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It’s not just high gas prices – inflation is now spreading through the US economy

By D. Brian Blank, Mississippi State University and Brandy Hadley, Appalachian State University

Americans don’t need a press release to know that inflation is rising. Gasoline is above $4 per gallon amid the ongoing conflict in the Middle East and closure of the Strait of Hormuz, and the release of key price data on May 28, 2026, underscores why policymakers are worried these pressures could spread into the broader economy.

The report offered a mixed but still uncomfortable picture. The month-to-month rise was softer than expected, but the change year over year still points to concern: a 3.8% jump from a year earlier, the fastest pace since 2021, and a less volatile index that excludes food and energy up 3.3%.

This increase suggests inflation isn’t limited to gasoline. Housing, utilities and recreational spending are also keeping underlying inflation elevated, even as other data shows a slowing economy and weaker income growth.

As finance and applied investments professors who study how businesses make decisions amid uncertainty, we have been watching this tension build. In our 2026 economic outlook, we warned that recession fears could persist alongside rising prices. Fresh inflation data now suggests the challenge may be deeper and longer lasting than many expected.

Are all prices rising?

The fresh inflation data comes from the Personal Consumption Expenditures Price Index, or headline PCE, which is maintained and released by the Commerce Department’s Bureau of Economic Analysis. Headline PCE had already been getting hotter, rising to 3.5% year on year in March 2026, up from 2.8% in February. But an even more important metric for the Federal Reserve is core PCE, which excludes the more volatile categories of food and energy. Core PCE matters because it gives policymakers a clearer read on underlying inflation pressures and is generally considered a better predictor of where inflation is headed, the Fed’s chief concern. That has been rising this year as well.

The key question isn’t simply whether gas prices are rising, but whether those higher energy costs are spreading into the rest of the economy.

That’s why energy costs are both a measure of current inflation and a signal of future rising prices. They show up directly in inflation data like PCE but also affect shipping, airline fares, food production, utilities, packaging, business profit margins and consumer psychology. A one-time bump doesn’t necessarily create lasting inflation. But the risk increases when those higher costs pass through to the broader economy and people begin to expect inflation to remain high. For example, if workers believe costs will be higher in general, they might demand higher wages, which in turn can make inflation even hotter.

There’s already some evidence that the inflationary effect of energy prices is spreading. April’s Consumer Price Index report – another inflation gauge – showed a 3.8% leap, the fastest in three years, with energy prices up 18% and spending on airlines up over 20%, while grocery prices posted their largest monthly gain since 2022. Tariff-sensitive categories like apparel and household furnishings are also still climbing.

And it’s these costs, not core PCE, that households experience every day. Americans buy gas, pay utility bills, purchase groceries and start changing their spending behavior in response to these pressures. That’s why the Fed is watching to see how energy prices impact other measures of inflation.

What’s the Fed to do?

Kevin Warsh has just been sworn in as the new chair of the central bank, which means the next meeting of the Fed’s policymaking committee on June 16-17, 2026, will be his first in that role. He’ll face an unusual amount of disagreement among committee members as well as scrutiny over his own positions given his rhetorical shifts on inflation and Fed policy since he was nominated by President Donald Trump. The president has pressured the Fed to cut rates, while Warsh has recently downplayed the significance and accuracy of the PCE gauge.

The Fed’s tool for responding to inflation is to raise interest rates, but it’s not always straightforward. The Fed doesn’t just hike interest rates as a direct response to inflation. If the increase in energy prices looks temporary and inflation expectations remain “anchored” – that is, stable among consumers – the Fed may hold steady on rates or even cut them as consumers continue to dial back spending. But it may have to keep rates higher for longer or even consider additional tightening if those conditions don’t hold and inflation continues rising.

This creates a problem for the Fed’s “dual mandate” to control inflation while supporting economic growth. Higher gas prices are inflationary, but they also reduce households’ spending power and dampen growth. In that sense, higher energy prices can act like a tax on consumers: People spend more to drive, heat and cool their homes, and receive goods, leaving less income for restaurants, travel, retail and other purchases.

That’s why the Fed doesn’t have a simple answer. If it hikes interest rates to combat inflation, it still won’t resolve geopolitical conflict and increase global oil supplies. But it can reduce demand and slow inflation.

Indeed, according to notes of the most recent Fed policy committee meeting in April, many officials are increasingly concerned that persistent inflation could require additional rate hikes. While the Fed decided to hold rates steady at 3.50% to 3.75% at the time, committee members noted that inflation remains elevated, “in part reflecting the recent increase in global energy prices.”

Another factor: Long-term yields on Treasury bonds, which reflect what investors demand for buying U.S. debt, have reached their highest levels since 2007. That could be a sign that markets expect higher rates or more uncertainty – and it matters because yields influence mortgage rates, business borrowing costs and the value of retirement portfolios, to name a few examples. In other words, inflation concerns don’t have to wait for another Fed rate hike to affect the economy. If markets believe inflation will stay elevated, borrowing costs can rise on their own.

What to watch at the Fed’s June meeting

The leadership transition at the Fed makes this moment particularly noteworthy. Warsh’s first major challenge may not be whether to raise or cut rates immediately, but how to explain what the Fed is watching. Will he emphasize headline inflation, core inflation, other inflation measures, consumer expectations, financial conditions or signs of slowing demand? This is especially important, as some of these gauges are closer to 2% and rising more slowly while others rise more rapidly away from the Fed’s 2% target.

Artificial intelligence adds another complication. AI-related investment may be helping hold up growth even as households feel pressured by higher gas and grocery prices. That creates a divided economy: Consumers struggle with higher prices and borrowing costs, but AI-related investment supports markets, infrastructure spending and business optimism. For his part, Warsh argues that AI also will help drive down prices, allowing the Fed to cut rates sooner.

All of this makes the inflation outlook hard to read. Weakening consumer demand and wage growth argues for caution, while rising inflation expectations and businesses passing on higher costs to consumers and the broader economy argue for higher rates.

Ultimately, the key question for the Fed is not simply whether inflation is rising, but whether energy prices are reopening the inflation fight at the exact moment it’s trying to prove that price stability is still within reach. Warsh’s first months as chair will test whether the Fed can maintain inflation credibility while avoiding unnecessary damage to an already pressured consumer economy.

