Archive for Opinions – Page 15

18 Sep

Fed rate cut is attempt to prevent recession without sending prices soaring

By Ryan Herzog, Gonzaga University 

The Federal Reserve on Sept. 17, 2025, cut its target interest rate as it shifts focus from fighting inflation to supporting the choppy labor market.

As financial markets expected, the Fed lowered rates a quarter point to a range of 4% to 4.25%, its first cut since December 2024.

The Fed’s decision to begin cutting rates comes as evidence mounts that the U.S. labor market is losing momentum. The headline unemployment rate has stayed steady at near record lows, but the underlying trends are more concerning.

At the same time, the fight against inflation is not over yet. While a cooling jobs market could lead to a recession, cutting rates too much could drive inflation higher.

So if you’re the Fed, what do you do?

I’m an economist who tracks labor market data and monetary policy, examining how changes in hiring, wages and unemployment influence the Federal Reserve’s efforts to steer the economy. There’s an incredibly large amount of data the Fed, investors, economists like me and many others use to understand the state of the economy – and much of it often tells conflicting stories.

Here are some the data points I’ve been following most closely to better understand where the U.S. economy might go from here – and the tough choices the Fed has to make.

Underlying trouble in the labor market

The labor market looks stable on the surface, but more granular data tells a different story.

The unemployment rate has remained close to historic lows at 4.3% as of August 2025, according to the U.S. Bureau of Labor Statistics.

But the number of long-term unemployed – people out of work for 27 weeks or longer – rose to 1.9 million in August, up 385,000 from a year earlier. These workers now make up 25.7% of all unemployed people, the highest share since February 2022. Persistent long-term joblessness often signals deeper cracks forming in the labor market.

At the same time, new claims for unemployment benefits are spiking. Initial claims for unemployment insurance – a leading indicator of labor market stress – jumped by 27,000 to 263,000 for the week ending Sept. 6, according to the U.S. Department of Labor. That’s the sharpest increase in months and well above economists’ forecasts. It suggests layoffs are becoming more common.

We also got news that past payroll growth was overstated. In a process the Bureau of Labor Statistics undertakes annually to double-check its data, the bureau recently revised its jobs data downward from April 2024 through March 2025 by 911,000. In other words, the economy created roughly 75,000 fewer jobs per month than previously reported. This implies the labor market was weaker than it appeared all along.

Finally, workers are losing confidence. The Federal Reserve Bank of New York reported in August that the confidence of people who lost their jobs in finding another fell to its lowest level – 44.9% – since it started surveying consumers in June 2013. That’s another sign workers are feeling less secure about their prospects.

Taken together, these data points paint a clear picture: The labor market is not collapsing, but it is softening. That helps explain why the Fed is beginning to cut rates now – hoping to stimulate spending – before the job market breaks more sharply.

Tariffs are complicating the inflation data

Even as the labor market softens, tariffs are pushing certain prices higher than they otherwise would be, complicating the Federal Reserve’s effort to bring inflation down.

Government data shows that businesses have begun passing the costs of President Donald Trump’s new import tariffs to consumers. In August, clothing prices rose 0.5% and grocery prices rose 0.6%, with especially strong gains for tariff-sensitive items such as coffee.

Lower-income households are getting hit hardest because they spend more of their budget on imported goods, which tend to be the lower-cost items most affected by tariffs. A report from the Yale Budget Lab found that core goods prices are about 1.9% above pre-2025 trends as tariffs raise costs for basic items such as appliances and electronics.

Phillip Swagel, director of the Congressional Budget Office, said recently that Trump’s tariffs have pushed inflation higher than CBO analysts had expected, even as overall economic activity has weakened since January.

Typically, a slowdown in the labor market is met with slower inflation. But while the CBO now projects that the tariffs will reduce the federal budget deficit by about US$4 trillion over the next decade – roughly $3.3 trillion in new revenue and $700 billion in lower debt service costs – but it will come at the cost of near-term upward pressure on prices.

This creates a difficult balancing act for the Fed: Cut rates too quickly, and tariff-driven price pressures could reignite inflation; move too slowly, and the softening labor market could tip into recession.

A narrow path to a soft landing

As it resumes cutting rates, the Federal Reserve is trying to thread a narrow needle – easing policy enough to keep the labor market from cracking while not reigniting inflation, which is proving stickier in part because of tariffs.

Markets are betting the Fed will keep cutting. The futures market is betting the Fed will cut rates by another half point by the end of the year. And the one-year Treasury yield has dropped about 150 basis points (1.5%) since June, signaling that investors expect a series of rate cuts through 2025 and into 2026.

At its latest meeting, the Fed signaled two more rate cuts in 2025 and at least one rate cut in 2026.

Such cuts would ultimately bring the federal funds rate closer to 3% and hopefully reduce 30-year mortgage rates to around 5% – from an average of 6.35% as of Sept. 11. If the labor market continues to weaken – with jobless claims climbing, payrolls revised down and more workers stuck in long-term unemployment – that expectation will likely harden into consensus.

But the path is far from certain. Cutting rates too quickly could cause inflation to spike, while going too slow could lead to further deterioration in the labor market. Either outcome would jeopardize the Fed’s credibility – whether by appearing unable to control prices or by allowing unemployment to rise unnecessarily. That would undermine its ability to influence markets and enforce its dual mandate of maximum employment and stable prices.

Another tricky issue is Trump’s public campaign to push the Fed to cut rates – appearing to do his bidding could also undercut Fed credibility. For what it’s worth, the Sept. 17 rate cut appears driven less by politics than by economic data. The Fed itself was projecting a year ago that rates would be much lower today than they actually are, suggesting it’s been following the data.

The economy appears to be slowing but remains resilient, which is why the Fed is likely to move gradually. The risk is that the window for a soft landing is closing. The coming months will determine whether the Fed can ease early enough to avoid recession, or whether it has already waited too long.The Conversation

About the Author:

Ryan Herzog, Associate Professor of Economics, Gonzaga University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Economics & Fundamentals Opinions
16 Sep

Fed, under pressure to cut rates, tries to balance labor market and inflation – while avoiding dreaded stagflation

By Jason Reed, University of Notre Dame 

The Federal Reserve is in a nearly impossible spot right now.

Markets are expecting a quarter-point interest rate cut to a range of 4% to 4.25% when the Fed policy-setting committee concludes its latest meeting on Sept. 17, 2025. After all, the slowdown in the jobs market, as well as a massive revision to past figures showing close to a million fewer jobs were created than previously reported, makes a strong case for lower interest rates to shore up the economy.

But at the same time, inflation – the other component of the Fed’s dual mandate – has begun to accelerate again. As rising tariffs squeeze consumer spending in sectors exposed to the harshest tariffs – such as clothing and electronics – other inflationary pressures loom over the horizon.

A slowing economy or rising inflation is a circumstance that policymakers want to avoid. But as an economist and finance professor, I’m increasingly concerned about the risk that they happen at the same time – a horrible economic condition known as stagflation – and that the Fed may be too slow in responding.

Between a rock and a hard data point

The Fed has been under pressure to cut rates for some time – including from President Donald Trump.

The reason markets and the White House are so interested is because what the Fed does matters. The central bank’s decision at its near-monthly meetings helps banks and other lenders to determine rates on auto loans, mortgages, credit cards and more. Lower rates usually lead more businesses and consumers to borrow and spend more, boosting economic activity. This also can drive up inflation.

