Political Calculations
Unexpectedly Intriguing!
18 March 2024
A bull and a bear riding a roller coaster together, not smiling. Generated with Microsoft Copilot Designer.

As trading weeks go, the second week of March 2024 resembled a disappointing roller coaster ride for investors. The S&P 500 (Index: SPX) climbed to a new record high of 5,175.27 on Tuesday, 12 March 2024, but then went on to lose 1.1% of that new high value by the end of the week on the downhill part of its ride. The index closed at 5,117.09, a small 0.13% decline from the previous week's close.

What made the week disappointing for investors is a shift in expectations for how frequent interest rate cuts will be during 2024. Higher than expected inflation reports drove the change. While the CME Group's FedWatch Tool continues to project the Fed will hold the Federal Funds Rate steady in a target range of 5.25-5.50% until 12 June 2024 (2024-Q2) when it is expected to begin a series of quarter point rate cuts starting on that date, the FedWatch Tool's outlook changed to indicate investors are now anticipating these rate cuts will proceed at twelve week intervals, occurring less often than was projected just last week.

The downward leg of the S&P 500's roller coaster ride during the past week puts the index' trajectory closer to the middle of the redzone forecast range, as indicated in the latest update in the alternative futures chart.

Alternative Futures - S&P 500 - 2024Q1 - Standard Model (m=+1.5 from 9 March 2023) - Snapshot on 15 Mar 2024

Speaking about the future for interest rates, there were two other big economic news headlines involving them during the week that was. First, the European Central Bank (ECB) signaled it will almost certainly begin cutting Eurozone interest rates by the end of the this month. But in Japan, the Bank of Japan will take the opposite action as inflation ramps up in that country, marking the end of its long-running negative interest rate policy.

Those headlines, and more, are included in the following summary of the week's market-moving headlines:

Monday, 11 March 2024
Tuesday, 12 March 2024
Wednesday, 13 March 2024
Thursday, 14 March 2024
Friday, 15 March 2024

The Atlanta Fed's GDPNow tool's latest estimate of real GDP growth for the first quarter of 2024 (2024-Q1) fell to +2.3% after last week's +2.5% growth projection.

Image credit: Microsoft Copilot Designer. Prompt: "A bull and a bear riding a roller coaster together, not smiling."

Labels: ,

15 March 2024
Brown and Black Basketball photo by Kylie Osullivan on Unsplash - https://unsplash.com/photos/brown-and-black-basketball-ball-BfaBLVCBTI8

What separates the top teams in the National Basketball Association from the bottom teams in the league?

If you answered "their scores", you're right. But you might be surprised by how seemingly little difference there is between teams.

The offensive performance of NBA teams can be summarized in a figure known as the offensive rating. This statistic combines several different scoring statistics into a single measure that can be used to rank teams. According to the available data at StatMuse, at this point of the NBA's 2023-2024 season, the Boston Celtics have the highest offensive rating of 122.5, while the Detroit Pistons have the lowest at 111.6. If you've been paying attention to the NBA season, that these teams are in their respective positions should sound about right. The Celtics are recognized as a dominant team while the Pistons would be at high risk of being sent down to a lower league if European-style relegation existed for U.S. sports leagues.

But as we're about to show, the two teams have some very similar statistics. The data below shows their offensive output on several different categories of scoring for the 2023-2024 season through Sunday, March 9, 2024.

Boston Celtics:

  • Points per game (PPG): 120.8 points per game, most in the NBA.
  • Field Goals Made (FGM): 43.8 field goals per game, fifth overall and 48.5% of their attempts.
  • Three-Pointers Made (3PM): 16.2 three-pointers per game, 38.5% of their attempts and best in the NBA.
  • Free Throws Made (FTM): 17.0 free-throws per game, 80.8% of their attempts, ranking 17th in the NBA.

Detroit Pistons:

  • Points per game (PPG): 112.4 points per game, tied for fifth-lowest in the NBA.
  • Field Goals Made (FGM): 41.9 field goals per game, 47.1% of their attempts and ninth-lowest in the league.
  • Three-Pointers Made (3PM): 11.3 three-pointers per game, 35.6% of their attempts and second-lowest in the NBA.
  • Free Throws Made (FTM): 17.3 free-throws per game, 78.4% of free-throws attempted, tenth-lowest among NBA teams.