About the Author:

D. Brian Blank, Associate Professor of Finance, Mississippi State University and Brandy Hadley, Associate Professor of Finance and Distinguished Scholar of Applied Investments, Appalachian State University

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

Economics & Fundamentals Opinions

29 May

Scientists used a method from ecology to identify whether icy moons could hold conditions for life

By Gideon Yoffe, Weizmann Institute of Science

New observatories and spacecraft missions are probing environments in our solar system that could potentially host life but have long remained hidden. Icy moons like Saturn’s Enceladus and Jupiter’s Europa likely contain oceans beneath frozen outer shells. But a layer of ice prohibits space probes from sampling them directly.

Exploring these icy moons is almost forensic: Their surfaces keep a partial record of inaccessible interiors. Scientists need tools that can help them figure out whether evidence of life lies beneath without observing it directly.

I’m a planetary scientist, and my colleagues and I have developed a tool that could help evaluate whether an environment has the right conditions for life, based on patterns in the types of molecules found in a sample.

Future missions may sample environments that could host life, such as Saturn’s moon Enceladus.
Jason Major, Cassini spacecraft/Flickr, CC BY-NC-SA

Looking for life’s fingerprints

The search for life often begins with organic molecules: the carbon-based molecules from which life on Earth is built. Two especially important families of molecules are amino acids, which cells use to build proteins, and fatty acids, which help form cell membranes.

Yet these molecules are not unique to life – they can also form through nonbiological chemistry. Scientists have previously detected them in asteroids and meteorites.

Because detecting amino acids or fatty acids in a planetary environment alone will not tell researchers whether they are produced by life or by nonlife, they must seek additional evidence.

One clue is molecular handedness, or “chirality.” Certain amino acids occur in two mirror-image forms. Nonbiological processes often produce both forms in similar amounts, whereas life on Earth uses almost exclusively the left-handed forms. A strong excess of one form can point toward biology.

Another clue is found in the balance between the heavier and lighter forms of the same element within molecules. Usually, life prefers to use the lighter form.

Both of these clues are powerful indicators but difficult to measure in space. They require sensitive instruments, clean samples and often more material than a spacecraft can obtain.

That said, current and planned missions may provide a more limited – but still valuable – kind of measurement: a list of molecules and the proportions in which they are found. Our study demonstrates how researchers can use this simpler information to learn more about the molecules’ chemical origin.

Investigating diversity

Life does not merely produce certain molecules – it produces them in arrangements of unique patterns. Living systems invest energy into making molecules that serve specific functions, even when those molecules are complex and harder to form. Proteins, for example, require a broad set of amino acids, including relatively complex ones. Nonbiological chemistry can also make amino acids, but typically it makes simpler ones.

A chemical diagram showing the general structure of an amino acid. — Your body requires many different amino acids to live. But nonliving chemical processes can also produce amino acids, so their presence in a sample doesn’t definitively prove life.

In our study, we investigated whether these molecules leave a statistical pattern that could serve as a biosignature: a measurable clue that may point toward life.

To quantify this idea, we used a method from ecology called diversity theory. Ecologists do not only ask how many species exist in a particular ecosystem, but also how those species are distributed: whether the community is dominated by a few very common species or by many species occurring in comparable numbers. The point of diversity theory is to both compile a list of species and capture the prevalence of each.

We applied the same logic to molecules. Within a family, such as amino acids, we treated each molecule like a species in an ecological community and measured its abundance. We wanted to know: Is a given mixture of molecules distributed evenly across different types or dominated by only a few of them? And could that pattern reflect the process that produced those molecules, whether biological or nonbiological?

Testing the framework

To test this idea, we compiled a deliberately broad dataset that included amino acids from a variety of sources: meteorites, samples from asteroid missions, laboratory simulations of nonbiological chemistry, modern organisms, sediments, ancient fossils and samples from various environments on Earth. We later did the same with fatty acids.

For amino acids, we found a clear distinction. The biological samples tended to contain many complex amino acids, in proportions similar to those of simpler ones. Nonbiological samples were usually sparser – that is, more strongly dominated by simple molecules.

This result makes sense. If biology can overcome the chemical bottlenecks necessary to create more complex molecules, you’d expect to see more of those molecules. On the other hand, nonbiological chemistry is more limited and dominated by molecules that form randomly. Complex molecules are far less likely to form under nonbiological conditions.

Fatty acids showed an opposite but equally informative pattern. Chains of fatty acids make up the outer membranes of living cells. We found that in biological samples, the fatty acid chains were all a similar length. In contrast, nonbiological samples had a wider distribution of chain lengths.

A chemical structure diagram of a fatty acid — Fatty acids are chains of molecules made up of carbon and hydrogen, with oxygen at the end.
Innerstream/Wikimedia Commons

Even though, unlike the amino acid results, the nonbiological samples showed greater fatty acid diversity, this chain length finding supported the main idea behind our research: Life shapes molecular mixtures according to function.

Taken together, our results suggest that molecular diversity can serve as a new kind of biosignature. It cannot prove the presence of life on its own, and it should be interpreted alongside other measurements. But it offers a practical way to use the kind of data spacecraft are most likely to obtain: the proportions of molecules.

Searching for life in the solar system and beyond

Future spacecraft are unlikely to find pristine biological material, even if it exists. More likely, they will encounter the chemical traces of molecules, altered by the harsh conditions on planetary surfaces.

Next, we wanted to know how long the diversity signal could survive in the type of harsh environment where scientists may look, such as the surface of Europa. Its surface is continually being bombarded by energetic particles trapped in Jupiter’s magnetic field, which can break different organic molecules apart at different rates.

An illustration of a spacecraft flying over an ice-covered moon. — NASA’s Europa Clipper mission will fly around the moon of Jupiter and take measurements to investigate whether it could harbor life.
NASA/JPL-Caltech

We modeled how these molecules would degrade under such conditions and found that the diversity signal could remain recognizable for thousands of years when the molecules are buried under a few centimeters of ice. The signal is not indestructible, but it does not require an exceptionally fresh sample.

Our results suggest that in some cases the pattern left by life may still be recognizable even after the individual molecules have begun to break down.

The take-home message from our study is that life organizes chemistry in ways that could persist even after those ingredients are altered. Living systems arrange molecules according to biological needs, while nonbiological chemistry usually follows what is easiest to produce. If this organization can survive in planetary materials, future spacecraft may search not only for the building blocks of life but for the deeper statistical pattern that life leaves behind.