For the better part of three years, the central bank has been focused on its generational fight against inflation. But now, with inflation down significantly from its 40-year high of 9% reached in 2022 and the jobs market sputtering, conditions finally seemed right to resume cutting rates.

The labor market has seen continued deterioration, most notably with the Bureau of Labor Statistics’ revisions to nonfarm payrolls – in effect reducing the number of jobs economists thought the U.S. gained by almost 1 million for the year ending in March 2025.

But a recent uptick in inflation has made the Fed’s call more complicated.

Over the past four months, the consumer price index has consistently ticked up, with the most recent CPI figure indicating year-over-year inflation of 2.9% – well above the Fed’s target of 2%.

Switching focus to jobs

At the Fed’s last meeting in August, Chair Jerome Powell said that the risks to the labor market now exceed the risks of inflation.

For example, for the first time since 2021, the number of unemployed people have outpaced job vacancies as companies have moved to eliminate open positions before laying off workers.

Most compelling is the so-called U6 unemployment rate – which includes those in the regular unemployment figures and people who have stopped looking for jobs, as well as those who are working part time but are looking for full-time opportunities. That has increased over the past three months to 8.1%.

The evidence suggests that businesses are reluctant to add workers as tariff policy and broad economic uncertainty appear to drive hiring decisions.

The worst of both worlds

The short-term risk here is that a quarter-point cut won’t be enough to shore up the jobs market, and it may be too late to prevent the economy from tipping into recession.

The longer-term risk is more concerning: Not only could the economy contract, but it could do so while inflation accelerates.

The last time the U.S. experienced stagflation was in the 1970s, when an oil embargo caused the price of crude to double. This drove up inflation while causing unemployment to soar and the economy to stall. Policies aimed at reducing inflation typically exacerbate slowing growth, and vice versa. In other words, there were fewer dollars to go around – and those dollars were worth a little less every day.

The pain experienced during this previous bout of stagflation convinced a generation of economists and policymakers that the condition was to be avoided at all costs.

The Fed, which has consistently shown its hand and has guided the markets toward this week’s rate cut, now has to make what seems like an impossible decision: cut rates even if doing so will add inflationary pressures.

And there are other potential headwinds for the U.S. economy. For example, it has yet to fully absorb the impact of Trump’s immigration crackdown on productivity and output due to the loss of workers. Waning consumer confidence suggests consumer spending could soon drop. And a potential federal government shutdown looms in September.

In my view, it’s clear that a cut is warranted. But will it drive up inflation? Economists like me will be watching this closely.The Conversation

About the Author:

Jason Reed, Associate Teaching Professor of Finance, University of Notre Dame

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Economics & Fundamentals Opinions
16 Sep

Even professional economists can’t escape political bias

By Aeimit Lakdawala, Wake Forest University 

Republican-leaning economists tend to predict stronger economic growth when a Republican is president than Democrats do – and because of this partisan optimism, their forecasts end up being less accurate.

I’m an economist, and my colleagues and I found this by analyzing nearly 40 years of responses to The Wall Street Journal’s Economic Forecasting Survey. Unlike most such surveys, the Journal publishes each forecaster’s name, allowing us to link their predictions to their political affiliations.

The respondents were professional economists at major banks, consulting firms and universities whose forecasts help guide financial markets and business decisions. Out of more than 300 economists in our sample, we could identify the political affiliations of 122. We did this by looking at the forecasters’ political donation records, voter registration data and work histories with partisan groups.

The pattern was striking: Republican forecasters systematically predicted higher gross domestic product growth when their party controlled the presidency, representing roughly 10% to 15% of average growth rates during our study period.

When we examined forecast accuracy using real-time GDP data, Republican forecasters made larger errors when their preferred party held office. This suggests partisan optimism makes their professional judgment worse.

What makes this finding particularly notable is its asymmetry. The partisan gap emerged specifically during Republican presidencies. Under Democratic Presidents Bill Clinton, Barack Obama and Joe Biden, Republican and Democratic forecasters made virtually identical predictions. That wasn’t the case when George W. Bush, and later Donald Trump, occupied the White House.

Interestingly, this bias appears only in GDP forecasts. When we analyzed predictions for inflation, unemployment and interest rates, we found no systematic differences between Republican and Democratic forecasters.

That makes sense, because GDP forecasts are inherently more uncertain than other economic predictions. Professional forecasters tend to disagree more and make more mistakes when predicting GDP compared to inflation or unemployment rates. This creates opportunities for partisan ideologies to sneak in.

We traced the bias to different views about the effectiveness of tax policies. Using Google Trends data to measure when tax cuts were in the news, we found Republican forecasters become systematically more optimistic precisely when tax policy discussions heat up.

Why it matters

Previous research has found that most people have a strong partisan bias when they make economic predictions. Our work is the first to show that professional economists can also succumb to such influences – despite their training and market incentives to be accurate.

Their errors can come at a high price. Financial markets, policymakers and businesses rely on economists’ forecasts to make major decisions. When the Federal Reserve sets interest rates, when companies plan investments and when investors allocate portfolios, they often reference these professional consensus forecasts.

Our research challenges a common assumption in economics: that aggregating diverse expert forecasts eliminates individual biases and improves accuracy.

This doesn’t mean professional forecasters are incompetent or dishonest. These are highly trained economists with strong financial incentives for accuracy. Rather, our findings reveal how even experts with the best intentions can be unconsciously influenced by their own ideological beliefs – especially when dealing with inherently uncertain data.

What still isn’t known

Several important questions remain unanswered. It’s unclear how this bias might be reduced. Would making forecasters more aware of their political leanings help reduce the effect? Or would developing new forecasting methods that weight predictions based on historical accuracy during different political regimes improve consensus forecasts?

We’re also curious whether institutional factors matter. Might forecasters at institutions with explicit political diversity policies show less bias? How do international forecasters viewing the U.S. economy compare to domestic ones?

Finally, our research focuses on U.S. forecasters during a period of increasing political polarization. Whether similar patterns emerge in other countries with different political systems, or during less polarized times, remains an open question.

The Research Brief is a short take on interesting academic work.The Conversation

About the Author:

Aeimit Lakdawala, Associate Professor of Economics, Wake Forest University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Economics & Fundamentals Opinions
14 Sep

Book Review: Hands-On AI Trading with Python, QuantConnect and AWS

AI is all the rage these days. We know this! But as investors and traders, do we know how to incorporate AI into our systems? Do we even know the many possible ways we could use AI to help our trading? Well, today I am going to do bring something a little bit different to the blog, a quick book review!

As a Python coder, automated trader and investor, I feel constantly bombarded with bits and pieces of AI trading information from newsletters or ‘how to’ tutorials to implement this or that. Luckily, I was recently given a complimentary copy of Hands-On AI Trading with Python, QuantConnect and AWS and, it turns out, this book is a comprehensive guide that brings a whole lot of information into one place with a consistent presentation and coding style.

Front cover of Hands-On AI Trading

Basic Information:

This book was written by five active data-driven market professionals that all run businesses or have positions that are aligned to the financial markets and/or using AI and automated solutions. Jiri Pik is the CEO of RocketEdge.com, Jared Broad is the founder and CEO of QuantConnect, Ernest Chan is the founder of PredictNow.AI, Philip Sun is the CEO of Adaptive Investment Solutions and Vivek Singh previously worked at a hedge fund and is now a senior product manager at AWS.

