to your HTML Add class="sortable" to any table you'd like to make sortable Click on the headers to sort Thanks to many, many people for contributions and suggestions. Licenced as X11: http://www.kryogenix.org/code/browser/licence.html This basically means: do what you want with it. */ var stIsIE = /*@cc_on!@*/false; sorttable = { init: function() { // quit if this function has already been called if (arguments.callee.done) return; // flag this function so we don't do the same thing twice arguments.callee.done = true; // kill the timer if (_timer) clearInterval(_timer); if (!document.createElement || !document.getElementsByTagName) return; sorttable.DATE_RE = /^(\d\d?)[\/\.-](\d\d?)[\/\.-]((\d\d)?\d\d)$/; forEach(document.getElementsByTagName('table'), function(table) { if (table.className.search(/\bsortable\b/) != -1) { sorttable.makeSortable(table); } }); }, makeSortable: function(table) { if (table.getElementsByTagName('thead').length == 0) { // table doesn't have a tHead. Since it should have, create one and // put the first table row in it. the = document.createElement('thead'); the.appendChild(table.rows[0]); table.insertBefore(the,table.firstChild); } // Safari doesn't support table.tHead, sigh if (table.tHead == null) table.tHead = table.getElementsByTagName('thead')[0]; if (table.tHead.rows.length != 1) return; // can't cope with two header rows // Sorttable v1 put rows with a class of "sortbottom" at the bottom (as // "total" rows, for example). This is B&R, since what you're supposed // to do is put them in a tfoot. So, if there are sortbottom rows, // for backwards compatibility, move them to tfoot (creating it if needed). sortbottomrows = []; for (var i=0; i
There are quite a few famous unsolved problems in mathematics. Some are so famous they have million dollar bounties on them that will be paid out to the first mathematicians who definitively either prove or disprove them.
And then there are other important conjectures out there that, in addition to academic recognition, might pay smaller rewards to the people who crack them. But which if they are cracked, can be even more valuable if they can successfully be put to use.
One of those smaller conjectures is the Talagrand Convexity Conjecture. Proposed by French mathematician Michael Talagrand in 1995, Talagrand famously offered a $2,000 prize to anyone who solved it. Here is Jules Aknin's simplified description of the conjecture:
Talagrand’s convexity conjecture is a statement about high‑dimensional geometry: even in enormous, messy clouds of points, simple convex shapes are guaranteed to appear. In other words, no matter how chaotic the configuration looks, there is unavoidable structure hiding inside it.
If such structures are truly unavoidable, they can open the door to practical applications, including in financial markets. Aknin describes how they might contribute to finding order within chaos:
At a deeper level, results like this sit exactly at the intersection of geometry and probability. They don’t just create “pretty shapes” in higher dimensions, they show how structure emerges inside high‑dimensional random systems, which is the same conceptual problem we face when building models for complex datasets. In fact, as the original article notes, this kind of unification between geometric and probabilistic thinking could eventually influence how machines process high‑dimensional data and how we design algorithms that operate in those spaces.
[...]
On the surface, a high‑dimensional cloud of points in geometry and the Australian equity market do not look related. But in practice, we deal with a very similar object every day: hundreds of stocks, multiple time horizons, momentum profiles, risk factors, macro shocks and behavioural flows all interacting at once.
From a distance, that system may appear as pure noise. Talagrand’s result reinforces an idea that systematic managers have believed for a long time: structure is not optional, it is inevitable; the real question is whether you have the tools and discipline to find it.
Even though it doesn't carry the million-dollar prize of mathematics most famous unsolved problems, the stakes for those who can successfully build on the proven conjecture are still very high. That work is quite possibly worth a lot more than Talagrand's two-thousand dollar prize for the proof itself.
A team of three mathematicians stand to collect Talagrand's $2,000. Merrick Hua, Antoine Song, and Stefan Tudose posted a preprint paper of their proof on 11 May 2026, in which they converted the problem from one involving geometry to one involving probability and combinatorics. Like many math stories this year, AI is involved, but unlike those other stories, it's only a bit player.