About the Author:

Gideon Yoffe, Postdoctoral Fellow in Planetary Science, Weizmann Institute of Science

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

Economics & Fundamentals Opinions

29 May

Week Ahead: Dollar in the crosshairs

By ForexTime

FXTM’s USDInd ↑ 0.8% YTD
Iran conflict + US NFP combo = fresh volatility?
Over past year, NFP triggered moves of ↑ 0.3% & ↓ 1.2%
NFP forecast to trigger moves of ↑ 0.3% & ↓ 0.4%
Technical levels: 99.50, 50-day, 200-day SMA

The first full trading week of June is packed with high-impact events.

A volley of market-moving events, the US May jobs report, the OECD economic outlook and speeches from the financial heavyweights set the stage for serious volatility.

Monday, 1st June

• AUD: Australia Melbourne Institute inflation gauge

• CNY: China RatingDog manufacturing PMI

• EUR: Eurozone S&P Global manufacturing PMI, unemployment, inflation expectations

• GBP: UK S&P Global manufacturing PMI, Nationwide house prices

• USDInd: ISM Manufacturing, S&P Global manufacturing PMI

Tuesday, 2nd June

• EUR: Eurozone CPI

• USDInd: Minneapolis Fed President Neel Kashkari, Cleveland Fed President Beth Hammack speech

Wednesday, 3rd June

• AUD: Australia GDP

• CNY: China RatingDog services PMI

• EUR: Eurozone S&P Global services PMI, PPI

• OECD releases its latest economic outlook

• USDInd: Fed Beige Book, ISM services index, US Treasury Secretary Scott Bessent testimony

Thursday, 4th June

• EUR: Eurozone retail sales

• USDInd: US initial jobless claims, San Francisco Fed President Mary Daly speech

• GBP: BOE Governor Andrew Bailey speech

Friday, 5th June

• CAD: Canada unemployment

• EUR: Eurozone GDP

• GBP: BOE Governor Andrew Bailey speech

• USDInd: US unemployment, nonfarm payrolls

One instrument sits right at the centre of it all – FXTM’s USDInd.

Note: The USD Index tracks how the dollar is performing against a basket of six different G10 currencies, including the Euro, British Pound, Japanese Yen, and Canadian dollar.

Here is how they are weighed:

Euro: 57.6%
JPY: 13.6%
GBP: 11.9%
CAD: 9.1%
SEK: 4.2%
CHF: 3.6%

For weeks, the USD Index has been stuck…coiled inside a wide range, going nowhere fast.

Same story, different day.

But that range won’t hold forever with the lineup of key events potentially sparking a major move.

Here are 4 reasons why a breakout could be on the horizon:

1) US-Iran tentative deal

In a welcome development to global markets, the US and Iran have reached a tentative deal to extend a ceasefire by 60 days and launch further talks on Tehran’s nuclear program.

However, President Donald Trump has yet to agree to the terms.

Nevertheless, this marks a positive shift somewhat outweighing concerns about clashes in the Persian Gulf.

If Trump signs of the ceasefire deal, this may raise hopes of the re-opening of the Strait of Hormuz – weakening the dollar as inflation fears cool.
Should the tentative deal fall apart, the dollar may rally on renewed inflation concerns and geopolitical risk

2) US May NFP report

The May US jobs report on Friday 5th June may provide critical insight into the health of the labour markets.

Here’s what economists predict for this closely watched jobs report:

Headline NFP figure: 93,000 (new jobs added to US labour market)

If so, this would be a decline from the April 115,000 headline NFP figure.

Unemployment rate: 4.3%

If so, this would match April unemployment rate

Average hourly earnings month-on-month (May 2026 vs. APril 2026): 0.3%

If so, this would higher than April’s figure.

Note: Other key data in the week including the ADP and Fed speeches may influence gold prices.

A stronger-than-expected US jobs data may stimulate bets around the Fed hiking rates – boosting the USDInd.
A weaker-than-expected figure could cool bets around Fed hikes, weakening the USDInd.

Note: Traders are currently pricing a 56% chance that the Fed will hike rates by December 2026.

3) Major central bank speakers

A host of Fed speakers and financial heavyweights will be under the spotlight in the week ahead.

Central bank heads from BoE’s Andrey Bailey, BoJ Governor Kazuo Ueda, RBA Governor Michele Bullock may share key insight into future policy moves and thoughts on inflation. This could translate into heightened volatility for the USDInd given how its weighted.

4) Technical forces

FXTM’s USDInd remains in a wide range.

A solid breakout and daily close above 99.50 could trigger an incline towards the 100.00 and 100.67.
Should prices break below 98.90, bears could be encouraged to hit the 200-day SMA and 97.70.

Article by ForexTime

ForexTime Ltd (FXTM) is an award winning international online forex broker regulated by CySEC 185/12 www.forextime.com

Forex and Currency News Opinions

24 May

Australian Dollar Speculators continue to raise Bullish Bets for 4th straight week

By InvestMacro

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 May 19th 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

The COT currency market speculator bets were overall lower this week as just three out of the eleven currency markets we cover had higher positioning while the other eight markets had lower speculator contracts.

Leading the gains for the currency markets was the Brazilian Real (2,459 contracts) with Bitcoin (853 contracts) and the Australian Dollar (654 contracts) also showing positive weeks.

The currencies seeing declines in speculator bets on the week were the British Pound (-21,248 contracts), the Japanese Yen (-18,803 contracts), the Canadian Dollar (-14,989 contracts), the EuroFX (-6,687 contracts), the US Dollar Index (-3,666 contracts), the New Zealand Dollar (-1,463 contracts), the Mexican Peso (-1,841 contracts) and with the Swiss Franc (-740 contracts) also registering lower bets on the week.

Australian Dollar speculators continue to raise bullish bets for 4th straight week

Highlighting this week’s Currency market speculator positioning is the Australian Dollar’s continued speculator strength. The currency speculators raised their Australian Dollar bets very modestly by just 654 contracts, but have now pushed AUD bets higher for a fourth consecutive week—and for the 20th time out of the past 24 weeks—illustrating the recent strength for speculators in the Australian Dollar. In these past 24 weeks, the Australian Dollar has surged by almost +170,000 net contracts, going from a total position of -83,393 contracts on December 2nd to this week’s position of +85,644 net contracts. This week’s position is now the highest level for the Australian Dollar standing since 2013 and not far off from the all-time record, which was a total of 103,376 contracts on December 11th of 2012. In the Forex markets, the Australian Dollar against the US Dollar has recently traded at four-year highs but has now dipped for two consecutive weeks. Currently, the AUD is trading at 0.7135, with major support at 0.7100 sitting below while recent highs were capped by resistance above around 0.7270.