This book is targeted towards those in finance, aspiring quants, veteran quants, hedge fund traders, as well as independent traders & investors. As you can tell from the book’s title, there’s a focus on using the Python programming language as well as the services of QuantConnect, Amazon Web Services (AWS), and Predictnow.ai.

The authors present these specific tools (QuantConnect, AWS, Predictnow.ai) as a tech-stack to get things from start to finish. As stated in the book, the goal was to provide, “an easy-to-setup and use environment where readers could instantly experiment with the algorithms to build their confidence without spending any time setting up the required infrastructure.” In other words, the reader has an opportunity to go from the learning, creating and testing phase (with code and AI models) to potentially working through to a live strategy trading (through QuantConnect and their connected brokers).

I found the book to be well organized and it is structured into 3 main parts.

Part 1 is about the Capital Markets and Quantitative Trading.

Part one quickly brings those unfamiliar with the financial markets up to speed. It covers various topics from the different types of markets traded to the mechanics of how things work in the market ecosystem. This includes all the different types of participants, the different roles they play, the different types of orders these traders use as well as who has unique types of informed access. The authors go further through derivatives, futures, charting, crypto and more.

The quantitative analysis and trading part of this section brings a comprehensive overview of quantitative trader functions using QuantConnect and Python code. It details the steps, processes, and aspects that quants will go through, experience and need to consider for a successful process. I think this section will be very beneficial for aspiring and seasoned quant traders alike, as this book does a great job of laying out the market framework and the quantitative trading landscape.

ai python trading image

Image from example in Hands-On AI Trading.

Part 2 goes into AI and Machine Learning (ML) in Algorithmic Trading.

Part two focuses on AI-based algorithmic trading. Here, you start to address the market prediction, forecasting or other specific problems you’re trying to solve. You proceed step by step, breaking down issues and finding solutions using AI and machine learning processes. It details the data set preparation, handling data, creating features, and splitting datasets into training and testing phases.

If you are unfamiliar with AI models – this section (especially Chapter 4) is for you as it delves into models like linear regression, Markov, Bayes, decision trees, support vector machines, neural networks, and many more. Found alongside these characteristics and concepts is the Python code you can use for these different types of quant functions.

Part 3 delves into Advanced Applications of AI in Trading and Risk Management.

Finally, part three discusses using these AI models in real trading and investing scenarios. The authors provide 19 specific examples and this is where I think the main strength of this book lies. These examples illustrate different aspects of the investment game or problems that are solved using various AI models for major financial markets (FX, stocks, etc.). These examples, once understood, ideally can form the basis for many new ideas, as well as just understanding how these pros go about it. Also, the Python code is included for these examples.

For instance, one of my favorite examples (#8) was just a simple exercise in using a stop-loss based on historical volatility (and drawdown recovery). This example used a LASSO regression model with features including the VIX, Average True Range (of n months) and Standard Deviation (of n months). The example used a few different methods to test variations of a dynamic stop-loss order to varying degrees of success. This type of example represents a common problem most traders come into when working through their strategies.

The examples also give interesting ideas on how to use AI and models in use cases beyond just trying to predict future price returns.

Overall Takeaway: 

I thought this book was well done and is the best book that bridges quant trading and AI together that I have read so far. I think a lot of the AI and machine learning aspects were explained and guided in a clear, concise, and a well-organized way, since it’s very easy to get lost in the weeds with this subject.

The breadth of coverage among these many strategies, concepts, and factors involved is admirable, covering all the way from data acquisition and programming to the role of generative AI. There’s a lot to unpack. There’s a lot to learn. I think it’s a testament to the authors that they created a book that covers so much. There’s also a github repository for the examples.

I would recommend this book for any aspiring quant traders or programmers, or anyone who is interested in the understanding of these markets, especially in how quant trading and AI intersect. I would also recommend it for traders looking for examples of AI in trading or finding new ideas to implement AI strategies.

Disclaimer: Complimentary book copy was provided by Wiley.

Article written by Zac@InvestMacro

 

Opinions Programming
14 Sep

Large Speculators push Mexican Peso Bets to a 65-week high

By InvestMacro

Speculators OI FX Futures COT Chart

Here are the latest charts and statistics for the Commitment of Traders (COT) data published by the Commodities Futures Trading Commission (CFTC).

The latest COT data is updated through Tuesday September 9th and shows a quick view of how large market participants (for-profit speculators and commercial traders) were positioned in the futures markets. All currency positions are in direct relation to the US dollar where, for example, a bet for the euro is a bet that the euro will rise versus the dollar while a bet against the euro will be a bet that the euro will decline versus the dollar.

Weekly Speculator Changes led by Brazilian Real & Japanese Yen

Speculators Nets FX Futures COT Chart
The COT currency market speculator bets were overall higher this week as seven out of the eleven currency markets we cover had higher positioning while the other four markets had lower speculator contracts.

Leading the gains for the currency markets was the Brazilian Real (22,918 contracts) with the Japanese Yen (18,385 contracts), the EuroFX (6,085 contracts), the Australian Dollar (3,452 contracts), the Mexican Peso (719 contracts), Bitcoin (434 contracts) and the Canadian Dollar (59 contracts) also showing positive weeks.

The currencies seeing declines in speculator bets on the week were the Swiss Franc (-2,951 contracts), the New Zealand Dollar (-2,269 contracts), the US Dollar Index (-537 contracts) and with the British Pound (-465 contracts) also registering lower bets on the week.

Large Speculators push Mexican Peso Bets to a 65-week high

Highlighting the currency speculator positions this week is the increasingly bullish Mexican Peso sentiment. Speculators boosted their bullish bets for the Mexican Peso this week for a fourth consecutive week and for the seventh time out of the past eight weeks. The Peso position has now added +23,610 net contracts over these past eight weeks and this bullish momentum has brought the current net standing to a total of +73,732 net bullish contracts.

This marks the highest level for the Mexican Peso contracts in the past 65 weeks, dating back to June 11th of 2024 when the net position was last over +100,000 contracts. The Peso positioning was strong throughout the first half of 2024 with 15 consecutive weeks of contracts over the +100,000 contract level. Peso bets then started to cool off mid-2024 and steadily decreased to an overall negative net position on January 21st of 2025. Since then, contracts have rebounded, improved and increased to this week’s 65-week high.

Despite the increased bullishness for the Peso, the strength score is still just modestly high at 66% of its three-year range. This shows that if bullish momentum continues, there is plenty of room to go before a bullish extreme is reached.

The Mexican Peso position in the exchange markets versus the US dollar has been on an uptrend since the beginning of the year. The Peso has risen through its 200-week moving average and is currently up 13.10% against the US Dollar so far.

Bitcoin leads Price Performance for the Week

The currency market price performance this week was led by Bitcoin, which rose by 4.41% for the past 5 days. Bitcoin is now up by 13% over the last 90 days.

Next was the Australian Dollar, which increased by 1.50% this week. The Australian Dollar is up by 3% over the last 30 days and is higher by 3.74% over the last 90 days. The Mexican Peso came in next with a 1.47% gain and the Peso has risen by 5.57% over the past 90 days.

The New Zealand Dollar advanced by 1.16% over the last five days. The Brazilian Real saw a gain of 0.86% while the Real is now up by approximately 6% in the last 90 days.

The British Pound Sterling came in next with a 0.45% rise, followed by the Swiss Franc, which was higher by 0.26%. The Euro saw prices edge up by just 0.16% and the Canadian Dollar was virtually unchanged with a 0.05% advance.

On the downside, the US Dollar Index was virtually unchanged, down by -0.07%, while the Japanese Yen was just a tick lower with a -0.09% decline.