Only 8.4 points per game separates the two teams' overall averages. They are within two baskets per game of each other when considering field goals and free-throws, where they even have very similar shooting percentages. Where they differ most is three-point shots. On average, the Boston Celtics successfully make five more three-point shots per game than the Detroit Pistons do.

That single statistic goes a long way to explaining why the Celtics have the highest offensive rating in the NBA and why the Pistons have the lowest, despite the two teams scoring on 38.5% and 35.6% of their respective three-point attempts. With such a similar percentage of successful attempts, that means the Celtics higher number of successful three-point shots per game is based on their ability to attempt more of these shots than the Pistons are able to. That ability is the key to the Celtics offensive dominance during the 2023-2024 season.

If only there were a great way to visualize that respective dominance. Over at Reddit's r/dataisbeautiful, Solid_Example7519 has put together a fantastic heat map graphic to illustrate how every team in the NBA compares to each other in their ability to score from different parts of the court at about the time of the NBA's All Star Game. We've excerpted the following charts for the Boston Celtics and the Detroit Pistons to show them next to each other:

Boston Celtics:

Boston Celtics - 2024

Detroit Pistons:

Detroit Pistons - 2024

Here's how Solid_Example7519 describes what the data visualization shows:

Blue is good, red is bad....

I calculated how many points every team got in each position on the court and then normalised it using a Z-score (0 means they got an average number of points, a score of 1 is one standard deviation above meaning top 16%, 2 is two standard deviations and means they are in the top 2.5%)....

I filtered it to be only the coordinates where a team scored at least 5 points, and so if there are no points within a hexagon with more than 5 points then it is blank. This was to make it easier to read and draw meaning from i.e. because these empty spots had teams scoring very few points in them, it meant they got a really really low score, while teams only had to score relatively few points to be seen as disproportionately good there....

It is relative to other teams, so a high z-score on the three-pointer line means that they score more points there relative to other teams.

The individual team charts also emphasize the extent to which the three-point shot affects how professional basketall is played in 2024. The mostly empty hexagonal grids that fall between the key and the three-point line confirm that nearly all teams have bought into the strategy of either going in close to score field goals or shooting from a distance to collect higher points, even though they score less often per attempt.

Comparing Boston to Detroit again, we see the Celtics are highly at the three point line from the left hand side of the court. The Detroit Pistons, on the other hand, are best around the basket itself, but are very weak along the entire arc of the three-point line.

We'll close by pointing again to Solid_Example7519's entire chart, but please do click through to the r/dataisbeautiful post to find out more about how it was generated.

Previously on Political Calculations

Image credits: Brown and Black Basketball photo photo by Kylie Osullivan on Unsplash. NBA 2023-2024 Heat Map by u/Solid_Example7519 on r/dataisbeautiful. Used with permission.

Labels: ,

14 March 2024
A diagram showing a coal power plant emitting carbon dioxide. The power plant has the Chinese characters for 煤 (coal) written on it. Image generated by Microsoft Copilot Designer.

The pace at which the concentration of carbon dioxide increases in the Earth's atmosphere rose again for the sixth consecutive month in February 2024. Since last bottoming in February 2023, the trailing twelve month average of the year-over-year change in the parts per million of CO₂ being added to the Earth's air has increased by 58%, rising from 1.89 ppm to 2.98 ppm. Only the months of August and September 2023 have seen a small pause in that otherwise upward trend.

That trend continues to be dictated by China's emissions of carbon dioxide, which is primarily produced by its coal-fired power generation plants. China is, by very large margins, the worlds's biggest consumer of coal and the world's biggest producer of carbon dioxide emissions.

China's emissions of CO₂ have generally been on the upswing since the nation ended its zero_COVID lockdowns at the end of 2022. Those emissions have generally reflected the Chinese economy's performance under China's government's ongoing efforts to stimulate the nation's economy during 2023.

The following chart shows the trends for the increasing emissions of excess carbon dioxide into the Earth's atmosphere from January 2000 through February 2024.

