Political Calculations
Unexpectedly Intriguing!
February 12, 2016

Have you ever bought something that you thought would last, then had to replace it far sooner than you believe you ever should have had to?

Sure, maybe you deliberately bought something that was cheap just because it was cheap, and because you figured you would replace it with a higher quality, more durable version once you became a grown up and could afford to pay more to get something better. But what if you had to deal with that better item falling apart or wearing out long before it ought to have?

Core77's Rain Noe vents his frustration before finding a solution:

I am so sick of the fact that we must constantly buy things, throw them out and buy new ones. I can't stand the appliance that breaks, the cheaply-made tool that fails, the object that's suddenly rendered entirely useless because one small plastic irreplaceable hinge has failed.

Tara Button is sick of it, too, and resolved to do something about it. Button ditched her career in advertising to start Buy Me Once, an online retailer that searches far and wide to find manufacturers who actually build things that were made to last.

In the following video, Button explains what the site is about:

We think it's a cool idea, and also long overdue. And we have to say that we didn't think that we would ever have seen socks with a lifetime guarantee!

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February 11, 2016
Juggling Risk - Source: State of Connecticut - http://www.osc.ct.gov/empret/defcomp/brochure/guidelines.htm

Last week, we offered a challenge for our readers at Seeking Alpha: "How Would You Exploit the Future for the S&P 500 If You Knew It?"

In that challenge, we presented a scenario for investors that actually took place in the last week of January 2016, which were based on the series of "If-Then" statements we had outlined a week earlier. After discussing those statements, we laid down the challenge.

Getting back to what we really want to get at, do you see how all these changes in the value of the S&P 500 were specifically covered in our If-Then conditions representing how stock prices would be most likely to behave during the fourth week of 2016?

And since they were, the question of how you as an investor could have taken advantage of the foreknowledge of how stock prices would behave under the circumstances we described, but not the knowledge of the exact timing of when the changes in stock prices we described would take place, is a very open question. One where the strategies you might have used in the fourth week of January 2016 would be very similar to what you might do in future weeks when similar circumstances come back into play.

So what would you have done with your investments to maximize your returns given these circumstances (and the benefit of 20-20 hindsight)? If you're reading this article on Seeking Alpha, we'll monitor the responses addressing that question in the comments over the next week and will share the more interesting strategies that are put forward through that venue in our next discussion of our alternative futures model for the S&P 500.

It's been just over a week since our challenge appeared at Seeking Alpha, and we were very pleasantly surprised by the quality of the comments that were provided.

In particular, ghiblinewt picked up on the degree of difficulty in the challenge from a conventional investing perspective:

As I understand it, knowing the market reaction to a set of events which may or may not eventuate is near meaningless, unless you also happen to know that will occur. Perhaps I'm missing the real question being posed here, but the If-Then proposal gives you no real advantage unless you know that the antecedent will occur.

If you know that one of two (n) possible outcomes will definitely occur however then you could decompose the index space into a bi- (n)-nomial tree and then weighted sum over the multiple paths to arrive at some more probable paths.

But I would suggest that attempting to guess market reaction to specific events is very much a losing game- since the market is already continuously integrating these weightings into its prices- I just don't see that as exploitable in any way. the single most significant piece of information you have about the market is where its just been, of course this means that its not IID, but that's pretty well established.

According to conventional finance theory (think Bachelier, Samuelson, Fama, Ross, Tobin and Shiller), ghiblinewt is absolutely correct. However, our futures-based model extracts more information about the likely future for stock prices than any of these market hypothesists were able to consider in their day, opening up the possibility of being able to exploit the additional information to gain greater returns than would otherwise be possible. Here's how we fleshed out that greater potential and re-expressed the challenge (emphasis and links added, spelling errors corrected):

There's an important bit of information to consider with the If-Then conditions as well - where stock prices are currently, with respect to where our futures-based model indicates they would be based upon how far ahead in the future investors are looking.

For example, there are times when we absolutely know that investors are going to shift their attention to a different point of time in the future, which most often happens when investors are focused on the current quarter (the end of which is still in the future), but the clock for which is ticking down, because there is only so much of the current quarter left to play out before investors *have* to shift their focus to a more distant point of time in the future. The classic example from recent years is 2012-Q4 thanks to what we call the "Great Dividend Raid" ahead of the Fiscal Cliff crisis.