Currencies Data:

Speculators FX Futures COT Data Table
Legend: Open Interest | Speculators Current Net Position | Weekly Specs Change | Specs Strength Score compared to last 3-Years (0-100 range)

Strength Scores led by Brazilian Real & EuroFX

Speculators Strength Scores FX Futures COT Chart
COT Strength Scores (a normalized measure of Speculator positions over a 3-Year range, from 0 to 100 where above 80 is Extreme-Bullish and below 20 is Extreme-Bearish) showed that the Brazilian Real (90 percent) and the EuroFX (77 percent) lead the currency markets this week. The Japanese Yen (76 percent), Mexican Peso (66 percent) and the New Zealand Dollar (54 percent) come in as the next highest in the weekly strength scores.

On the downside, the US Dollar Index (4 percent) and the British Pound (11 percent) come in at the lowest strength levels currently and are in Extreme-Bearish territory (below 20 percent). The next lowest strength scores are the Australian Dollar (20 percent).

3-Year Strength Statistics:
US Dollar Index (3.7 percent) vs US Dollar Index previous week (5.0 percent)
EuroFX (76.6 percent) vs EuroFX previous week (74.3 percent)
British Pound Sterling (10.8 percent) vs British Pound Sterling previous week (11.0 percent)
Japanese Yen (75.9 percent) vs Japanese Yen previous week (70.8 percent)
Swiss Franc (42.5 percent) vs Swiss Franc previous week (48.4 percent)
Canadian Dollar (43.1 percent) vs Canadian Dollar previous week (43.1 percent)
Australian Dollar (20.1 percent) vs Australian Dollar previous week (17.6 percent)
New Zealand Dollar (54.4 percent) vs New Zealand Dollar previous week (57.0 percent)
Mexican Peso (66.4 percent) vs Mexican Peso previous week (66.0 percent)
Brazilian Real (90.1 percent) vs Brazilian Real previous week (71.5 percent)
Bitcoin (42.8 percent) vs Bitcoin previous week (33.6 percent)

Brazilian Real & Bitcoin top the 6-Week Strength Trends

Speculators Trends FX Futures COT Chart
COT Strength Score Trends (or move index, calculates the 6-week changes in strength scores) showed that the Brazilian Real (26 percent) and Bitcoin (11 percent) lead the past six weeks trends for the currencies. The Mexican Peso (9 percent) was the next highest positive mover in the 3-Year trends data.

The Canadian Dollar (-16 percent) leads the downside trend scores currently with the British Pound (-11 percent), Swiss Franc (-10 percent) and the New Zealand Dollar (-8 percent) following next with lower trend scores.

3-Year Strength Trends:
US Dollar Index (-3.5 percent) vs US Dollar Index previous week (-3.9 percent)
EuroFX (0.9 percent) vs EuroFX previous week (-2.3 percent)
British Pound Sterling (-11.0 percent) vs British Pound Sterling previous week (-17.1 percent)
Japanese Yen (0.7 percent) vs Japanese Yen previous week (-9.2 percent)
Swiss Franc (-9.7 percent) vs Swiss Franc previous week (0.4 percent)
Canadian Dollar (-16.0 percent) vs Canadian Dollar previous week (-19.1 percent)
Australian Dollar (-0.8 percent) vs Australian Dollar previous week (-1.0 percent)
New Zealand Dollar (-7.7 percent) vs New Zealand Dollar previous week (-3.8 percent)
Mexican Peso (8.7 percent) vs Mexican Peso previous week (8.6 percent)
Brazilian Real (26.1 percent) vs Brazilian Real previous week (5.9 percent)
Bitcoin (11.4 percent) vs Bitcoin previous week (20.1 percent)

Individual COT Forex Markets:

US Dollar Index Futures:

US Dollar Index Forex Futures COT ChartThe US Dollar Index large speculator standing this week reached a net position of -5,558 contracts in the data reported through Tuesday. This was a weekly lowering of -537 contracts from the previous week which had a total of -5,021 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish-Extreme with a score of 3.7 percent. The commercials are Bullish-Extreme with a score of 99.8 percent and the small traders (not shown in chart) are Bearish-Extreme with a score of 15.6 percent.

Price Trend-Following Model: Weak Downtrend

Our weekly trend-following model classifies the current market price position as: Weak Downtrend.

US DOLLAR INDEX StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:50.534.96.8
– Percent of Open Interest Shorts:65.117.59.7
– Net Position:-5,5586,642-1,084
– Gross Longs:19,19213,2882,604
– Gross Shorts:24,7506,6463,688
– Long to Short Ratio:0.8 to 12.0 to 10.7 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):3.799.815.6
– Strength Index Reading (3 Year Range):Bearish-ExtremeBullish-ExtremeBearish-Extreme
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-3.56.4-18.9

 

Euro Currency Futures:

Euro Currency Futures COT ChartThe Euro Currency large speculator standing this week reached a net position of 125,677 contracts in the data reported through Tuesday. This was a weekly advance of 6,085 contracts from the previous week which had a total of 119,592 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 76.6 percent. The commercials are Bearish-Extreme with a score of 20.0 percent and the small traders (not shown in chart) are Bullish-Extreme with a score of 88.4 percent.

Price Trend-Following Model: Uptrend

Our weekly trend-following model classifies the current market price position as: Uptrend.

EURO Currency StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:29.453.411.4
– Percent of Open Interest Shorts:15.173.95.3
– Net Position:125,677-179,64753,970
– Gross Longs:258,049468,861100,417
– Gross Shorts:132,372648,50846,447
– Long to Short Ratio:1.9 to 10.7 to 12.2 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):76.620.088.4
– Strength Index Reading (3 Year Range):BullishBearish-ExtremeBullish-Extreme
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:0.9-1.33.3

 

British Pound Sterling Futures:

British Pound Sterling Futures COT ChartThe British Pound Sterling large speculator standing this week reached a net position of -33,605 contracts in the data reported through Tuesday. This was a weekly reduction of -465 contracts from the previous week which had a total of -33,140 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish-Extreme with a score of 10.8 percent. The commercials are Bullish with a score of 78.9 percent and the small traders (not shown in chart) are Bullish with a score of 70.2 percent.

Price Trend-Following Model: Strong Uptrend

Our weekly trend-following model classifies the current market price position as: Strong Uptrend.

BRITISH POUND StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:23.257.110.4
– Percent of Open Interest Shorts:33.647.99.2
– Net Position:-33,60529,7843,821
– Gross Longs:74,849184,37033,639
– Gross Shorts:108,454154,58629,818
– Long to Short Ratio:0.7 to 11.2 to 11.1 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):10.878.970.2
– Strength Index Reading (3 Year Range):Bearish-ExtremeBullishBullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-11.07.110.8

 

Japanese Yen Futures:

Japanese Yen Forex Futures COT ChartThe Japanese Yen large speculator standing this week reached a net position of 91,643 contracts in the data reported through Tuesday. This was a weekly increase of 18,385 contracts from the previous week which had a total of 73,258 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 75.9 percent. The commercials are Bearish with a score of 25.6 percent and the small traders (not shown in chart) are Bullish with a score of 63.2 percent.

Price Trend-Following Model: Downtrend

Our weekly trend-following model classifies the current market price position as: Downtrend.