The new proof was worked out by Dongming Hua and Antoine Song from the California Institute of Technology, and Stefan Tudose from Princeton University, who joined the other authors after hearing about their work. Together, the mathematicians reformulated Talagrand's geometric conjecture to a problem of probability theory and random vectors. In their paper published on the arXiv preprint server, they proved an equivalent conjecture for probability, showing that any 1-subgaussian random vector in n dimensions can be expressed as the sum of three standard Gaussian random vectors.
This result solves Talagrand's convexity problem, proving that for any large enough set in Gaussian space, a convex set of significant measures can be found inside a triple sum of the original set. The solution also confirms a combinatorial analog of the problem, which is important for discrete mathematics.
Initially, Song and Hua say they attempted to work out a solution with the help of ChatGPT. However, while the LLM helped to answer some of their questions and move them closer to a solution, it was Tudose who provided the final proof. Ultimately, the team did not use the work done with ChatGPT. In their paper, the team writes that Tudose's proof was "more general and conceptual."
It's a remarkable achievement. It is also a harbinger of the kind of role AI will find as just another tool used by people.
Image credit: Texture of an iced leaf image by Daniela deGol on Wikimedia Commons. Creative Commons CC By 4.0 Attribution 4.0 International Deed.
The U.S. new home market has largely recovered from the disruption of January 2026's blizzards. Unfortunately, rising mortgage rates combined with an uptick in the average sale price of new homes to reduce the quantity of sales. This combination of factors resulted in the total valuation of new homes sold in April 2026 to decline below the levels recorded a month earlier.
Political Calculations' initial estimate of the total value of new home sales in the United States during April 2026 is $28.30 billion. This value is slightly higher than the initial estimate of $28.24 billion for March 2026, but has declined from a revised value of $28.43 billion for the month.
The number of new home sales continues to hold relatively steady. The initial estimate of the annualized trailing twelve month average of the total number of new home sales for April 2026 is 665,000. This value falls below the range of 671,000 and 684,000 that had held since January 2024.
The initial estimate of the trailing twelve month average of a new home sold in April 2026 is $521,300. New home prices have generally rising since bottoming at $502,525 in September 2024. The average remains below the peak of $529,692 recorded for June 2022 at the height of the high inflation unleashed by the Biden administration.
All these figures represent time-shifted, partial trailing twelve month averages for each data series, which will be subject to revision for the next ten months before being finalized. The following charts present the U.S. new home market capitalization, the number of new home sales, and their average sale prices as measured by their time-shifted, trailing twelve month averages from January 1976 through April 2026.
New home sales were reported to have surged in March as prices fell to a five-year low, but much of this boost in sales may represent a springback from the impact of blizzards in much of the U.S. in January 2026 that shrank sales far below expectations.
Bloomberg confirms the April 2026 sales slump for new homes was not expected:
Sales of new US homes declined in April by more than forecast as builder incentives failed to motivate potential buyers at the start of the spring selling season.
Purchases of new single-family homes decreased 6.2% from March to a 622,000 annualized pace, according to government data released Thursday. Economists expected a 660,000 rate, based on the median estimate in a Bloomberg survey.
It would seem the springback in sales from January 2026's blizzards was truly that and not the start of an upward trend.
U.S. Census Bureau. New Residential Sales Historical Data. Houses Sold. [Excel Spreadsheet]. Accessed 28 May 2026.
U.S. Census Bureau. New Residential Sales Historical Data. Median and Average Sale Price of Houses Sold. [Excel Spreadsheet]. Accessed 28 May 2026.
Image Credit: High angle shot of suburban neighborhood photo by David McBee on Pexels.
Labels: real estate
Six months and two earnings seasons have come and gone since Thanksgiving 2025 when we were introduced to the worst performing stocks of the S&P 500 (Index: INX). How many of those 10 stocks have seen their fortunes improve and how many are proving to be an even bigger investment turkey than they appeared on the day after last Thanksgiving?
Let's cut to the chase! Here are the relative winners as measured by the percentage of their stock price recorded value on 28 November 2025:
The stock price of these S&P 500 component companies are all higher than they were on 28 November 2026 and are also beating the S&P 500's growth, which has risen to 109.8% of its day-after-Thanksgiving-Day-2025 level. What each of these companies have in common is improved business performance combined with an improved outlook for their earnings.