Then, there are most other times where investors have to make some kind of determination of how likely an event is (such as the future timing of Fed rate hikes). You're right in assessing that the market is continually doing that, but here, we're seeing in our model that Fed meetings often coincide with investors focusing on a specific future point in time (the time at which the Fed has indicated it plans to next change interest rates), which provides a basis for setting up an investment strategy based on the likelihood of alternative outcomes.

We'll interject here to note that we have come to use Fed meetings and announcements where investors are clearly and nearly universally focused on one particular point of time in the future as calibration events - to check out well our model is providing feedback on the future outlook of investors. It's something we have to do because the scale factor in the basic math behind our model has to be determined empirically.

Resuming our response:

In a sense, it's like the Monty Hall problem from statistics, where you're given three doors to choose from on a game show, behind one of which is the grand prize. The game show host shows you the prize that is behind one of the doors you didn't pick, then gives you the opportunity to choose again from the two unopened doors, so you now have the choice of sticking with your previous choice or switching it.

Statistics says it is very much to your advantage to switch from your original choice.

For our futures based model, the prizes behind the doors you get to choose are the alternative trajectories for stock prices - and unlike the Monty Hall problem, you get to see what the prize is behind each. The model plays the role of Monty Hall and shows you at certain points of time what the prize is behind a particular door is, or rather, the current level of stock prices tells you exactly how far ahead into the future investors are looking.

Unlike the Monty Hall problem, you have the option of sticking with the door through which you can see the prize. Or you can switch to one of the others based on your assessment of how likely it is investors will focus their attention toward the alternative points of time in the future they represent.

There is a probability that attaches to each, so really the question is one of how you can maximize your returns with that knowlege. Or perhaps a better way to ask the question is if you were to make a larger bet on one of those alternatives becoming the focus of investors, how would you hedge your risk if one of the other alternatives turned into the actual outcome?

Armed with that additional information, ghiblinewt proposed the following strategy:

If we take the reductive case that there are just 2 extremal trajectories for the index - say one broadly net up and the other net down over the period of interest. And then we get a peek at the prize - here do you mean that the "prize" is the expected target level for the index +n days into the future? Is it just the current futures level?

Interjecting to answer these questions, from a practical perspective, since the timing of the changes would be unknown, it would the +n days into the future, which may or may not align with the current futures level. Now back to ghiblinewt's proposed strategy:

Then in that case if and only if we had some ex ante probability for those two extremal paths e.g say that they were 70/30 rather than 50/50 probability of eventuating, then we could definitely exploit that difference. Options for example are priced on the "naïve" equal probability distribution at each point in time - so you would be able to exploit any information advantage you had vis vis the standard pricing models. But as I see it when all is said and done you'd still need to gain your advantage via the ex ante probabilities, and whenever these diverge from those priced by the spot and/or futures level then you can exploit that divergence.

So, difficult, but possible.

Meanwhile, Geoffrey Caveney tackled the problem from more of a hands-on, "How could I do this?", perspective:

Well, if you knew the next big move of the S&P 500 will be downward, but you didn't know the exact timing of the move, a safe way to make a big profit would be to sell lots of out of the money call options on SPY. All the premiums you collect would be free money, if you knew that the S&P 500 wouldn't rise enough to make the call options in the money. In real life I would always recommend selling call *spreads* to protect yourself from unlimited losses in the case of an unexpected huge rally.

We would assume that the opposite strategy, buying "in-the-money" put options, would apply in the situation where we expected a decline in prices. Which is coincidentally what Mark Cuban did with his long position in Netflix (NASDAQ: NFLX) back on 5 February 2016.

Altogether, these are what we would consider to be pretty sophisticated strategies. We'll have to tackle the question of what strategy a typical investor might be able to execute in a regular trading account without having to open an options trading account in a future post.

But if you have good ideas, by all means, please share them in the comments to our How Would You Exploit the Future for the S&P 500 If You Knew It? challenge at Seeking Alpha - if you beat us to a workable strategy before we get around to posting our thoughts, we'll give you full credit for devising it!

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February 10, 2016

Last week, we used a well established method of statistical analysis to demonstrate that layoffs were beginning to spread to the states outside of the eight oil-patch states where they had previously been concentrated.

Today, we're digging into that discovery to see if we can identify the factors that are driving the statistical break in what had been a steady trend of improvement that had become established in July 2014.