JAPANESE YEN StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:44.038.410.6
– Percent of Open Interest Shorts:21.762.98.5
– Net Position:91,643-100,4428,799
– Gross Longs:180,724157,85443,537
– Gross Shorts:89,081258,29634,738
– Long to Short Ratio:2.0 to 10.6 to 11.3 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):75.925.663.2
– Strength Index Reading (3 Year Range):BullishBearishBullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:0.7-2.012.9

 

Swiss Franc Futures:

Swiss Franc Forex Futures COT ChartThe Swiss Franc large speculator standing this week reached a net position of -28,839 contracts in the data reported through Tuesday. This was a weekly fall of -2,951 contracts from the previous week which had a total of -25,888 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 42.5 percent. The commercials are Bearish with a score of 49.7 percent and the small traders (not shown in chart) are Bullish with a score of 70.4 percent.

Price Trend-Following Model: Uptrend

Our weekly trend-following model classifies the current market price position as: Uptrend.

SWISS FRANC StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:6.674.116.5
– Percent of Open Interest Shorts:34.144.818.4
– Net Position:-28,83930,815-1,976
– Gross Longs:6,98977,87417,332
– Gross Shorts:35,82847,05919,308
– Long to Short Ratio:0.2 to 11.7 to 10.9 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):42.549.770.4
– Strength Index Reading (3 Year Range):BearishBearishBullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-9.74.19.7

 

Canadian Dollar Futures:

Canadian Dollar Forex Futures COT ChartThe Canadian Dollar large speculator standing this week reached a net position of -108,917 contracts in the data reported through Tuesday. This was a weekly advance of 59 contracts from the previous week which had a total of -108,976 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 43.1 percent. The commercials are Bullish with a score of 60.9 percent and the small traders (not shown in chart) are Bearish with a score of 26.5 percent.

Price Trend-Following Model: Weak Uptrend

Our weekly trend-following model classifies the current market price position as: Weak Uptrend.

CANADIAN DOLLAR StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:7.577.210.1
– Percent of Open Interest Shorts:51.231.112.6
– Net Position:-108,917115,041-6,124
– Gross Longs:18,704192,46525,216
– Gross Shorts:127,62177,42431,340
– Long to Short Ratio:0.1 to 12.5 to 10.8 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):43.160.926.5
– Strength Index Reading (3 Year Range):BearishBullishBearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-16.016.6-15.1

 

Australian Dollar Futures:

Australian Dollar Forex Futures COT ChartThe Australian Dollar large speculator standing this week reached a net position of -79,231 contracts in the data reported through Tuesday. This was a weekly advance of 3,452 contracts from the previous week which had a total of -82,683 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 20.1 percent. The commercials are Bullish with a score of 73.6 percent and the small traders (not shown in chart) are Bullish with a score of 64.9 percent.

Price Trend-Following Model: Strong Uptrend

Our weekly trend-following model classifies the current market price position as: Strong Uptrend.

AUSTRALIAN DOLLAR StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:15.567.812.1
– Percent of Open Interest Shorts:53.532.69.2
– Net Position:-79,23173,2216,010
– Gross Longs:32,200141,05125,129
– Gross Shorts:111,43167,83019,119
– Long to Short Ratio:0.3 to 12.1 to 11.3 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):20.173.664.9
– Strength Index Reading (3 Year Range):BearishBullishBullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-0.8-1.17.8

 

New Zealand Dollar Futures:

New Zealand Dollar Forex Futures COT ChartThe New Zealand Dollar large speculator standing this week reached a net position of -8,743 contracts in the data reported through Tuesday. This was a weekly lowering of -2,269 contracts from the previous week which had a total of -6,474 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 54.4 percent. The commercials are Bearish with a score of 46.3 percent and the small traders (not shown in chart) are Bearish with a score of 26.8 percent.

Price Trend-Following Model: Downtrend

Our weekly trend-following model classifies the current market price position as: Downtrend.

NEW ZEALAND DOLLAR StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:21.653.25.1
– Percent of Open Interest Shorts:33.838.08.0
– Net Position:-8,74310,841-2,098
– Gross Longs:15,47938,0653,637
– Gross Shorts:24,22227,2245,735
– Long to Short Ratio:0.6 to 11.4 to 10.6 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):54.446.326.8
– Strength Index Reading (3 Year Range):BullishBearishBearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-7.710.9-38.5

 

Mexican Peso Futures:

Mexican Peso Futures COT ChartThe Mexican Peso large speculator standing this week reached a net position of 73,732 contracts in the data reported through Tuesday. This was a weekly gain of 719 contracts from the previous week which had a total of 73,013 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 66.4 percent. The commercials are Bearish with a score of 34.4 percent and the small traders (not shown in chart) are Bearish with a score of 43.8 percent.

Price Trend-Following Model: Uptrend

Our weekly trend-following model classifies the current market price position as: Uptrend.

MEXICAN PESO StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:50.441.73.3
– Percent of Open Interest Shorts:15.878.11.5
– Net Position:73,732-77,5353,803
– Gross Longs:107,50389,0647,051
– Gross Shorts:33,771166,5993,248
– Long to Short Ratio:3.2 to 10.5 to 12.2 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):66.434.443.8
– Strength Index Reading (3 Year Range):BullishBearishBearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:8.7-9.02.1

 

Brazilian Real Futures:

Brazil Real Futures COT ChartThe Brazilian Real large speculator standing this week reached a net position of 56,087 contracts in the data reported through Tuesday. This was a weekly boost of 22,918 contracts from the previous week which had a total of 33,169 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bullish-Extreme with a score of 90.1 percent. The commercials are Bearish-Extreme with a score of 8.4 percent and the small traders (not shown in chart) are Bearish with a score of 43.5 percent.

Price Trend-Following Model: Strong Uptrend

Our weekly trend-following model classifies the current market price position as: Strong Uptrend.

BRAZIL REAL StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:62.932.04.5
– Percent of Open Interest Shorts:10.787.90.8
– Net Position:56,087-60,0984,011
– Gross Longs:67,64134,4054,856
– Gross Shorts:11,55494,503845
– Long to Short Ratio:5.9 to 10.4 to 15.7 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):90.18.443.5
– Strength Index Reading (3 Year Range):Bullish-ExtremeBearish-ExtremeBearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:26.1-26.55.4

 

Bitcoin Futures:

Bitcoin Crypto Futures COT ChartThe Bitcoin large speculator standing this week reached a net position of -468 contracts in the data reported through Tuesday. This was a weekly advance of 434 contracts from the previous week which had a total of -902 net contracts.

This week’s current strength score (the trader positioning range over the past three years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 42.8 percent. The commercials are Bullish with a score of 57.9 percent and the small traders (not shown in chart) are Bullish with a score of 58.7 percent.

Price Trend-Following Model: Uptrend

Our weekly trend-following model classifies the current market price position as: Uptrend.

BITCOIN StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:85.74.04.7
– Percent of Open Interest Shorts:87.43.83.2
– Net Position:-46856412
– Gross Longs:23,0751,0811,263
– Gross Shorts:23,5431,025851
– Long to Short Ratio:1.0 to 11.1 to 11.5 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):42.857.958.7
– Strength Index Reading (3 Year Range):BearishBullishBullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:11.4-10.6-3.4

 

Article By InvestMacro – Receive our weekly COT Newsletter

*COT Report: The COT data, released weekly to the public each Friday, is updated through the most recent Tuesday (data is 3 days old) and shows a quick view of how large speculators or non-commercials (for-profit traders) were positioned in the futures markets.