Meanwhile, all seven of the other Thanksgiving Leftover stocks have experienced continuing declines in their stock prices. Here they are, ranked from best-to-worst performing over the past six months:
The following spaghetti chart shows how each performed throughout the last six months:
Since our last update, Lululemon Athletica (NASDAQ: LULU) has taken the most negative turn for the worse. The athletic apparel company is struggling to sell its mostly foreign-made clothing line in the U.S. after hiking prices to cover the cost of new tariffs. But higher costs are not the "athleisure" clothing company's biggest problem. Its latest products have been on the wrong side of fashion trends as it faces increased competition.
If that weren't enough, the company's top management is involved in war of words with Chip Wilson, the company's founder, who criticized them for losing the company's "cool" factor.
TLDR: Poorly managed Lululemon has become costly and unfashionable with few indications that will change anytime soon, sending its stock price even lower.
Breaking away from the ongoing drama of a failing business, the next chart reveals how the performance of the Thanksgiving 2025 Leftover stocks compares as a group with the S&P 500 index, both as a market-cap weighted index and as an equal-weighted index.
By both grouping methods, the Thanksgiving Leftover stocks are substantially underperforming the S&P 500 index.
Compared to a month ago, the equal-weighted group is close to the same, but the market-cap weighted group is worse off. If you went double-or-nothing in betting whether the Thanksgiving Leftover stocks were going to be doing better or worse than they were a month ago, we'd have to give the edge to worse this month.
Labels: SP 500, stock prices
The S&P 500 (Index: SPX) rose 0.8% above its previous week's close to end the trading week at 7,473.45 on Friday, 22 May 2026 as investors went into the Memorial Day holiday weekend.
In doing so, the index confirmed that stock prices have fully recovered from the Iran war impact. We find the trajectory of the S&P 500 falls very close to the central trend line of the redzone forecast range we added to the alternative futures chart back on 23 February 2026, several days ahead of when the geopolitical event began. For us, that timing has been fortunate because the redzone forecast range has been able to function as a counterfactual projection of how the S&P 500 would have changed if the Iran war geopolitical event had never happened.
So for the trajectory of stock prices to fall so neatly near the middle of that forecast range now that we're coming to its end is a strong confirmation the negative shock of the geopolitical event upon them has fully waned. Here is the latest update of the alternative futures chart that shows that outcome:
The redzone forecast range is based on the assumption investors would primarily focus on 2026-Q2 in making the decisions that set the trajectory of stock prices throughout its run. The Iran War geopolitical event didn't alter that focus throughout this period, but rather, added noise on top of the signal provided by the dividend futures-based model we use to forecast the S&P 500's future.
There are other factors that affect both the signal and noise investors consider in their decision making, which is provided from the random onset of new information. Here are the past week's market-moving headlines.
The CME Group's FedWatch Tool moved up the expected timing of a quarter point increase in the Federal Funds Rate to 28 October (2026-Q4). The FedWatch tool also now anticipates another quarter point rate hike will come on 28 April (2027-Q2), with a strong probability its timing could also move earlier.
The Atlanta Fed's GDPNow toolestimate of real GDP growth for the U.S. economy in the current quarter of 2026-Q2 increased to +4.3%, up from the +4.0% it projected a week earlier.
Image credit: Microsoft Copilot Designer. Prompt: "An editorial cartoon of a Wall Street bull and bear who are at Indianapolis watching cars labeled 'Dow', 'SP 500', and 'NASDAQ' race around the track".
Which states are the most and least affordable places for American families to live after paying taxes and their essential expenses?
The Common Sense Institute tallied up the numbers and ranked each state after subtracting federal and state taxes and also essential expenses like housing, utilities, groceries, auto and health insurance, fuel, and childcare in each state from the paychecks for a family of four with two adult breadwinners who work full time and earn the state's median hourly income.
Visual Capitalist's Dorothy Neufield then revisualized the results to focus on how much that of the modeled families' income remained. Here's her version of the Common Sense Institute's map:
Here's her analysis of the most and least affordable states:
In top-ranked states like Iowa, households keep nearly 35% of their income, about $2,900 per month. In Hawaii, that figure drops to just 9%. That’s a difference of more than $2,000 per month in disposable income.
[...]
Midwestern states dominate the rankings, largely due to lower housing and childcare costs.