The British Pound Sterling fell sharply this week by over -21,000 contracts, and has now has fallen for three out of the past four weeks. This weakness has pushed the overall net speculator position to the most bearish level of the past nine weeks. In the Foreign Exchange market, the GBPUSD currency pair has been consolidating in sideways trading action for over a year against the USD, with support at the 1.3030 level and overhead resistance at the 1.3700 level. Currently, the price is right in the middle of that sideways channel at about 1.3447.

The Japanese Yen also saw lower levels in speculator bets this week by over -18,000 contracts. The speculator position for the Yen has been deteriorating since having a couple of weeks in bullish territory in February and has now seen speculator positions fall in 10 out of the past 13 weeks, with this week’s net speculator standing totaling -93,905 contracts. In the Foreign Exchange market, the Yen has fallen for three consecutive weeks following the Bank of Japan’s intervention to prop up the Yen in late April. The price is approaching those same levels where the BOJ intervened, and it will be interesting looking forward as the market tests the BOJ resolve once again.

The Canadian Dollar speculator bets fell this week by over -14,000 contracts and have fallen for two straight weeks. The CAD speculator position has now fallen in seven out of the past 10 weeks and right now, the Canadian Dollar’s overall speculator standing sits at -31,231 net contracts. In the Forex markets, the Canadian Dollar has declined for three consecutive weeks against the US Dollar and has fallen below its 200-weekly moving average. The CAD, however, continues to trade in an ascending triangle pattern, which has not broken to the downside or the upside yet and will likely resolve itself in the coming weeks.

The US Dollar Index speculator positions dropped this week by -3,666 contracts. This has flipped the US Dollar speculative position into an overall bearish position. This small bearish level represents the first bearish position since March 10th, a span of 10 weeks and signals an overall neutral position in the big scheme of things. In the Foreign Exchange markets, the US Dollar Index has remained in its trading range for basically one full year with a price of 100.00 on the upside and a lower support level of 96.50 representing the bottom of the trading range. At the moment, the US Dollar Index positioning is closer the top of the trading range at a closing price on the week of 99.01.

The British Pound Sterling topped Currency Market price performance.

Price performances for the Currency Markets on the week were led by the British Pound, which rose by almost 1% with a 0.94% 5-Day increase. The Brazilian Real came in second with a 0.72% rise and was followed by the New Zealand Dollar, which saw an uptick by 0.33%.

Next up, the Swiss Franc was modestly higher by 0.30% and was followed by the Mexican Peso, which rounded out the gainers with a 0.18% rise.

On the downside, the Australian Dollar and the US Dollar Index were virtually unchanged with a -0.01% decline for each of those markets. The Japanese Yen was lower by -0.08%, followed by the Euro, which dipped by -0.16%, and the Canadian Dollar fell by -0.52%. The biggest decliner on the week was Bitcoin, which fell by -1.99%.

Currencies Data:

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 Australian Dollar & Brazilian Real

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 Australian Dollar (100 percent) and the Brazilian Real (92 percent) lead the currency markets this week. Bitcoin (91 percent) and the Canadian Dollar (71 percent) come in as the next highest in the weekly strength scores.

On the downside, the British Pound (12 percent) and the New Zealand Dollar (18 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 Japanese Yen (25 percent) and the Swiss Franc (26 percent).

3-Year Strength Statistics:
US Dollar Index (42.8 percent) vs US Dollar Index previous week (52.7 percent)
EuroFX (42.6 percent) vs EuroFX previous week (45.2 percent)
British Pound Sterling (12.3 percent) vs British Pound Sterling previous week (21.3 percent)
Japanese Yen (24.9 percent) vs Japanese Yen previous week (30.0 percent)
Swiss Franc (26.0 percent) vs Swiss Franc previous week (27.5 percent)
Canadian Dollar (71.0 percent) vs Canadian Dollar previous week (77.5 percent)
Australian Dollar (100.0 percent) vs Australian Dollar previous week (99.7 percent)
New Zealand Dollar (18.5 percent) vs New Zealand Dollar previous week (20.1 percent)
Mexican Peso (45.2 percent) vs Mexican Peso previous week (46.5 percent)
Brazilian Real (91.6 percent) vs Brazilian Real previous week (89.8 percent)
Bitcoin (91.5 percent) vs Bitcoin previous week (74.5 percent)

Brazilian Real & EuroFX top the 6-Week Strength Trends

COT Strength Score Trends (or move index, calculates the 6-week changes in strength scores) showed that the Brazilian Real (22 percent) and the EuroFX (16 percent) lead the past six weeks trends for the currencies. The Canadian Dollar (11 percent), the Australian Dollar (8 percent) and the Mexican Peso (3 percent) are the next highest positive movers in the 3-Year trends data.

The US Dollar Index (-16 percent) leads the downside trend scores currently with the Swiss Franc (-13 percent), Bitcoin (-9 percent) and the New Zealand Dollar (-5 percent) following next with lower trend scores.

3-Year Strength Trends:
US Dollar Index (-16.2 percent) vs US Dollar Index previous week (-1.3 percent)
EuroFX (16.0 percent) vs EuroFX previous week (15.5 percent)
British Pound Sterling (-3.4 percent) vs British Pound Sterling previous week (4.1 percent)
Japanese Yen (-0.0 percent) vs Japanese Yen previous week (-0.6 percent)
Swiss Franc (-12.6 percent) vs Swiss Franc previous week (-12.8 percent)
Canadian Dollar (10.5 percent) vs Canadian Dollar previous week (7.1 percent)
Australian Dollar (7.7 percent) vs Australian Dollar previous week (1.8 percent)
New Zealand Dollar (-5.2 percent) vs New Zealand Dollar previous week (-12.1 percent)
Mexican Peso (3.4 percent) vs Mexican Peso previous week (4.5 percent)
Brazilian Real (22.5 percent) vs Brazilian Real previous week (11.5 percent)
Bitcoin (-8.5 percent) vs Bitcoin previous week (-19.8 percent)

Individual COT Forex Markets:

US Dollar Index Futures:

Positioning Notes:

US Dollar Index large speculator standing this week reached a net position of -479 contracts in the data reported through Tuesday.
Weekly Speculator position decline of -3,666 contracts from the previous week which had a total of 3,187 net contracts.
This week’s current strength score (range over the past 3 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 52.4 percent.
The Small Traders (not shown in chart) are Bullish with a score of 74.5 percent.