Trailing Twelve Month Average Year-Over-Year Change in Parts per Million of Atmospheric Carbon Dioxide, January 2000 - February 2024

China remains on track to replace the United States as the world's historically largest emission source of excess carbon dioxide in the atmosphere before the end of the 2020s.

That accomplishment is likely given that China's internal coal production hit a new record high in 2023 as the country also boosted its imports of coal to support its growing appetite for electricity.

China continues to rely on coal and coal-fired power generation to meet its growing power demand, and despite being the world's top investor in solar and wind capacity, it also plans a lot of new coal-fired electricity capacity.

During the first half of 2023 alone, China approved more than 50 GW of new coal power, Greenpeace said in a report this year. That's more than it did in all of 2021, the environmental campaign group said.

That continuing expansion has real consequences. Earlier this month, China's government backed off its official five-year target for reducing the carbon intensity of its economy.

“China is effectively admitting its failure to fulfill the five-year target,” Li Shuo, director of the China Climate Hub at the Asia Society think-tank, told Climate Home. “This year’s target is even more modest than the average rate of reduction needed, while they should be playing catch up.”

Lauri Myllyvirta, a senior fellow at the Asia Society and co-founder of CREA, said that China is “basically admitting defeat” with this “very important metric”.

“The [2.5%] target is completely inadequate to get China back on track towards its 2025 goals,” he added. “It is very alarming that the government is not articulating a plan on how they are going to hit an internationally-pledged target.”

China's coal consumption is now projected to peak in 2026. Assuming China's government doesn't continue to back away from its pledges to reduce its use of coal and other carbon-based fuels.

References

National Oceanographic and Atmospheric Administration. Earth System Research Laboratory. Mauna Loa Observatory CO2 Data. [Online Data]. Updated 5 March 2024.

Image Credit: Microsoft Copilot Designer.. Prompt: "A diagram showing a coal power plant emitting carbon dioxide. The power plant has the Chinese characters for 煤 (coal) written on it."

Labels: ,

13 March 2024
Global trade routes Image generated by Microsoft Copilot Designer.

The Super-Tuesday presidential primaries have come and gone in the United States. Along with them is any question of which political candidates will collect enough delegates to win their respective major party's nomination for president in the 2024 elections. Barring unforeseen events, Donald Trump will be the Republican nominee and Joe Biden will be the Democratic nominee. It's going to be an excruciatingly long election season.

The day after Super Tuesday, the U.S. Census Bureau released its data on the U.S.' international trade for January 2024. That new data gives us an opportunity to compare the trade policies of the two candidates, since Trump served as U.S. President from January 2017 to January 2021 and Biden has served as U.S. President in the period since.

The following chart provides a picture of that history as measured by the value of the goods exchanged between the United States and China from January 2017 through January 2024. Both candidates made trade with China a central part of their respective presidencies and as such, it can tell us a lot about their policies.

Combined Value of U.S. Exports to China and U.S. Imports from China, January 2017 - Januar 2024

As president, both candidates implemented anti-free trade policies focusing on trade between the U.S. and China in particular. Those policies contributed to serious declines in the goods exchanged between to two countries. But in Trump's case, those policies overlapped with 2020's Coronavirus Pandemic, which caused trade between the two countries to fall even further than it would otherwise have, which adds a complicating factor to any comparison.

Or does it? What if we directly compared the two declines in trade between the U.S. and China as a percentage of the pre-decline peak recorded during both presidential terms? For Trump, that peak took hold after October 2018, when China's 'madman' retaliation against the tariffs Trump had imposed earlier in 2018 impacted the international trade data, but long before the pandemic had any effect. For Biden, the equivalent "Month 0" is October 2022, when he announced export restrictions to block China from importing advanced semiconductor technology from the U.S.

The next chart shows the negative impact resulting from the anti-free trade policies of both presidents, as measured by the percentage change in the trailing twelve month average of the total value of goods exchanged each month between the two countries.

Combined Value of U.S. Exports to China and U.S. Imports from China, January 2017 - Januar 2024

Measured this way, we can identify the point in time at which 2020's coronavirus pandemic would have impacted trade between the two countries, allowing us to visually compare the periods where only the results of the trade policies of the two candidates affected it. The data is clear, President Biden's anti-free trade policies have had a more negative impact than those of President Trump.