Iowa ranks first, with households keeping 34.7% of their income, followed by South Dakota (34.6%) and North Dakota (33.5%).
[...]
In the least affordable states, families spend up to 91% of their income on essentials and taxes, leaving little room for savings or unexpected expenses.
Hawaii families are most strained, with 9% of income left, followed by California at 10.9%. Between 2019 and 2025, California households saw one of the largest declines in affordability across states.
Massachusetts, despite high incomes, ranks near the bottom. Childcare alone consumes 24% of household income, showing how a single cost category can erode income advantages.
The Common Sense Instutute also looks at how each state's affordability has changed from 2019 to 2025. They find that Kansas, New Mexico, and Utah have seen their cost of living fall the most, while Rhode Island, Massachusetts, and California have seen the biggest escalation in living expenses over these years.
Dorothy Neufield. Mapped: Where Americans Keep Most of Their Paycheck. Visual Capitalist. [Online article]. 27 April 2026.
Labels: data visualization, personal finance
Once upon a time, in 2005, we wrote about Solare, architect Paolo Soler's ambitious concept of the Lean Linear City of the future, a city without cars and without urban sprawl. Robert Ipsen describes the basic concept of how a city might be successfully configured to achieve that result:
The first step is not design. The first step is choosing the spine.
The corridor comes before the city
The Lean Linear City is organized around an arterial spine. In the book, that spine is both technical and symbolic: high-speed rail, pedestrian circulation, utilities, energy distribution, civic access, and the shared metabolic infrastructure of the city are concentrated into a single linear system. The inhabited modules attach to it. Growth proceeds along it. The landscape outside it remains legible because the city has not spilled everywhere at once.
This is the crucial difference between a linear city and sprawl. Sprawl also grows outward, but it does so by multiplying roads, pipes, wires, parking lots, and private parcels in every direction. The lean linear form grows by intensifying a corridor. It accepts length while refusing dispersal. It says: if the city must extend, let it extend along a shared artery rather than dissolve into an asphalt mist.
It's the stuff of science fiction dreams. But in Saudi Arabia, it served as a foundation for a real-life megaproject: Neom. Here's a video introduction:
But it turned out to be a lot harder to execute than to draw up. The ambitious project has been severely scaled back. Matt Bevan of Australian Broadcasting Corporation (ABC) News looks at what happened to the city of the future in the following 13-and-a-half minute video:
That report was in 2024. But is "The Line", as the Neom megaproject is known, really dead? The two percent of the project that wasn't defunded is still going forward. Here's The B1M's video report on the city's project from December 2025, which digs into the construction challenges of actually building the linear city:
As conceived, the linear city is a bold vision of what the city of the future could be. Given the challenges of making the project viable, it might always be.
Labels: ideas, technology
In the United States, when people talk about mortgages, they almost invariably are talking about the 30-year fixed-rate conventional mortgage.
It wasn't always that way. In fact, it wasn't until the Housing Act of 1954 became law that the 30-year fixed rate mortgage became mainstream. The law's "combination of federal insurance and full amortization requirements made the extended timeline financially safe for banks". Soon after, the 30-year fixed rate conventional mortgage became the default for both lenders and home buyers.
But it wasn't until much later that federally-backed agencies like Freddie Mac began keeping regular track of what the average monthly interest rate was for homes bought in the U.S. with these mortgages. As important as they are for prospective American homeowners, the historical data for these mortgages only goes back to April 1971. Freddie Mac, officially known as Federal Home Loan Mortgage Corporation, has maintained weekly data for mortgages extending back to that month. The government-sponsored enterprise also used to report monthly averages for mortgage rates from April 1971 forward, but discontinued the practice after December 2022.
And yet, because housing sales and prices are reported on a monthly basis, it's incredibly useful to have mortgage rates averaged over the period of a month. Since Freddie Mac isn't doing that job any more, we took it over and have made it publicly available.
It's built into the following interactive chart, which we've just updated to visualize 55 years worth of the average monthly interest rates for 30-year conventional mortgages in the U.S.
The average 30-year fixed-rate conventional mortgage was 6.33% in April 2026.