Price Trend-Following Model: Uptrend

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

US DOLLAR INDEX Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	52.7	34.3	9.3
– Percent of Open Interest Shorts:	53.9	38.1	4.3
– Net Position:	-479	-1,551	2,030
– Gross Longs:	21,403	13,920	3,791
– Gross Shorts:	21,882	15,471	1,761
– Long to Short Ratio:	1.0 to 1	0.9 to 1	2.2 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	42.8	52.4	74.5
– Strength Index Reading (3 Year Range):	Bearish	Bullish	Bullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-16.2	14.0	15.1

Euro Currency Futures:

Positioning Notes:

Euro Currency large speculator standing this week reached a net position of 33,513 contracts in the data reported through Tuesday.
Weekly Speculator position decrease of -6,687 contracts from the previous week which had a total of 40,200 net contracts.
This week’s current strength score (range over the past 3 years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 42.6 percent.
The Commercials are Bullish with a score of 58.2 percent.
The Small Traders (not shown in chart) are Bearish with a score of 44.2 percent.

Price Trend-Following Model: Downtrend

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

EURO Currency Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	28.2	57.1	10.4
– Percent of Open Interest Shorts:	24.2	65.1	6.4
– Net Position:	33,513	-66,535	33,022
– Gross Longs:	233,251	471,453	86,082
– Gross Shorts:	199,738	537,988	53,060
– Long to Short Ratio:	1.2 to 1	0.9 to 1	1.6 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	42.6	58.2	44.2
– Strength Index Reading (3 Year Range):	Bearish	Bullish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	16.0	-11.5	-16.2

British Pound Sterling Futures:

Positioning Notes:

British Pound Sterling large speculator standing this week reached a net position of -64,307 contracts in the data reported through Tuesday.
Weekly Speculator position decrease of -21,248 contracts from the previous week which had a total of -43,059 net contracts.
This week’s current strength score (range over the past 3 years, measured from 0 to 100) shows the speculators are currently Bearish-Extreme with a score of 12.3 percent.
The Commercials are Bullish-Extreme with a score of 87.0 percent.
The Small Traders (not shown in chart) are Bearish with a score of 41.8 percent.

Price Trend-Following Model: Downtrend

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

BRITISH POUND Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	23.8	66.6	8.8
– Percent of Open Interest Shorts:	46.3	42.6	10.3
– Net Position:	-64,307	68,698	-4,391
– Gross Longs:	68,075	190,244	25,040
– Gross Shorts:	132,382	121,546	29,431
– Long to Short Ratio:	0.5 to 1	1.6 to 1	0.9 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	12.3	87.0	41.8
– Strength Index Reading (3 Year Range):	Bearish-Extreme	Bullish-Extreme	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-3.4	2.3	5.1

Japanese Yen Futures:

Positioning Notes:

Japanese Yen large speculator standing this week reached a net position of -93,905 contracts in the data reported through Tuesday.
Weekly Speculator position lowering of -18,803 contracts from the previous week which had a total of -75,102 net contracts.
This week’s current strength score (range over the past 3 years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 24.9 percent.
The Commercials are Bullish with a score of 73.8 percent.
The Small Traders (not shown in chart) are Bearish with a score of 44.8 percent.

Price Trend-Following Model: Downtrend

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

JAPANESE YEN Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	26.9	57.1	10.0
– Percent of Open Interest Shorts:	50.6	34.3	9.2
– Net Position:	-93,905	90,766	3,139
– Gross Longs:	106,603	226,613	39,648
– Gross Shorts:	200,508	135,847	36,509
– Long to Short Ratio:	0.5 to 1	1.7 to 1	1.1 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	24.9	73.8	44.8
– Strength Index Reading (3 Year Range):	Bearish	Bullish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-0.0	0.1	-0.5

Swiss Franc Futures:

Positioning Notes:

Swiss Franc large speculator standing this week reached a net position of -36,937 contracts in the data reported through Tuesday.
Weekly Speculator position reduction of -740 contracts from the previous week which had a total of -36,197 net contracts.
This week’s current strength score (range over the past 3 years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 26.0 percent.
The Commercials are Bullish with a score of 75.7 percent.
The Small Traders (not shown in chart) are Bearish with a score of 37.1 percent.

Price Trend-Following Model: Downtrend

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

SWISS FRANC Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	6.0	83.0	10.7
– Percent of Open Interest Shorts:	41.5	38.1	20.2
– Net Position:	-36,937	46,775	-9,838
– Gross Longs:	6,284	86,464	11,198
– Gross Shorts:	43,221	39,689	21,036
– Long to Short Ratio:	0.1 to 1	2.2 to 1	0.5 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	26.0	75.7	37.1
– Strength Index Reading (3 Year Range):	Bearish	Bullish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-12.6	13.2	-7.8

Canadian Dollar Futures:

Positioning Notes:

Canadian Dollar large speculator standing this week reached a net position of -31,231 contracts in the data reported through Tuesday.
Weekly Speculator position decline of -14,989 contracts from the previous week which had a total of -16,242 net contracts.
This week’s current strength score (range over the past 3 years, measured from 0 to 100) shows the speculators are currently Bullish with a score of 71.0 percent.
The Commercials are Bearish with a score of 32.3 percent.
The Small Traders (not shown in chart) are Bearish with a score of 32.8 percent.

Price Trend-Following Model: Weak Uptrend

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

CANADIAN DOLLAR Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	24.8	59.8	9.6
– Percent of Open Interest Shorts:	35.5	47.6	11.2
– Net Position:	-31,231	35,724	-4,493
– Gross Longs:	72,674	175,009	28,192
– Gross Shorts:	103,905	139,285	32,685
– Long to Short Ratio:	0.7 to 1	1.3 to 1	0.9 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	71.0	32.3	32.8
– Strength Index Reading (3 Year Range):	Bullish	Bearish	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	10.5	-9.0	-5.9

Australian Dollar Futures:

Positioning Notes:

Australian Dollar large speculator standing this week reached a net position of 85,644 contracts in the data reported through Tuesday.
Weekly Speculator position lift of 654 contracts from the previous week which had a total of 84,990 net contracts.
This week’s current strength score (range over the past 3 years, measured from 0 to 100) shows the speculators are currently Bullish-Extreme with a score of 100.0 percent.
The Commercials are Bearish-Extreme with a score of 1.0 percent.
The Small Traders (not shown in chart) are Bullish-Extreme with a score of 90.5 percent.