The CFTC categorizes trader positions according to commercial hedgers (traders who use futures contracts for hedging as part of the business), non-commercials (large traders who speculate to realize trading profits) and nonreportable traders (usually small traders/speculators) as well as their open interest (contracts open in the market at time of reporting). See CFTC criteria here.

COT Updates Forex and Currency News Opinions
13 Sep

Speculator Extremes: MSCI EAFE-Mini & Lean Hogs lead weekly Bullish Positions

By InvestMacro 

The latest update for the weekly Commitment of Traders (COT) report was released by the Commodity Futures Trading Commission (CFTC) on Friday for data ending on September 9th.

This weekly Extreme Positions report highlights the Most Bullish and Most Bearish Positions for the speculator category. Extreme positioning in these markets can foreshadow strong moves in the underlying market.

To signify an extreme position, we use the Strength Index (also known as the COT Index) of each instrument, a common method of measuring COT data. The Strength Index is simply a comparison of current trader positions against the range of positions over the previous 3 years. We use over 80 percent as extremely bullish and under 20 percent as extremely bearish. (Compare Strength Index scores across all markets in the data table or cot leaders table)

Extreme Bullish Speculator Table

Here Are This Week’s Most Bullish Speculator Positions:

MSCI EAFE MINI

Extreme Bullish Leader
The MSCI EAFE MINI speculator position continues to come in as the most bullish extreme standing again this week as the MSCI EAFE-Mini speculator level is currently at a 99 percent (just below the maximum 100%) score of its 3-year range.

The six-week trend for the percent strength score totaled a gain of 6 percentage points this week. The overall net speculator position was a total of 13,583 net contracts this week with a small rise of 891 contract in the weekly speculator bets.

Speculators or Non-Commercials Notes:

Speculators, classified as non-commercial traders by the CFTC, are made up of large commodity funds, hedge funds and other significant for-profit participants. The Specs are generally regarded as trend-followers in their behavior towards price action – net speculator bets and prices tend to go in the same directions. These traders often look to buy when prices are rising and sell when prices are falling. To illustrate this point, many times speculator contracts can be found at their most extremes (bullish or bearish) when prices are also close to their highest or lowest levels.

These extreme levels can be dangerous for the large speculators as the trade is most crowded, there is less trading ammunition still sitting on the sidelines to push the trend further and prices have moved a significant distance. When the trend becomes exhausted, some speculators take profits while others look to also exit positions when prices fail to continue in the same direction. This process usually plays out over many months to years and can ultimately create a reverse effect where prices start to fall and speculators start a process of selling when prices are falling.

 

Lean Hogs

Extreme Bullish Leader
The Lean Hogs speculator position comes next in the extreme standings this week. The Lean Hogs speculator level is now at a 96 percent score of its 3-year range.

The six-week trend for the percent strength score was an increase by 13 percentage points this week. The speculator position registered 91,584 net contracts this week with a weekly boost of 8,244 contracts in speculator bets.

Brazil Real

Extreme Bullish Leader
The Brazil Real speculator position comes in third this week in the extreme standings with the BRL speculator level residing at a 90 percent score of its 3-year range.

The six-week trend for the speculator strength score came in at an increase by 26 percentage points this week while the overall speculator position was 56,087 net contracts this week with a rise of 22,918 contracts in the weekly speculator bets.

Live Cattle

Extreme Bullish Leader
The Live Cattle speculator position comes up number four in the extreme standings this week. The Live Cattle speculator level is at a 83 percent score of its 3-year range.

The six-week trend for the speculator strength score saw no change this week. The overall speculator position was 106,678 net contracts this week with a decline of -3,557 contracts in the speculator bets.

Silver

Extreme Bullish Leader
The Silver speculator position rounds out the top five in this week’s bullish extreme standings. The Silver speculator level sits at a 82 percent score of its 3-year range. The six-week trend for the speculator strength score was a drop by -7 percentage points this week.

The speculator position was 53,937 net contracts this week with a decrease of -1,986 contracts in the weekly speculator bets.

The Most Bearish Speculator Positions of the Week:

Extreme Bearish Speculator Table

VIX

Extreme Bearish Leader
The VIX speculator position comes in tied as the most bearish extreme standing this week with the VIX speculator level is at a 0 percent score of its 3-year range.

The six-week trend for the speculator strength score was a drop by -34 percentage points this week. The overall speculator position was -107,810 net contracts this week with a dip of -858 contracts in the speculator bets.

Sugar

Extreme Bearish Leader
The Sugar speculator position comes in tied for the most bearish extreme standing on the week with the Sugar speculator level at a 0 percent score of its 3-year range.

The six-week trend for the speculator strength score was a decline by -18 percentage points this week. The speculator position was -139,610 net contracts this week with a sharp drop of -53,805 contracts in the weekly speculator bets.

WTI Crude Oil

Extreme Bearish Leader
The WTI Crude Oil speculator position comes in tied as the most bearish extreme standing of the week. The WTI Crude speculator level resides at a 0 percent score of its 3-year range.

The six-week trend for the speculator strength score was -28 percentage points this week. The overall speculator position was 81,844 net contracts this week with a reduction by -20,584 contracts in the speculator bets.

US Dollar Index

Extreme Bearish Leader
The US Dollar Index speculator position comes in as this week’s fourth most bearish extreme standing with the USD Index speculator level at a 4 percent score of its 3-year range.

The six-week trend for the speculator strength score was a dip by -4 percentage points this week while the speculator position was -5,558 net contracts this week with a shortfall of -537 contracts in the weekly speculator bets.

5-Year Bond

Extreme Bearish Leader
Next, the 5-Year Bond speculator position comes in as the fifth most bearish extreme standing for this week. The 5-Year speculator level is at a 6 percent score of its 3-year range.

The six-week trend for the speculator strength score was a decline by -2 percentage points this week. The speculator position was -2,554,763 net contracts this week with a jump by 127,224 contracts in the weekly speculator bets.

Article By InvestMacro – Receive our weekly COT Newsletter

*COT Report: The COT data, released weekly to the public each Friday, is updated through the most recent Tuesday (data is 3 days old) and shows a quick view of how large speculators or non-commercials (for-profit traders) were positioned in the futures markets.

The CFTC categorizes trader positions according to commercial hedgers (traders who use futures contracts for hedging as part of the business), non-commercials (large traders who speculate to realize trading profits) and nonreportable traders (usually small traders/speculators) as well as their open interest (contracts open in the market at time of reporting). See CFTC criteria here.

COT Updates Opinions
11 Sep

AI’s ballooning energy consumption puts spotlight on data center efficiency

By Divya Mahajan, Georgia Institute of Technology 

Artificial intelligence is growing fast, and so are the number of computers that power it. Behind the scenes, this rapid growth is putting a huge strain on the data centers that run AI models. These facilities are using more energy than ever.

AI models are getting larger and more complex. Today’s most advanced systems have billions of parameters, the numerical values derived from training data, and run across thousands of computer chips. To keep up, companies have responded by adding more hardware, more chips, more memory and more powerful networks. This brute force approach has helped AI make big leaps, but it’s also created a new challenge: Data centers are becoming energy-hungry giants.

Some tech companies are responding by looking to power data centers on their own with fossil fuel and nuclear power plants. AI energy demand has also spurred efforts to make more efficient computer chips.

I’m a computer engineer and a professor at Georgia Tech who specializes in high-performance computing. I see another path to curbing AI’s energy appetite: Make data centers more resource aware and efficient.