Freddie Mac. 30-Year Fixed Rate Mortgages Since 1971. [Online Database]. Accessed 15 May 2026. Note: Starting from December 2022, the estimated monthly mortgage rate is taken as the average of weekly 30-year conventional mortgage rates recorded during the month.
Image credit: Mortgage Payment Due date by alanharder.ca via Wikimedia Commons. Creative Commons Attribution 2.0 Generic (CC BY 2.0).
Labels: data visualization, real estate, tool
The outlook for the quarterly dividends of the S&P 500 (Index: SPX) has substantially improved in the month since our previous snapshot.
Here is our summary of how the outlook for the S&P 500's quarterly dividends per share changed since our 15 April 2026 snapshot:
The following chart shows how expectations for the S&P 500's quarterly dividends per share changed in the month from 15 April 2026 to 15 May 2026.
The large change in the forecast value for the most distant future quarter of 2027-Q2 is typical of the kind of volatility we see in dividend futures after the data for a new quarter first becomes available. Based on just the increases seen for all the other future quarters however, May 2026 saw one of the strongest month-over-month improvements in the S&P 500's dividend outlook in years.
Ready to find out more about dividend futures data? If this is your first exposure to the S&P 500's quarterly dividend futures, be sure to read the following section that explains what this data is communicating about the future for the index' dividends.
For this series, we take a snapshot of the CME Group's S&P 500 quarterly dividend futures data shortly after the second or third week of each month.
Dividend futures indicate the amount of dividends per share to be paid out over the period covered by each quarter's dividend futures contracts, which start on the day after the preceding quarter's dividend futures contracts expire and end on the third Friday of the month ending the indicated quarter. For example, as determined by dividend futures contracts, the now "current" quarter of 2026-Q2 began on Saturday, 21 March 2026 and will end on Friday, 19 June 2026. Since the expectations for this quarter's dividend payouts can change all the way up to that final date, it qualifies as a future quarter.
Because dividend futures are tied to options contracts that run on this schedule, that makes these figures different from the quarterly dividends per share figures that are reported by Standard and Poor. S&P reports the amount of dividends per share paid out during regular calendar quarters after the end of each quarter. This term mismatch accounts for the differences in dividends reported by both sources, with the biggest differences between the two typically seen in the first and fourth quarters of each year.
Dividend futures data is important for more than just what they project will be a future quarter's dividend payout. They represent the quantified expectations investors have for the future income they will realize from holding their investments, which in turn, affects how investors set current day stock prices. How changes in the outlook for dividends at specific points of time in the future contribute to changes in current day stock prices is described by this math.
Image Credit: Microsoft Copilot Designer. Prompt: "A crystal ball with the word 'SP 500' written inside it". And 'Dividends' written above it, which we added.
The S&P 500 (Index: SPX) continued rising during the trading week ending on Friday, 15 May 2026, clocking several new record high closes during the week that was. The index however retreated from those new highs on Friday, but still closed at 7,408.50, up a little over 0.1% above its previous week's close.
Friday was the day inflation fears came roaring back for the U.S. economy, erasing two days worth of gains for the index in the process and taking expectations of rate cuts entirely off the table for 2026.
The CME Group's FedWatch Tool now anticipates no change in the Federal Funds Rate until 9 December (2026-Q4), when it now projects a quarter point rate hike, which is a big change from the previous week. Right now, it's not projecting much more than that increase, but the tool's bias going into 2027 has shifted toward expectations of more rate hikes.
QTR's Fringe Finance captured the Wall Street zeitgeist of the moment:
Lauren Hyslop, investment manager at Mattioli Woods, summarized the situation well in comments to CNBC: “Rising bond yields are once again imposing their will on markets, tightening financial conditions and sapping risk appetite across asset classes,” she said.
She added that investors are confronting the “uncomfortable reality of ‘higher for longer’ rates in the U.S., as stubborn inflation and surprisingly resilient growth push back any meaningful pivot to easing.” She also noted that a stronger dollar, fading expectations for liquidity support, geopolitical uncertainty, and fiscal concerns are all adding pressure simultaneously. That combination is particularly dangerous because it removes the easy narrative markets have relied on for months that rate cuts were inevitable and policymakers would remain quick to intervene.