Price Trend-Following Model: Uptrend

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

AUSTRALIAN DOLLAR Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	50.3	33.3	13.7
– Percent of Open Interest Shorts:	21.9	69.7	5.7
– Net Position:	85,644	-109,572	23,928
– Gross Longs:	151,583	100,426	41,158
– Gross Shorts:	65,939	209,998	17,230
– Long to Short Ratio:	2.3 to 1	0.5 to 1	2.4 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	100.0	1.0	90.5
– Strength Index Reading (3 Year Range):	Bullish-Extreme	Bearish-Extreme	Bullish-Extreme
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	7.7	-5.3	-5.5

New Zealand Dollar Futures:

Positioning Notes:

New Zealand Dollar large speculator standing this week reached a net position of -40,613 contracts in the data reported through Tuesday.
Weekly Speculator position lowering of -1,463 contracts from the previous week which had a total of -39,150 net contracts.
This week’s current strength score (range over the past 3 years, measured from 0 to 100) shows the speculators are currently Bearish-Extreme with a score of 18.5 percent.
The Commercials are Bullish-Extreme with a score of 83.0 percent.
The Small Traders (not shown in chart) are Bearish-Extreme with a score of 14.8 percent.

Price Trend-Following Model: Downtrend

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

NEW ZEALAND DOLLAR Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	13.3	83.1	3.1
– Percent of Open Interest Shorts:	57.2	36.1	6.2
– Net Position:	-40,613	43,497	-2,884
– Gross Longs:	12,319	76,920	2,879
– Gross Shorts:	52,932	33,423	5,763
– Long to Short Ratio:	0.2 to 1	2.3 to 1	0.5 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	18.5	83.0	14.8
– Strength Index Reading (3 Year Range):	Bearish-Extreme	Bullish-Extreme	Bearish-Extreme
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-5.2	7.2	-24.6

Mexican Peso Futures:

Positioning Notes:

Mexican Peso large speculator standing this week reached a net position of 62,249 contracts in the data reported through Tuesday.
Weekly Speculator position decrease of -1,841 contracts from the previous week which had a total of 64,090 net contracts.
This week’s current strength score (range over the past 3 years, measured from 0 to 100) shows the speculators are currently Bearish with a score of 45.2 percent.
The Commercials are Bullish with a score of 52.5 percent.
The Small Traders (not shown in chart) are Bullish with a score of 50.8 percent.

Price Trend-Following Model: Strong Uptrend

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

MEXICAN PESO Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	46.7	48.1	4.1
– Percent of Open Interest Shorts:	16.2	81.1	1.6
– Net Position:	62,249	-67,299	5,050
– Gross Longs:	95,246	98,026	8,400
– Gross Shorts:	32,997	165,325	3,350
– Long to Short Ratio:	2.9 to 1	0.6 to 1	2.5 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	45.2	52.5	50.8
– Strength Index Reading (3 Year Range):	Bearish	Bullish	Bullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	3.4	-3.8	4.1

Brazilian Real Futures:

Positioning Notes:

Brazilian Real large speculator standing this week reached a net position of 71,012 contracts in the data reported through Tuesday.
Weekly Speculator position gain of 2,459 contracts from the previous week which had a total of 68,553 net contracts.
This week’s current strength score (range over the past 3 years, measured from 0 to 100) shows the speculators are currently Bullish-Extreme with a score of 91.6 percent.
The Commercials are Bearish-Extreme with a score of 7.7 percent.
The Small Traders (not shown in chart) are Bearish with a score of 43.3 percent.

Price Trend-Following Model: Uptrend

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

BRAZIL REAL Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	73.8	21.6	3.9
– Percent of Open Interest Shorts:	18.9	79.6	0.8
– Net Position:	71,012	-74,999	3,987
– Gross Longs:	95,462	27,975	5,037
– Gross Shorts:	24,450	102,974	1,050
– Long to Short Ratio:	3.9 to 1	0.3 to 1	4.8 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	91.6	7.7	43.3
– Strength Index Reading (3 Year Range):	Bullish-Extreme	Bearish-Extreme	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	22.5	-21.8	-2.3

Bitcoin Futures:

Positioning Notes:

Bitcoin large speculator standing this week reached a net position of 2,112 contracts in the data reported through Tuesday.
Weekly Speculator position gain of 853 contracts from the previous week which had a total of 1,259 net contracts.
This week’s current strength score (range over the past 3 years, measured from 0 to 100) shows the speculators are currently Bullish-Extreme with a score of 91.5 percent.
The Commercials are Bearish-Extreme with a score of 6.0 percent.
The Small Traders (not shown in chart) are Bearish with a score of 45.6 percent.

Price Trend-Following Model: Weak Downtrend

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

BITCOIN Statistics	SPECULATORS	COMMERCIALS	SMALL TRADERS
– Percent of Open Interest Longs:	77.4	0.9	5.5
– Percent of Open Interest Shorts:	68.2	10.8	4.8
– Net Position:	2,112	-2,272	160
– Gross Longs:	17,791	212	1,262
– Gross Shorts:	15,679	2,484	1,102
– Long to Short Ratio:	1.1 to 1	0.1 to 1	1.1 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):	91.5	6.0	45.6
– Strength Index Reading (3 Year Range):	Bullish-Extreme	Bearish-Extreme	Bearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:	-8.5	-0.9	24.2

Article By InvestMacro – Receive our weekly COT Reports by Email

*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.

All information and opinions on this website and contained in this article are for general informational purposes only and do not constitute investment advice.

COT Updates Forex and Currency News Opinions

24 May

Speculator Extremes: AUD, Soybean Meal & Copper lead 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 Tuesday May 19th.

This weekly Extreme Positions report highlights the Most Bullish and Most Bearish Positions for the speculator category and is a current snapshot of how speculators were positioned as of Tuesday. 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).

The 6-WK Trend score is the change in the Strength Index over the past 6 weeks and signals how strong and which way the Strength Index is going.

Here Are This Week’s Most Bullish Speculator Positions:

Australian Dollar

The Australian Dollar speculator position comes in tied at the top of this week’s extreme standings as the AUD speculator level resides at a 100 percent score of its 3-year range.

The six-week trend for the speculator strength score came in at an advance by 8 percentage points this week. The overall speculator position was 85,644 net contracts this week with an increase of 654 contracts 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.