It's not just China either. As the final chart shows, trade between the United States and the rest of the world has been much more negative as a result of President Biden's anti-free trade policies than they were under President Trump.

Combined Value of U.S. Exports to China and U.S. Imports to World (With and Without China), January 2017 - January 2024

Despite Trump's tariff policies, trade between the U.S. and the rest of the world increased during his term in office aside from the period affected by the Coronavirus Pandemic. Under President Biden's trade policy, trade between the U.S. and the rest of the world has been shrinking.

We don't plan to wade any deeper into the U.S. political waters of 2024 than we have, except to observe that neither candidate is good on trade and of the two, one has been unquestionably worse than the other. That candidate is not Donald Trump, which is a sentence we never thought we'd ever be writing, but here we are.

We're looking forward to going back to mostly ignoring the already too-long election campaign.

References

U.S. Census Bureau. Trade in Goods with China. Last updated: 7 March 2024.

U.S. Census Bureau. Trade in Goods with World, Not Seasonally Adjusted. Last updated: 7 March 2024.

Image Credit: Microsoft Copilot Designer. Prompt: "Global trade routes".

Labels:

12 March 2024

Imagine pouring sand onto a pile, one grain at a time. As the individual particles of sand are added, the pile grows larger, but seems to be otherwise stable. But as time goes by, something changes within that seemingly stable system. Instead of settling on the surface of the pile, adding another grain of sand causes an avalanche as rivers of sand suddenly break loose and rush down the surface of the pile. Kind of like what you see happen in the following short video:

What's happening in this scenario is some very complex and chaotic physics. Millions of individual particles are dynamically interacting with each other to produce this effect after they reach what's called a self-organized criticality. Once they reach this point, instead of acting like a somewhat solid and stable object, the individual grains of stand start moving together and flow like a fluid. That change is called a phase transition and its something of a signature of a system that's affected by quantum dynamics.

Of all the inventions humans have created, the stock market perhaps comes closest to sharing the same quantum-like properties at play in a sand pile. Every day, people engage in millions if not billions of transactions and, when all is going well, the value of their investments rise over time. Metaphorically much like a pile of sand. Until....

There's a new paper, published earlier this year, that caught our attention because it focused on the quantum-like properties of stock prices to explain why they suddenly break from their stable rising pattern, with investors randomly reacting to new information, and start behaving chaotically instead, with investors rushing for the proverbial exits all at the same time.

Here's an excerpt from the introduction of paper published by Kwangwon Ahn, Linxiao Cong, Hanwool Jang, and Daniel Sungyeon Kim that gets into the work they did to describe that phenomenon using the tools of quantum physics:

Stock markets exhibit universal characteristics similar to physical systems with considerable interacting units, for which several microscopic models have been developed (Shalizi 2001; Lux and Marchesi 1999). For example, the return distribution presents pronounced tails that are thicker than those of the Gaussian distribution (Shalizi 2001; Lux 1996; Mantegna and Stanley 1995). Several models have been proposed that phenomenologically show fat-tail distributions induced by investors’ herding behavior (Banerjee 1993; Topol 1991). Furthermore, Cont and Bouchaud (2000), Orĺean (1995), Banerjee (1993), and Topol (1991) showed that market participants’ interactions through imitation can lead to large fluctuations in aggregate demand and heavy tails in the distribution of returns. This approach had been formalized as a power law exponent at the tail of the distribution with a smaller magnitude associated with stronger herding behavior in stock returns (Nirei et al. 2020; Gabaix et al. 2005; Plerou et al. 1999; Gopikrishnan et al. 1999), trading volumes (Gabaix et al. 2006; Gopikrishnan et al. 2000), and commodity returns (Joo et al. 2020), which have been empirically investigated. Another stream of literature theoretically explains the power law in firm size distribution (Ji et al. 2020; Luttmer 2007) and trading volume (Nirei et al. 2020). However, these studies are limited to providing a connection between the power law exponent and other external factors, such as the business cycles and economic uncertainty.

