attention on the more distant future of 2017-Q2, with stock prices changing to be consistent with the expectations associated with that future quarter.

On a separate note, this upcoming week will see the end of our use of the modified futures-based model we developed back in Week 3 of July 2016 to address the effect of the echo of historic stock price volatility upon our standard futures-based model of how stock prices work, which we knew in advance would affect the accuracy of the model's forecasts during 2016-Q3.

We have enough data now to confirm that our modified model was much more accurate than our standard model during the past several weeks in projecting the future trajectories that the S&P 500 would be likely to follow given how far forward in time investors were collectively looking in making their current day investment decisions. The following chart shows how the echo of the extreme volatility that was observed a year earlier affected our standard model's projections.

Alternative Futures - S&P 500 - 2016Q3 - Standard Model - Snapshot on 2016-09-24

Although we show the echo effect from the previous year's volatility ending after 28 September 2016 in these charts, that may be somewhat misleading, as we're really entering a period which is reflecting the echo of a relatively short interlude in the greater stock price volatility that defined the latter half of 2015. As such, we expect that the trajectory of the S&P 500 will deviate from our standard model's projections during the next two weeks, but not as greatly as it did during the last several weeks.

We will have another prolonged opportunity to test drive our modified model in 2016-Q4.

Meanwhile, here are the headlines that caught our attention as newsworthy from their market-moving potential during Week 3 of September 2016.

Monday, 19 September 2016
Tuesday, 20 September 2016
Wednesday, 21 September 2016
Thursday, 22 September 2016
Friday, 23 September 2016

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

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September 23, 2016
Source: National Science Foundation - https://www.nsf.gov/news/special_reports/science_nation/blindsight.jsp

Can different parts of the human brain be reprogrammed to do tasks that are very different from those they would seem designed to do?

That's an intriguing possibility that has been raised by the findings of recent research at Johns Hopkins, which found some very remarkable differences in the way that the region of the human brain dedicated to processing visual information works between sighted and blind individuals. Their findings suggest the possibility that what would appear to be very specialized areas of the brain can indeed be trained to perform other tasks.

To find out, the researchers ran functional MRIs of 36 individuals, 17 who had been blind since birth and 19 who had been born able to see, who might be considered to be the control group in their experiment. What they wanted to test is whether there would be significant differences in how the visual cortex region of the brain might process non-sensory information between blind and sighted people.

Why non-sensory information? Because earlier experiments had indicated that the visual cortex region of the brain can reprogram itself to process information from other senses, like hearing and touch, one of the researchers, Marina Bedny, wanted to find out if that region could be retasked to do something entirely unrelated to its seemingly predefined purpose. Something far removed from anything to do with the senses and processing sensory information.

So she picked algebra.

We're pretty sure that many people, particularly algebra students, would agree that algebra is about as far removed from the human sensory experience as one can get! But as an an exercise in abstract, structured logic, it represents a skill that must be learned rather than an inherent ability, which made it an ideal mental process for which to monitor brain activity in the researchers' experiment.

During the experiment, both blind and sighted participants were asked to solve algebra problems. "So they would hear something like: 12 minus 3 equals x, and 4 minus 2 equals x," Bedny says. "And they'd have to say whether x had the same value in those two equations."

In both blind and sighted people, two brain areas associated with number processing became active. But only blind participants had increased activity in areas usually reserved for vision.

The researchers also found that the brain activity in the visual cortex of blind individuals would increase along with the increasing difficulty of the algebra problems each were asked to solve.

And that's where the promise of the researchers' findings lies:

The result suggests the brain can rewire visual cortex to do just about anything, Bedny says. And if that's true, she says, it could lead to new treatments for people who've had a stroke or other injury that has damaged one part of the brain.

If the John Hopkins researchers' findings hold, it suggests that better treatments for brain damage related to strokes, concussions and other injuries can be developed, which until this research finding, would not have been considered to have as good a potential prognosis for recovery based on our previous understanding of how the brain works.

There's a lot of science that has to happen between now and that more promising future, but that future has just gotten a lot cooler than it used to be!


Kanjlia, Shipra, Lane, Connor, Feigenson, Lisa and Bedny, Marina. Absence of visual experience modifies the neural basis of numerical thinking. Proceedings of the National Academy of Sciences of the United States of America. Early Edition, 19 September 2016. [Online article]. 28 July 2016.

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September 22, 2016

What really separates science from pseudoscience?

At its core, the main difference between a legitimate scientific endeavor and a pseudoscientific one comes down to the goals of the people engaged in each activity. Our checklist for how to detect junk science in fact leads with that category.

How to Distinguish "Good" Science from "Junk" or "Pseudo" Science
Aspect Science Pseudoscience Comments
Goals The primary goal of science is to achieve a more complete and more unified understanding of the physical world. Pseudosciences are more likely to be driven by ideological, cultural or commercial (money-making) goals. Some examples of pseudosciences include: astrology, UFOlogy, Creation Science and aspects of legitimate fields, such as climate science, nutrition, etc.