Soybean Meal

The Soybean Meal speculator position also comes in tied atop the extreme standings this week with the Soybean Meal speculator level at a 100 percent score of its 3-year range.

The six-week trend for the percent strength score was a boost by 17 percentage points this week. The speculator position registered 159,741 net contracts this week with an advance of 11,088 contracts in speculator bets.

Copper

The Copper speculator position comes in as the most bullish extreme standing this week. The Copper speculator level is currently at a 99 percent score of its 3-year range.

The six-week trend for the percent strength score totaled a gain of 32 percentage points this week. The overall net speculator position was a total of 75,886 net contracts this week with a decrease of -423 contract in the weekly speculator bets.

Wheat

The Wheat speculator position comes up number four in the extreme standings this week with the Wheat speculator level at a 99 percent score of its 3-year range.

The six-week trend for the speculator strength score totaled a lift of 16 percentage points this week and the overall speculator position was 263 net contracts this week with an addition of 14,684 contracts in the speculator bets.

Cotton

The Cotton speculator position rounds out the top five in this week’s bullish extreme standings. The Cotton speculator level sits at a 94 percent score of its 3-year range and the six-week trend for the speculator strength score was a lift of 19 percentage points this week.

The speculator position was 92,470 net contracts this week with a retreat of -9,919 contracts in the weekly speculator bets.

The Most Bearish Speculator Positions of the Week:

3-Month Secured Overnight Financing Rate

The 3-Month Secured Overnight Financing Rate speculator position comes in as the most bearish extreme standing this week. The SOFR 3-Months speculator level is at a 0 percent score of its 3-year range.

The six-week trend for the speculator strength score was a fall by -41 percentage points this week. The overall speculator position was -1,473,774 net contracts this week with a decline of -330,187 contracts in the speculator bets.

Cocoa Futures

The Cocoa Futures speculator position comes in next for the most bearish extreme standing on the week. The Cocoa speculator level is at a 7 percent score of its 3-year range.

The six-week trend for the speculator strength score was a boost of 6 percentage points this week while the speculator position was -15,488 net contracts this week with a fall of -2,507 contracts in the weekly speculator bets.

Natural Gas

The Natural Gas speculator position comes in as third most bearish extreme standing of the week. The Natural Gas speculator level resides at a 9 percent score of its 3-year range.

The six-week trend for the speculator strength score was a decline by -5 percentage points this week and the overall speculator position was -192,196 net contracts this week with a decrease of -15,890 contracts in the speculator bets.

British Pound

The British Pound speculator position comes in as this week’s fourth most bearish extreme standing. The GBP speculator level is at a 12 percent score of its 3-year range.

The six-week trend for the speculator strength score was a decline of -3 percentage points this week. The speculator position was -64,307 net contracts this week with a reduction of -21,248 contracts in the weekly speculator bets.

New Zealand Dollar

Next, the New Zealand Dollar speculator position comes in as the fifth most bearish extreme standing for this week. The NZD speculator level is at a 18 percent score of its 3-year range.

The six-week trend for the speculator strength score was a decline of -5 percentage points this week. The speculator position was -40,613 net contracts this week with a decrease of -1,463 contracts in the weekly speculator bets.

Article By InvestMacro – Receive our weekly COT Reports by Email

All information and opinions on this website and contained in this article are for general informational purposes only and do not constitute investment advice.

COT Updates Opinions

22 May

Week Ahead: EURUSD inches toward make-or-break support

By ForexTime

EURUSD ↓ 1.1% YTD
Germany CPI + US PCE combo = fresh volatility?
EU Flash CPI forecast to trigger moves of ↑ 0.3% & ↓ 0.2%
US April PCE forecast to trigger moves of ↑ 0.5% & ↓ 0.6%
Bloomberg FX model – 71.1% EURUSD – (1.1510 – 1.1730)

The world’s most-traded FX pair is at a crossroads…and the stakes couldn’t be higher.

After a brief pause, EURUSD is heading straight for critical support at 1.1580.

This is the level that could define the pair’s direction for weeks to come. A clean break lower opens the door to steeper losses, while a firm bounce invites bulls back into the scene.

Bloomberg’s FX model puts a 71.1% probability of EURUSD trading within the 1.1510 – 1.1730 range this week – that’s a potential swing of over 100 pips.

Volatility is coming. The only question is which side it favours.

Why is the EURUSD under pressure

1) A broadly stronger dollar amid ongoing geopolitical risk, a limbo in peace talks and growing bets around higher US rates.

2) Technical forces may also be at play with prices trading below the 50, 100 and 200-day SMA.

Key inflation data from Germany and the United States could spell fresh opportunities for the EURUSD in the week ahead:

Monday, 25th May

• US Memorial Day holiday, with markets closed

Tuesday, 26th May

• GBP: UK CBI distributive trades

• USDInd: US Chicago Fed national activity index, CB consumer confidence, Dallas Fed manufacturing index, S&P/Case-Shiller home prices

Wednesday, 27th May

• AUD: Australia CPI, RBA trimmed mean CPI, construction work done

• EUR: Eurozone new car registrations

• USDInd: US MBA mortgage rates, ADP employment change, API crude oil inventories

• JPY: BoJ Governor Ueda speech

Thursday, 28th May

• KRW: South Korea interest rate decision

• EUR: Eurozone economic sentiment, Italy business and consumer confidence, Spain business confidence

• CAD: Canada current account, BoC financial stability report

• USDInd: US Core PCE, PCE inflation, GDP second estimate, durable goods orders, personal income, personal spending, initial jobless claims, new home sales, EIA crude oil inventories

Friday, 29th May

• JPY: Japan unemployment, industrial production, consumer confidence, retail sales

• GBP: UK Nationwide house prices

• EUR: France, Spain, Italy and Germany preliminary CPI, German unemployment

• CAD: Canada GDP

• USDInd: US goods trade balance, wholesale inventories, Chicago PMI

Here are 4 key themes that could rock EURUSD:

1. Ongoing Iran war

As the Iran war enters its 13th week, the global economy is absorbing the pressure from high energy prices and prolonged uncertainty.

While there seems to be some progress in talks, Tehran has publicly made it clear that it will not be handing over its enriched uranium stockpiles. Should tensions escalate, this could boost the dollar – enforcing downside pressure on the EURUSD.