The fact that the Fed is stuck between a 3.8% CPI and 6% PPI rock and a market-teetering-on-the-brink-of violently-pulling-back hard place was the core of yesterday’s concern. If the bond market starts to get violent, what options does the Fed have to start printing to buy bonds and do yield curve control with inflation already where it is? The central bank’s hands might be tied — and this is a scary (and somewhat unprecedented) thought....
Even so, the latest update of the alternative futures chart shows the trajectory of the S&P 500 remains within the redzone forecast range we forecast for it almost three months ago following the disruption of the Iran war geopolitical event.
The change from being biased to either holding rates steady or cutting rates is a global response to inflation pressures and the disruptions from the Iran war geopolitical event. The market moving headlines of the week indicate they are increasingly expected in the U.S., in Japan, and also in the Eurozone:
The Atlanta Fed's GDPNow toolestimate of real GDP growth for the U.S. economy in the current quarter of 2026-Q2 rose to +4.0%, up from the +3.7% it projected a week earlier.
Image credit: Microsoft Copilot Designer. Prompt: "An editorial cartoon of a Wall Street bull and bear who are shocked and scared by a news report that says 'INFLATION IS BACK, BABY!'"
Prime numbers are unique. Unlike all other numbers, prime numbers cannot be divided equally by a whole number to get a whole number result except for itself and the number 1.
By contrast, every other number but prime numbers will include other numbers as factors. Also called composite numbers, when you drill down far enough, you'll find each has a unique factorization made up of nothing but prime numbers.
If you start counting up from 1, you'll frequently run into prime numbers at the beginning. But as you count higher and higher, you'll find prime numbers become fewer and farther apart. At first glance, it seems like they're randomly distributed among all the numbers you're counting. But that appearance is deceptive, because when you tease the numbers just right, a non-random pattern emerges.
The following video by Physics Explained's Rhett Allain is one of the best we've seen in establishing the foundation of just how the numbers have to be teased to reveal the pattern that the prime numbers are following.
In the video, New Scientist's Jacklin Kwan focuses on the Riemann Hypothesis, which describes the pattern that prime numbers appear to follow into infinity, which is the biggest unproven conjecture in math:
Labels: math
The affordability of new homes in the U.S. improved in March 2026 as builder incentives to reduce the sale prices of new homes combined with relatively low mortgage rates and a rising income for the typical American household.
The first two of these factors directly reduced the typical mortgage payment for U.S. households, while the third makes the lower cost for owning a new home more affordable by definition for the nation's median income-earning household. Here are the applicable numbers:
For that household at the exact middle of the U.S. income spectrum, the average mortgage payment for a new home purchased at the national median sale price with zero-percent down would consume 32.6% of the household's monthly income in March 2026.
This value falls in between the two major affordability thresholds mortgage lenders have traditionally used in the form of the 28/36 rule to determine whether to extend a mortgage to new home buyers. The following chart shows how March 2026's level of relative affordability for new homes compares with the affordability for every month from January 2000 through March 2026:
In March 2026, buying a new home was the most affordable it has been in the U.S. for a typical American household at any time in the last four years.
U.S. Census Bureau. New Residential Sales Historical Data. Houses Sold. [Excel Spreadsheet]. Accessed 5 May 2026.
U.S. Census Bureau. New Residential Sales Historical Data. Median and Average Sale Price of Houses Sold. [Excel Spreadsheet]. Accessed 5 May 2026.
Freddie Mac. 30-Year Fixed Rate Mortgages Since 1971. [Online Database]. Accessed 11 May 2026. Note: Starting from December 2022, the estimated monthly mortgage rate is taken as the average of weekly 30-year conventional mortgage rates recorded during the calendar month.
Image Credit: Microsoft Copilot Designer. Prompt: "An editorial cartoon of a new home buyer speaking with a real estate agent in front of a 'NEW HOME FOR SALE' sign that says 'MEDIAN PRICE MARKED DOWN TO $387,400!'"
Labels: personal finance, real estate
Welcome to the blogosphere's toolchest! Here, unlike other blogs dedicated to analyzing current events, we create easy-to-use, simple tools to do the math related to them so you can get in on the action too! If you would like to learn more about these tools, or if you would like to contribute ideas to develop for this blog, please e-mail us at:
ironman at politicalcalculations
Thanks in advance!
Closing values for previous trading day.
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