Our first step is to look at the state with the biggest population of the states: California. The chart below shows the state's residual distribution of the major trends in new jobless claims filed each week from 31 May 2014 through 23 January 2016, adjusted for seasonality using the BLS' national-level seasonal adjustment factors. (Since California represents the home of one out of every eight Americans, we will assume reasonably approximates the seasonal adjustment factors that would more accurately apply to that single state's economy.)

Residual Distribution of Seasonally-Adjusted Initial Unemployment Insurance Claims Filed Each Week, 31 May 2014 through 23 January 2016

In the chart above, we identify four main trends for new jobless claims in the state of California during the period of time since 31 May 2014.

  • Trend MCA: Coincides with the national trend that began on 23 February 2013, which extended through 28 June 2014. Nationally, the trend was defined by a general improving trend of declining layoffs and new jobless claims, which fell at an average rate of 460 per week. In California however, the trend of new jobless claims was characterized by a volatile but steady increase of 130 per week.
  • Trend NCA: Oil prices, which began falling in late June 2014, prompt a reversal in California's fortunes with respect to new jobless claims. From 5 July 2014 through 3 January 2015, California's new jobless claims would fall at an average pace of 486 per week. We should note that California also increased its minimum wage by $1.00 per hour, effective 1 July 2014, to $8.00 per hour right at the beginning of this change in trend.
  • Trend OCA: After months of falling oil and gasoline prices, they bottom in January 2015 and begin to rebound - peaking in July 2015. New jobless claims in California also rebound in this period, rising at an average rate of 64 per week. This flat-to-upward trend for new jobless claims extends past the end of rising oil and gasoline prices, coming to an end by 5 September 2015.
  • Trend PCA: Weeks after oil and gasoline prices begin falling again, new jobless claims in California begin falling at a steep rate, with the average pace of initial unemployment insurance claims filed each week plummeting by 984 per week - more than double the rate seen when oil and gasoline prices fell by a similar amount back during Trend NCA. The trend however appears to have come to a sudden end in January 2016.

Why such a difference between Trend PCA and Trend NCA? Unlike July 2014, we observe that California didn't increase its minimum wage in this period of falling oil and gasoline prices, which meant that businesses in the state, particularly those related to the fuel price-sensitive food, accommodation, travel and recreation industries, benefited from the increase in the disposable income of Californians without having their costs of doing business arbitrarily increased - allowing them to both put and keep more employees on their payroll to keep up with the improved economic situation.

That changed however on 1 January 2016, as California increased its minimum wage once again by $1.00 per hour, to its current level of $9.00 per hour.

We've previously observed that new jobless claims lag some 2 to 3 weeks behind the events that drive changes in the hiring and employee retention decisions of U.S. businesses, which corresponds to the typical weekly and biweekly payroll period that predominates throughout the U.S.

In the chart above, the sudden appearance of extreme statistical outliers on and after 16 January 2016 indicates that something changed to affect the outlook of California businesses between 26 December 2015 and 2 January 2016. Since oil and gasoline prices, a factor we've already identified to be significant where trends in new jobless claims are concerned, were still falling at that time and also in the weeks since, the factor that most likely caused the break in the established statistical trend was California's minimum wage hike.

Speaking of oil and gasoline prices, we'll close with a chart showing the average retail price of all grades and all formulations of gasoline in the period from May 2014 through January 2016.

Monthly Average Retail Price of One Gallon of Gasoline, All Grades, All Formulations, May 2014 through January 2016

Perhaps the most remarkable thing illustrated in the charts above is that during periods of time when oil and gasoline prices fell, the first period which had a larger decline in fuel prices combined with the immediate impact of a minimum wage hike saw half the rate of improvement in new jobless claims than the period that saw oil and gasoline prices falling by a lesser amount, but no minimum wage hike.

It's just a shame that trend had to come to an end so early after the minimum wage in California was hiked on 1 January 2016.

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February 9, 2016
Junk in Your Trunk Sale - Source: Aberdeen, WA - http://www.aberdeenwa.gov/wp-content/uploads/Junk-in-the-Trunk.pdf

Yesterday, the news broke that the Apollo Education Group (NASDAQ: APOL) has successfully negotiated the sale of itself to a group of private investors. Once approved, the $1.1 billion sale of the Apollo Education Group to the Vistria Group, which is affiliated with Apollo Global Management (NYSE: APO), and the private investment firm Najafi Companies would be expected to be closed in August 2016, just ahead of the education company's academic year..