Today's example of junk science shows the kind of innumeracy that can result when a nonprofit organisation concerned with where the threshold for poverty should be drawn puts its ideological, cultural and commercial goals ahead of objectively verifiable reality. Let's plunge straight in, shall we?

We've kept something of a wary eye on the Joseph Rowntree Foundation's ruminations on poverty over the years. Near a decade back they started that idea of the living wage. Ask people what people should be able to do if they're not in poverty. Along the lines of Adam Smith's linen shirt example. So far so good - but it was a measure of what is it that, by the standards of this time and place, people should be able to do and not be considered to be in poverty. 

In this latest report of theirs they are saying that if the average family isn't on the verge of paying 40% income tax then they're in poverty.

This is not, we submit, a useful or relevant measure of poverty. But it is the one that they are using. Here is their definition:

In 2008, JRF published the Minimum Income Standard (MIS) – the benchmark of minimum needs based on what goods and services members of the public think are required for an adequate standard of living. This includes food, clothes and shelter; it also includes what we need in order to have the opportunities and choices necessary to participate in society. Updated annually, MIS includes the cost of meeting needs including food, clothing, household bills, transport, and social and cultural participation.

JRF uses 75% of MIS as an indicator of poverty. People with incomes below this level face a particularly high risk of deprivation. A household with income below 75% of MIS is typically more than four times as likely to be deprived as someone at 100% of MIS or above. In 2016, a couple with two children (one pre-school and one primary school age) would need £422 per week to achieve what the public considers to be the Minimum Income Standard, after housing and childcare costs. A single working-age person would need £178 per week

Having an income that is just 75% of these amounts –£317 for the couple and £134 for the single person – is an indication that a household’s resources are highly likely not to meet their needs. The further their incomes fall, the more harmful their situation is likely to be.

So that's £16,500 a year for the high risk of deprivation and £22,000 a year for poverty. But note (this for the average family, two plus two) that this is disposable income after housing and childcare costs. We must add those back in to get the other definition of disposable income, the one that ONS uses.

Average rent is £816 a month, average childcare costs are £6,000 a child a year.

That's therefore £38,500 a year or £44,000 a year in actual consumption possibilities for such a family. That higher number marks poverty, the lower potential deprivation.

As ONS tells us (and this is disposable income, after tax and benefits, before housing and childcare) median household income in the UK is:

The provisional estimate of median household disposable income for 2014/15 is £25,600. This is £1,500 higher than its recent low in 2012/13, after accounting for inflation and household composition, and at a similar level to its pre-downturn value (£25,400)

And do note that ONS definition:

Disposable income:

Disposable income is the amount of money that households have available for spending and saving after direct taxes (such as income tax and council tax) have been accounted for. It includes earnings from employment, private pensions and investments as well as cash benefits provided by the state.

The JRF is using a definition of poverty that is higher than median income for the country. This is insane. It gets worse too. The band for higher rate income tax starts at £43,000. The JRF is defining a two parent, two child, household beginning to pay higher rate income tax as being in poverty.

To put this another way, a British family in the top 0.23% of the global income distribution for  an individual is in poverty and one in the top 0.32% of that global distribution is deprived. Yes, after adjusting for price differences across countries.

The British population is some 1% of the global population. It's not actually possible for us all to be in anything more than the top 1% of the global income distribution.

This simply is not a valid manner of trying to define poverty.

Using a slightly different measure of the global income distribution, the following chart puts several of these figures into some perspective.

World Individual Income Distribution (2013), with UK Median Income and JRF Implied Poverty Threshold for UK

We see that even though half of the UK's population finds itself in the Top 1.5% of the global income distribution just based on their disposable income, many would still be considered to be in poverty according to the JRF's unique definition of that condition. The JRF is however pressing forward with their absurd definition because they appear to believe that doing so will advance their ideological, cultural and commercial policy goals further than what they would be able to achieve if they were to instead anchor their definition of the UK poverty threshold more credibly as 60% of median income.

And so we find that their pursuit of their political agenda on that basis is really advancing pseudoscience. And vice versa, since it would appear to be a mutualistic relationship.


Hellebrandt, Tomas, Kirkegaard, Jacob Funk, Lardy, Nicholas R., Lawrence, Robert Z., Mauro, Paolo, Merler, Silvia, Miner, Sean, Schott, Jeffrey J. and Veron, Nicolas. Transformation: Lessons, Impact, and the Path Forward. Peterson Institute for International Economics Briefing. [PDF Document]. 9 September 2015.

Jones, Rupert. Average monthly rent hits record high of £816, highlighting housing shortage. The Guardian. [Online Article]. 15 October 2015.

Joseph Rowntree Foundation. We Can Solve Poverty in the UK: A Strategy for governments, businesses, communities and citizens. [PDF Document]. 6 September 2016.

Mack, Joanna. Definitions of Poverty: Income threshold approach. [Online Document]. 21 January 2016.