Energy and heat

Modern AI data centers can use as much electricity as a small city. And it’s not just the computing that eats up power. Memory and cooling systems are major contributors, too. As AI models grow, they need more storage and faster access to data, which generates more heat. Also, as the chips become more powerful, removing heat becomes a central challenge.

Cooling isn’t just a technical detail; it’s a major part of the energy bill. Traditional cooling is done with specialized air conditioning systems that remove heat from server racks. New methods like liquid cooling are helping, but they also require careful planning and water management. Without smarter solutions, the energy requirements and costs of AI could become unsustainable.

Even with all this advanced equipment, many data centers aren’t running efficiently. That’s because different parts of the system don’t always talk to each other. For example, scheduling software might not know that a chip is overheating or that a network connection is clogged. As a result, some servers sit idle while others struggle to keep up. This lack of coordination can lead to wasted energy and underused resources.

A smarter way forward

Addressing this challenge requires rethinking how to design and manage the systems that support AI. That means moving away from brute-force scaling and toward smarter, more specialized infrastructure.

Here are three key ideas:

Address variability in hardware. Not all chips are the same. Even within the same generation, chips vary in how fast they operate and how much heat they can tolerate, leading to heterogeneity in both performance and energy efficiency. Computer systems in data centers should recognize differences among chips in performance, heat tolerance and energy use, and adjust accordingly.

Adapt to changing conditions. AI workloads vary over time. For instance, thermal hotspots on chips can trigger the chips to slow down, fluctuating grid supply can cap the peak power that centers can draw, and bursts of data between chips can create congestion in the network that connects them. Systems should be designed to respond in real time to things like temperature, power availability and data traffic.

How data center cooling works.

Break down silos. Engineers who design chips, software and data centers should work together. When these teams collaborate, they can find new ways to save energy and improve performance. To that end, my colleagues, students and I at Georgia Tech’s AI Makerspace, a high-performance AI data center, are exploring these challenges hands-on. We’re working across disciplines, from hardware to software to energy systems, to build and test AI systems that are efficient, scalable and sustainable.

Scaling with intelligence

AI has the potential to transform science, medicine, education and more, but risks hitting limits on performance, energy and cost. The future of AI depends not only on better models, but also on better infrastructure.

To keep AI growing in a way that benefits society, I believe it’s important to shift from scaling by force to scaling with intelligence.The Conversation

About the Author:

Divya Mahajan, Assistant Professor of Computer Engineering, Georgia Institute of Technology

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Energy Opinions
10 Sep

How Europe’s deforestation law could change the global coffee trade

By Paul Mwebaze, University of Illinois at Urbana-Champaign 

If your morning can’t begin without coffee, you’re in good company. The world drinks about 2 billion cups of coffee a day. However, a European Union law might soon affect your favorite coffee beans – and the farmers who grow them.

Starting in 2026, companies selling coffee on the European Union market will have to prove that their product is “deforestation-free.” That means every bag of beans, every jar of ground coffee and every espresso capsule must trace back to coffee plants on land that hasn’t been cleared of forest since Dec. 31, 2020.

The new rules, found in what’s known as the EU Deforestation Regulation, are part of a wider effort to ensure European consumption doesn’t drive global deforestation.

However, on the ground – from the coffee hills of Ethiopia to the plantations of Brazil – the rule change could transform how coffee is grown, traded and sold.

Why the EU is targeting deforestation

Deforestation is a major driver of biodiversity loss and accounts for about 10% of global greenhouse gas emissions. And coffee plantations, along with cocoa, soy and palm oil production, which are also covered by the new regulations, are known sources of forest loss in some countries.

Under the new EU Deforestation Regulation, companies will be required to trace their coffee to its exact origin – down to the farm plot where the beans were grown – and provide geolocation data and documentation of supply chain custody to EU authorities.

They will also have to show proof, often through satellite imagery, that any open land where coffee is grown was forest-free before the 2020 cutoff date.

The rules were initially set to go into effect in early 2025 but were pushed back after complaints from many countries. Governments and industry groups in Latin America, Africa and Southeast Asia warned of trade friction for small farms, and the World Trade Organization has received complaints about the regulations.

Most companies must now comply by Dec. 30, 2025. Small enterprises get until June 30, 2026.

Potential winners and losers

The coffee supply chain is complex. Beans are grown by millions of farmers, sold to collectors, then move through processors, exporters, importers and roasters before reaching grocery shelves. Adding the EU rules means more checkpoints, more paperwork and possibly new strategies for sourcing coffee beans.

Small farms in particular could be vulnerable to losing business when the new rules go into effect. They could lose contracts or market access if they can’t provide the plot-level GPS coordinates and nondeforestation documentation buyers will require. That could prompt buyers to shift toward larger estates or organized co-ops that can provide the documentation.

If a farm can’t provide precise plot coordinates or pay for mapping services, it could end up being excluded from the world’s largest coffee market.

Larger coffee growers already using systems that can trace beans back to specific farm plots could gain a competitive edge.

Map showing tropical forests mostly in Africa, South America, Southeast Asia and Indonesia, and boreal and temperate forests across Canada, Russia and parts of Europe.
Global forest area by type and distribution in 2020, according to a U.N. Food and Agriculture Organization assessment.
FAO

The new regulations also include stricter oversight for countries considered most likely to allow deforestation, which could slow trade from those regions. As a result, buyers may shift to regions with lower deforestation risk.

Even outside Europe, big buyers are likely to prioritize beans they can trace to nondeforested plots, potentially dropping small farms that can’t provide plot-level proof. That could reduce availability and raise the price of some coffee types and put farms out of business. In some cases, the EU regulations could reroute undocumented coffee beans into markets such as the U.S.

Helping small farms succeed

For small farms, succeeding under the new EU rules will depend on access to technical support and low-cost tools for tracing their crop’s origin. Some countries are developing national systems to track deforestation, and they are pushing the EU to invest more in helping them.

Those small farms that can comply with the rules, often through co-ops, could become attractive low-risk suppliers for large buyers seeking compliant crops.

The change could also boost demand for sustainability certifications, such as Rainforest Alliance, 4C Common Code or Fairtrade, which certify only products that don’t contribute to deforestation. But even certified farms will still need to provide precise location data.

Agroforestry’s potential

Arabica coffee, the most common variety sold globally, naturally evolved as an understory shrub, performing best in cooler tropical uplands with good drainage and often partial shade. That points to a way farmers can reduce deforestation risk while still growing coffee: agroforestry.

Two women examine beans on a coffee plant.
Farmers check on coffee beans at a small agroforestry operation in Kenya. The coffee bushes were planted among trees that provide shade.
World Agroforestry Centre/Joseph Gachoka via Flickr, CC BY-NC-SA

Agroforestry involves planting or conserving shade trees in and around coffee plots to maintain the tree canopy.

In agroforestry systems, shade trees can buffer heat and drought, often reducing evaporation from soil and moderating plants’ water stress. Several field studies show lower evaporative losses and complementary water use between coffee and shade trees. In some contexts, this can lower irrigation needs and reduce fertilizer demand. Practical tools such as World Coffee Research’s Shade Catalog help farmers choose the right tree species for their location and goals.

Agroforestry is common in Ethiopia, where Arabica originated, and in parts of Central America, thanks to long traditions of growing coffee in shade and specialty demand for the products.

Under the new EU rules, however, even these farms must prove that no forest was cleared after 2020.

Why this matters to coffee drinkers

For European coffee drinkers, the new EU rules promise more sustainable coffee. But they may also mean higher prices if compliance costs are passed down the supply chain to consumers.