What won't be part of the sale is the Apollo Group's Carnegie Learning division, which we've previously described as "damaged goods", where we had concluded our previous analysis of the division as follows (emphasis added):

Any potential buyer for Carnegie Learning can expect that the University of Phoenix will not be a customer for the division's products. The Apollo Group recorded a $13 million write off for "certain Carnegie Learning technology intangibles that were no longer being used. The associated technology had been incorporated into University of Phoenix’s academic platform and as a result of the University ceasing use of the technology, no future cash flows associated with the technology were expected over its remaining useful life."

That's a major vote of no confidence in Carnegie Learning's products. Especially from an education institution that has so thoroughly degraded its academic standards for evaluating student performance in math so much, especially as compared to other institutions.

From a business perspective, the failure of Carnegie Learning's math curricula products at the University of Phoenix would suggest that if a sale is made, it will be at an extreme discount with respect to the $75 million price that the Apollo Education Group paid in acquiring the operation.

We suspect however that the more likely disposition for Carnegie Learning will be for its remaining assets to be unloaded to multiple buyers as part of the division's outright liquidation, as they will prove to be worth more than its future prospects as an ongoing business.

We were wrong about two things in the sections of the above passage that we highlighted. First, we were wrong that the Apollo Education Group's purchase price of Carnegie Learning was just $75 million. In reality, that $75 million was indeed the amount they paid for Carnegie Learning, Inc., but the Apollo Education Group also agreed to pay an additional $21.5 million to acquire additional technology from Carnegie Mellon University to support its acquisition, which it agreed to pay over a 10 year period.

Second, our assessment that outright liquidation would be the most likely disposition for the Apollo Education Group's Carnegie Learning division has turned out to be incorrect. It would appear that the Apollo Education Group did manage to find a single buyer for its damaged goods, which it disclosed through its 10-Q statement back on 11 January 2016. Here is the relevant part of the statement, which also outlines the depreciated value of the other assets it acquired as result of its purchase of Carnegie Learning:

During the first quarter of fiscal year 2016, we completed the sale of Carnegie Learning for a nominal amount resulting in a $2.8 million loss on sale. We do not have significant continuing involvement with Carnegie Learning after the sale.

During fiscal year 2015, we began presenting Carnegie Learning’s assets and liabilities as held for sale on our Condensed Consolidated Balance Sheets and its operating results as discontinued operations on our Condensed Consolidated Statements of Operations for all periods presented. As discussed further in Note 17, Segment Reporting, Carnegie Learning’s operating results were previously included in Other in our segment reporting, and certain additional Carnegie Learning expenses associated with University of Phoenix’s use of Carnegie Learning technology were included in our University of Phoenix reportable segment.

The major components of Carnegie Learning’s assets and liabilities presented separately as held for sale on our Condensed Consolidated Balance Sheets as of August 31, 2015 are as follows:

($ in thousands)
As of
August 31, 2015

Accounts receivable, net

Property and equipment, net

Intangible assets, net


Allowance for reduction of assets of business held for sale
Total assets

Deferred revenue


Total liabilities

In being sold for a claimed $2.8 million loss, can you say "extreme discount"?

The sale of Carnegie Learning to a private investor group was facilitated by Tyton Partners, which was previously known as Education Growth Advisors. The firm works closely with private equity firm Education Growth Partners, which while not identified in the Apollo Education Group's SEC filings, is whom we believe is the most likely acquirer of what remains of Carnegie Learning based upon the kinds of firms they've bought in previous acquisitions.

Unofficially, we're happy that our "damaged goods" post helped the purchaser set their price for taking on the remnants of Carnegie Learning's operations, where in the absence of the analysis we provided, they might have otherwise paid far more than they should.

And with Carnegie Learning finally off its books, the fire sale of the Apollo Education Group's remaining damaged goods, itself, would appear to be successful. For a firm whose market cap once peaked at $16.46 billion back in May 2004, its sale price of $1.1 billion indicates that it sold at a discount of 93.3% with respect to its peak valuation.

By our estimates, from the time we decided to take on the Apollo Education Group's problems back on 21 May 2015, the firm lost over $1 billion of its market value, which fell from $1.784 billion on 18 May 2015 to $0.754 billion on 5 February 2016.