Organisation for Economic Co-Operation and Development (OECD). OECD.Stat. Purchasing Power Parities for GDP and related indicators. [Online Database]. Accessed 17 September 2016.

PokeLondon. Global Rich List. [Online Application]. Accessed 7 September 2016.

Rutter, Jill. Family and Childcare Trust. Childcare Costs Survey 2015. [PDF Document]. 18 February 2015.

United Kingdom Income Tax rates and Personal Allowances. [Online Document]. Accessed 7 September 2016.

United Kingdom Office for National Statistics. Statistical bulletin: Nowcasting household income in the UK: Financial Year Ending 2015. [Online Document]. 28 October 2015.

Worstall, Tim. The Insanity of the JRF's Latest Poverty Campaign. Adam Smith Institute. [Online Article]. 7 September 2016. Republished with permission.

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September 21, 2016
Unofficially speaking, as measured by the number of domestic firms listed on U.S. stock exchanges, the U.S. stock market has shrunk to the lowest level on record in data that extends back to 1975.

Number of Listed Firms in U.S. Stock Market, 1975-2016 (Excluding Investment Funds and Trusts)

In the chart above, we've overlaid the timing of the official periods of recession for the U.S. economy and also the entire span of the Dot-Com Stock Market Bubble, both of which are things that you would think would affect the number of publicly-traded firms in the U.S., but which turn out to not be so important.

Recessions, for example, are things that you would think would coincide with falling numbers of firms in the U.S. stock market, but for the five recessions shown in the chart, three occur when the number of listed firms was rising, while the other two just happen to fall in the middle of longer term declines in the number of publicly-traded companies in the U.S.

Meanwhile, you might reasonably think that the inflation phase of the Dot Com Bubble from April 1997 through August 2000 would coincide with a rising number of U.S. firms listed on U.S. stock exchanges, but instead we see that the number of listed firms peaked more than a year before the Dot Com Bubble even began to seriously inflate, where the number of public firms in the U.S. has typically fallen in each year since its peak in 1995-1996.

More remarkably, the rate at which U.S. firms delisted between the end of 1996 through 2003 was steady, spanning both the inflation and deflation phases of the Dot Com Bubble. It's only after 2003 that the rate slowed, before falling more rapidly again in the period from 2008 through 2012.

After 2012, we have a break in the data series we used to construct the chart, which may account for the apparent increase in listed firms from 2012 to 2013. However, perhaps the more important takeaway is that the decline in the number of listed U.S. firms has continued from 2013 through the present, where it has now reached the lowest level on record for all the years for which we have this kind of data.

Data Sources

Droidge, Craig, Karolyi, G. Andrew and Stulz, Rene M. The U.S. Listing Gap. Charles A. Dice Center for Research in Financial Economics. Dice Center WP 2015-07. Table 5. Listing counts, new lists, and delists. U.S. common stocks and firms listed on AMEX, NASDAQ, or NYSE, excluding investment funds and trusts. [PDF Document]. May 2015.

Wilshire Associates. Wilshire Broad Market Indexes, Wilshire 5000 Total Market Index Fundamental Characteristics. [2013, 2014, 2015 (for month ending 06/30/2015), 2016 (for month ending 06/30/2016)].


September 20, 2016

Two weeks ago, we noticed an unusual trend for the number of dividend increases announced by U.S. firms in August 2016, where we saw that it was falling as compared to the year over year data.

What makes that trend unusual is that it came at the same time that we saw an improvement in the trend for the number of U.S firms declaring that they would be cutting their dividends in August 2016 compared that announced dividend cuts a year earlier.

That means that the two trends are contradicting each other. For dividend increases, a falling number of firms announcing dividend hikes represents a negative situation, but for dividend cuts, a falling number of firms accouncing that they will reduce their dividends is a positive development.

Because of that contradiction, we thought it might be worthwhile to keep closer track on how the trend for dividend increases progressed throughout the month of September 2016, where we would compare that progression with what happened a year earlier, in the month of September 2015. The following chart shows what we find when we sampled the dividend increase data from our real-time news sources for them.

Cumulative Number of Dividend Increases Announced During Month, September 2016 vs September 2015

Through the 19th day of September 2016, we find that the pace of dividend hikes is generally keeping pace with the rate at which dividend increases were announced a year ago, and in fact, has tended to be slightly ahead of that pace.

However, we have to recognize that isn't as clear a signal of strength as we would like, since much of that difference might be attributed to the relative timing of weekends and holidays between September 2015 and September 2016.

Perhaps we'll get a clear break in one direction or the other in these last two weeks of the month to more clearly indicate if September 2016 will continue the unusual development we saw for dividend hikes in August 2016. If you would like to keep track on your own, our data sources are linked below.

Data Sources

Seeking Alpha Market Currents Dividend News. [Online Database]. Accessed 19 September 2016.

Wall Street Journal. Dividend Declarations. [Online Database]. Accessed 19 September 2016.


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