We contribute to literature by explaining the role of economic uncertainty as a bridge between business cycles and investors’ herding behavior. Specifically, we propose a parsimonious model that employs quantum mechanics as an intermediate step to obtain the final solution and justify the power law distribution in stock returns. We start with the Fokker–Planck (FP) equation to model the dynamics of stock return distribution and derive the Schrödinger equation for a particular external potential (Ahn et al. 2017). The form of the potential is postulated based on empirical evidence of the evolution of stock returns in the marketplace. The solution suggests the existence of a power law for the tail distribution of stock returns. This also predicts a positive association between business cycles and the power law exponent. Our model provides new insights into existing research that models stock prices using random walks (Bartiromo 2004; Ma et al. 2004), quantum oscillators (Ahn et al. 2017; Ye and Huang 2008), quantum wells (Pedram 2012; Zhang and Huang 2010), and quantum Brownian motions (Meng et al. 2016).

We provide further empirical evidence on whether herding behavior in stock returns is negatively associated with business cycles. Furthermore, business cycles, which are often used as proxies for economic growth, are closely related to economic uncertainty, whereby it is believed that recessions are accompanied by higher economic uncertainty (Bloom 2014). Moreover, greater economic uncertainty leads to higher levels of uncertainty in the stock market. With greater uncertainty in the stock market, investors are more likely to mimic others because increased information asymmetry leads to fewer investors having confidence in their valuations (Alhaj-Yaseen and Yau 2018; Park and Sabourian 2011; Devenow and Welch 1996), amplifying investors’ herding behavior in the tail. As hypothesized, we find that herding behavior is stronger during recessions than booms and that economic uncertainty causes significant herding behavior.

In normal circumstances, individual investors respond to the random onset of new information and their responses are generally not synchronized with each other. And why would they be? For example, Investor A may be seeking to build their portfolio of dividend stocks to provide income in their near retirement. Meanwhile, Investor B may be focused on investing in growth stocks because it will be years before they retire. Investor C, on the other hand, is excited to speculate and chases after the hottest stocks. Investors D through Z, and millions more, approach their investments differently and, most importantly, mostly at random with respect to each other.

But when recessionary conditions take hold, which have the potential to affect millions simultaneously, the onset of new information associated with that developing state and the decreased certainty of continued stability would appear to prompt investors to act more like a herd, which is one of their two main macro-level findings.

Their other main finding is that applying a quantum model to stock prices can explain the tendency of investors to cluster around the center but also allows for "local" herding among extreme investors. That sounds very much like what you get from a stock market whose variation is best described with a Lévy stable distribution, which came up when we recently described the day-to-day volatility of the S&P 500 since 3 January 1950.

Going beyond the paper, we should note that such herd behavior doesn't only arise with developing recessionary conditions. Since 2008, we've regularly documented how investors alter the trajectory of the S&P 500 in response to market moving news, such as changes in the expected timing of interest rate changes. These changes arising from the same phenomenon can drive either rising or falling stock prices depending on other underlying factors. Since many of these changes aren't tied to any significant changes in investor uncertainty, there are still big research opportunities in this area.

References

Ahn, K., Cong, L., Jang, H. et al. Business cycle and herding behavior in stock returns: theory and evidence. Financial Innovation 10, 6 (2024). DOI: 10.1186/s40854-023-00540-z. [PDF Document].

Helmrich, S., Arias, A., Lochead, G. et al. Signatures of self-organized criticality in an ultracold atomic gas. Nature 577, 481–486 (2020). DOI: 10.1038/s41586-019-1908-6.

Julian Léonard et al, Probing the onset of quantum avalanches in a many-body localized system, Nature Physics (2023). DOI: 10.1038/s41567-022-01887-3. [Related preprint PDF Document].

Labels: , ,

About Political Calculations

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!

Recent Posts

Indices, Futures, and Bonds

Closing values for previous trading day.

Most Popular Posts
Quick Index

Site Data

This site is primarily powered by:

This page is powered by Blogger. Isn't yours?

CSS Validation

Valid CSS!

RSS Site Feed

AddThis Feed Button

JavaScript

The tools on this site are built using JavaScript. If you would like to learn more, one of the best free resources on the web is available at W3Schools.com.

Other Cool Resources

Blog Roll

Market Links

Useful Election Data
Charities We Support
Shopping Guides
Recommended Reading
Recently Shopped

Seeking Alpha Certified

Archives