For coffee lovers elsewhere, changes in global trade flows could shift where beans are sold and at what price. As EU buyers bid up beans that can be traced to nondeforested plots, more of those “fully verified” coffees will flow to Europe. U.S. roasters may then face higher prices or tighter supply for traceable lots, while unverified beans are discounted or simply avoided by brands that choose to follow EU standards.The Conversation

About the Author:

Paul Mwebaze, Research Economist at the Institute for Sustainability, Energy and Environment, University of Illinois at Urbana-Champaign

This article is republished from The Conversation under a Creative Commons license. Read the original article.

 

Opinions Soft Commodities
09 Sep

China’s electric vehicle influence expands nearly everywhere – except the US and Canada

By Jack Barkenbus, Vanderbilt University 

In 2025, 1 in 4 new automotive vehicle sales globally are expected to be an electric vehicle – either fully electric or a plug-in hybrid.

That is a significant rise from just five years ago, when EV sales amounted to fewer than 1 in 20 new car sales, according to the International Energy Agency, an intergovernmental organization examining energy use around the world.

In the U.S., however, EV sales have lagged, only reaching 1 in 10 in 2024. By contrast, in China, the world’s largest car market, more than half of all new vehicle sales are electric.

The International Energy Agency has reported that two-thirds of fully electric cars in China are now cheaper to buy than their gasoline equivalents. With operating and maintenance costs already cheaper than gasoline models, EVs are attractive purchases.

Most EVs purchased in China are made there as well, by a range of different companies. NIO, Xpeng, Xiaomi, Zeekr, Geely, Chery, Great Wall Motor, Leapmotor and especially BYD are household names in China. As someone who has followed and published on the topic of EVs for over 15 years, I expect they will soon become as widely known in the rest of the world.

What kinds of EVs is China producing?

China’s automakers are producing a full range of electric vehicles, from the subcompact, like the BYD Seagull, to full-size SUVs, like the Xpeng G9, and luxury cars, like the Zeekr 009.

Recent European crash-test evaluations have given top safety ratings to Chinese EVs, and many of them cost less than similar models made by other companies in other countries.

A Wall Street Journal video explores a Chinese ‘dark factory’ – one so automated that it doesn’t need lights inside.

What’s behind Chinese EV success?

There are several factors behind Chinese companies’ success in producing and selling EVs. To be sure, relatively low labor costs are part of the explanation. So are generous government subsidies, as EVs were one of several advanced technologies selected by the Chinese government to propel the nation’s global technological profile.

But Chinese EV makers are also making other advances. They make significant use of industrial robotics, even to the point of building so-called “dark factories” that can operate with minimal human intervention. For passengers, they have reimagined vehicles’ interiors, with large touchscreens for information and entertainment, and even added a refrigerator, bed or karaoke system.

Competition among Chinese EV makers is fierce, which drives additional innovation. BYD is the largest seller of EVs, both domestically and globally. Yet the company says it employs over 100,000 scientists and engineers seeking continual improvement.

From initial concept models to actual rollout of factory-made cars, BYD takes 18 months – half as long as U.S. and other global automakers take for their product development processes, Reuters reported.

BYD is also the world’s second-largest EV battery seller and has developed a new battery that can recharge in just five minutes, roughly the same time it takes to fill a gas-powered car’s tank.

Exports

The real test of how well Chinese vehicles appeal to consumers will come from export sales. Chinese EV manufacturers are eager to sell abroad because their factories can produce far more than the 25 million vehicles they can sell within China each year – perhaps twice as much.

China already exports more cars than any other nation, though primarily gas-powered ones at the moment. Export markets for Chinese EVs are developing in Western Europe, Southeast Asia, Latin America, Australia and elsewhere.

The largest market where Chinese vehicles, whether gasoline or electric, are not being sold is North America. Both the U.S. and Canadian governments have created what some have called a “tariff fortress” protecting their domestic automakers, by imposing tariffs of 100% on the import of Chinese EVs – literally doubling their cost to consumers.

Customers’ budgets matter too. The average price of a new electric vehicle in the U.S. is approximately $55,000. Less expensive vehicles make up part of this average, but without tax credits, which the Trump administration is eliminating after September 2025, nothing gets close to $25,000. By contrast, Chinese companies produce several sub-$25,000 EVs, including the Xpeng M03, the BYD Dolphin and the MG4 without tax credits. If sold in America, however, the 100% tariffs would remove the price advantage.

Tesla, Ford and General Motors all claim they are working on inexpensive EVs. More expensive vehicles, however, generate higher profits, and with the protection of the “tariff fortress,” their incentive to develop cheaper EVs is not as high as it might be.

In the 1970s and 1980s, there was considerable U.S. opposition to importing Japanese vehicles. But ultimately, a combination of consumer sentiment and the willingness of Japanese companies to open factories in the U.S. overcame that opposition, and Japanese brands like Toyota, Honda and Nissan are common on North American roads. The same process may play out for Chinese automakers, though it’s not clear how long that might take.The Conversation

About the Author:

Jack Barkenbus, Visiting Scholar, Vanderbilt University

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Energy Opinions
08 Sep

Gold Poised to Test Fresh Highs

By RoboForex Analytical Department

Gold held near historic levels on Monday, trading around 3,590 USD per ounce, bolstered by a softer-than-expected US labour market report for August. Employment growth fell short of forecasts, while the unemployment rate climbed to its highest level since 2021. This has reinforced market expectations of an imminent Federal Reserve rate cut as early as September, with investors pricing in a 92% probability of such a move.

Further supporting the bullish sentiment are growing doubts over the Fed’s independence, as former President Donald Trump continues to criticise the central bank – driving increased safe-haven demand for gold.

Demand was also reinforced by the People’s Bank of China, which added to its gold reserves for the tenth consecutive month in August as part of a broader strategy to diversify its holdings away from the US dollar.

Additionally, the metal gained support from trade policy developments, with the Trump administration exempting gold and certain other metals from its latest tariff list.

In summary, gold remains near all-time highs due to a combination of dovish Fed expectations, political uncertainty, and sustained central bank demand.

Technical Analysis: XAU/USD

H4 Chart:

On the H4 chart, XAU/USD has completed another leg higher, reaching 3,600.07 USD. A corrective pullback toward the former resistance, which has now turned into support at around 3,550 USD, appears likely. Given the current fundamental backdrop, any test of this support may be followed by another upward wave, with initial targets at 3,600 USD and then 3,650 USD. The MACD indicator provides technical support for this scenario. Although the histogram and signal line remain above zero, both are declining – suggesting a near-term correction before the broader uptrend resumes.

H1 Chart:

On the H1 chart, the pair tested 3,600.07 USD and is now forming a corrective decline. The initial support target is 3,550 USD. Holding this level could prompt renewed buying, supporting a continuation of the upward trend. The Stochastic oscillator aligns with this view, with its signal line testing the 50.0 level, indicating potential for further near-term consolidation or a mild retracement.

Conclusion

Gold remains well-supported by a confluence of fundamental factors, including expectations of Fed easing, geopolitical tensions, and robust institutional demand. While a short-term technical correction is likely, the broader bullish trend remains intact, with scope for further gains towards 3,650 USD.

Disclaimer:

Any forecasts contained herein are based on the author’s particular opinion. This analysis may not be treated as trading advice. RoboForex bears no responsibility for trading results based on trading recommendations and reviews contained herein.

Metals Opinions
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