Unofficially, we're happy to have helped accelerate the market consensus in determining the Apollo Education Group's true diminished value.

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February 8, 2016

At the end of the last week of January 2016, stock prices rallied on the news that the Bank of Japan would implement negative interest rates in that country and that the Fed was likely to back off its plans to hike interest rates again before the end of 2016-Q1.

In the first week of February 2016, investors started the week still focuses on 2016-Q3, but quickly shifted their attention back toward the near term, holding it on 2016-Q2 through the end of the week, which is why stock prices fell on Tuesday, 1 February 2016, was largely flat through Thursday, 4 February 2016, and then fell again on Friday, 5 February 2016, as investors ended the week splitting their focus between 2016-Q1 and 2016-Q2.

Alternative Futures - S&P 500 - 2016Q1 - Standard Model - Snapshot on 2016-02-05

Click here to see what this chart looked like last week. Here are the main news events that drove the outcome we see in the chart above.

  • 1 February 2016: On a day with little other news to affect their forward-looking focus, investors maintained their focus on 2016-Q3, following the news during the weekend that the Fed's vice chairman Stanley Fischer had indicated that the Fed was concerned about market volatility resulting from the global economic slowdown.
  • 2 February 2016: Investors were compelled to shift their attention toward the nearer term, specifically to 2016-Q2, as the third largest company in the S&P 500, ExxonMobil (NYSE: XOM), reported that its profits had tumbled by 58%, and that it would slash its planned capital expenditures by 25%, signalling that even the largest firms in the U.S. energy sector would not escape being negatively affected by the oil industry's ongoing distress. At the same time, other firms indicated that they were also likely to cut back on their capital investments, suggesting that they also expected weaker growth ahead.
  • 3 February 2016: The day started strong, as New York Fed president Bill Dudley emphasized his concerns that the strong U.S. dollar would lead to "significant consequences", leading investors to once again shift their focus toward 2016-Q3 again as the statement suggested he would not support another rate hike before that time. Trading during the day was volatile however, and the market ended up by a small margin to close the day as oil prices appeared to firm up.
  • 4 February 2016: Stocks prices rose along with commodities as the U.S. dollar weakened. However, the upside potential of that news was muted as U.S. investors resumed their focus on the near term as Cleveland Fed president Loretta Mester offered her comments suggesting that she still thinks the Fed should jack U.S. interest rates higher, as she believes the U.S. economy's poor performance in recent months is just a short term "soft patch".
  • 5 February 2016: A "good" employment situation report for January 2016 opens the door to the Fed continuing its planned interest rate hikes, which leads investors to pull their forward-looking attention even closer to the near term, causing stock prices to fall in response.

Let's discuss some of the dynamics that were at work in the market during the week. First, the fall of stock prices on Tuesday, 2 February 2016 in response to ExxonMobil's earnings news.

Here, the worsening of the expected outlook for U.S. oil producers caused investors to focus more on the nearer term because of the impact that change would have on that industry and because of the timing of when that impact would be felt. As it happened, the oil companies that reported earnings in the first week of February 2016 consistently indicated that they were also cutting back on their planned capital investments, indicating that they see little prospects for growth ahead, and one, ConocoPhillips (NYSE: COP), went so far to cut its dividend too.

At the beginning of this article, we stated that as of Friday, 5 February 2016, investors were splitting their forward-looking attention between 2016-Q1 and 2016-Q2, although if you look closely at our chart, it would appear that investors were tightly focusing their attention to 2016-Q2.

The only reason it appears that way is due to our use of historic stock prices as the base reference points from which we project the alternative trajectories of future stock prices, where the echoes of the market's activity from 13 months ago, 12 months ago and 1 month ago contribute to our projections. In this case, the echoes of that past volatility makes it appear that investors are closely focused on 2016-Q2 in our model, rather than splitting their focus between 2016-Q1 and 2016-Q2. (The quick and easy way to confirm that assessment is to simply connect the dots for each of the alternative trajectories on each side of the volatility indicated by the short term echo event.)

Finally, on an upcoming programming note, we plan to follow up our post last week where we sought feedback from readers on Seeking Alpha on how to exploit the kind of information our futures-based model can provide later this week.

Previously on Political Calculations

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