2. US April PCE report – Thursday 28th May

It’s a big week for the United States due to a volley of economic reports including the latest PCE report.

The February US personal income and spending report including the PCE index — the Fed’s preferred inflation gauge — will offer key insight into the direction of price pressures.

Markets are forecasting PCE deflator YoY to jump3.9% in April with the core figure rising to 3.3% from 3.2%.

Ultimately, any signs of rising price pressure may reinforce bets around higher US interest rates.

Traders are currently pricing in a 77% probability of a 25-basis point cut by December.

The EURUSD may tumble on signs of rising price pressures in the United States.
A cooler-than-expected PCE report could boost the EURUSD.

3. Germany CPI report

A string of high impact data releases from Europe including the key CPI from Germany may provide critical insight into the economic outlook.

On Friday 29th May, the latest inflation figures from the largest country in Europe will be published with markets forecasting CPI to cool 2.8% YoY compared to 2.9% in the previous month.

Signs of rising inflationary pressures may reinforce bets around the ECB hiking as soon as June.

4. Technical forces

The EURUSD is under pressure on the daily charts with prices trading below the 50, 100 and 200-day SMA.

Should 1.1580 prove reliable support, this may trigger a rebound toward the 50-day SMA and 200-day SMA.

Weakness below 1.1580 could see a decline toward 1.1510 – the lower bound of Bloomberg’s FX model.

(Source BBG)

Bloomberg’s FX model points to a 71.1% chance that EURUSD will trade within the 1.1510 – 1.1730 range over the next one-week period.

Article by ForexTime

ForexTime Ltd (FXTM) is an award winning international online forex broker regulated by CySEC 185/12 www.forextime.com

Forex and Currency News Opinions

20 May

Nvidia earnings preview: In chips we trust…

By ForexTime

Nvidia shares only ↑18% year-to-date
Competition, data centre revenue and fiscal Q2 2027 guidance in focus
Shares could move 5.5% ↑ or ↓ post earnings
Analysts remain bullish with 12M target price at $277
Technical levels – $220, $235, $240

If Nvidia were a country, it would be the third-largest on Earth behind only US and China.

Let that sink in…

With a monster market cap fluctuating between $5.4 to $5.7 trillion, it now eclipses the entire silver market – making Nvidia the world’s second largest asset class. The only thing worth more than Nvidia is gold, which has been around for thousands of years.

So, when this tech titan reports earnings, the whole world is listening with the outcome either sparking an AI gold rush or reinforcing concerns about a bubble.

When & what to expect

Out today: Q1 FY2027 results drop after US markets close.

EPS: $1.78 expected vs $0.96 a year ago, an 85% jump.

Revenue: $79.15B forecast vs $44.1B last year, up 80%.

The call: Markets expect a beat-and-raise quarter. The real question is what Nvidia says next.

What to watch

Competition: Can Nvidia stay ahead as Google, AMD and Cerebras race to build rival AI chips?

Guidance: Q2 2027 outlook is everything — Wall Street wants $87.2B.

Data centre: Any update on its $1 trillion revenue target.

Diversification: Nvidia is moving into CPU-only computers — a big departure from its GPU roots.

China: Jensen Huang joined Trump’s trip to Beijing. What doors did that open?

How will Nvidia shares react to earnings

Markets are forecasting a 5.5% move, either Up or Down, for Nvidia stocks on Thursday post earnings.

This is equivalent to a move of almost $300 billion, bigger than the entire market cap of many large companies in the S&P500 and Nasdaq 100.

Analyst forecasts

According to Blomberg consensus, 95% of analysts are bullish on Nvidia with the 12-month price target at $277.19 – roughly 24% away from current prices.

Technical forces

A solid breakout above $235 may open a path toward fresh all-time highs at $240 and beyond.

Weakness below $220 could trigger a decline back toward $200.

Article by ForexTime

ForexTime Ltd (FXTM) is an award winning international online forex broker regulated by CySEC 185/12 www.forextime.com

Opinions Stock Market News

14 May

Button‑pushing explorers: How to grasp that AI agents can do amazing things while knowing nothing

By Ji Y. Son, California State University, Los Angeles and Alice Xu, University of California, Los Angeles

The nonprofit ARC Prize Foundation on May 1, 2026, released the results of a new benchmark: a test of an AI system’s ability to solve a game. The results were striking – humans scored 100%, while the most advanced AI systems scored under 1%.

At first glance, this may be surprising to users of AI who are impressed by its polished essays, codebases and multistep projects generated in seconds. How can these brilliant AI systems struggle with these simple Tetris-shape puzzles?

That confusion points to a risk: AI is becoming integrated into everyday life faster than people can make sense of it.

We are cognitive psychologists who study how to teach difficult concepts. To recognize the limits and risks of today’s AI agent systems, it’s important for people to grasp that the systems can both accomplish superhuman feats and make mistakes few humans would. To that end, we propose a new way to think about AIs: as button-pushing explorers.

Mental models for AI

We teach college students, a group rapidly incorporating AI tools into their daily routines. That gives us regular opportunities to ask what they think is going on with AI. The answers vary widely. One student said that someone at OpenAI or Anthropic is reading and approving every response the system generates. Another, more succinctly, said, “It’s magic.”

These responses illustrate two tempting ways of making sense of AI. At one extreme, AI is treated as an inscrutable black box – a powerful but ultimately mysterious force. At another, people explain it using the same assumptions they use to understand other humans: that its outputs reflect reasoning or judgment.

The worry is that these misinterpretations don’t go away as users gain more experience interacting with AI, and they might get reinforced. When AI performs well, its output can feel like evidence of understanding or confirmation that it really is something like magic. That apparent success makes it harder to question what the system is actually doing. Biases can seem logical or inevitable; harmful behavior can look like a deliberate choice or even fate, as if it could not have gone any other way.

Cognitive scientist Anil Seth explains why AIs don’t have – and won’t have – consciousness.

Saying that AI models are shaped by patterns in data, training processes and system design is true, but that’s too abstract to tell people when to trust the systems’ outputs or when they might fail. To help people avoid misplaced trust in AI, AI literacy efforts will need to include some mechanistic understanding of what produces their behavior – explanations that are perhaps not perfectly accurate but useful. Statistician George Box once wrote, “All models are wrong, but some are useful.”

Researchers have come up with several mental models for large language models. One is “stochastic parrot,” which shows that the models use statistical methods – stochastic refers to probabilities – to mimic responses with no understanding of meaning. Another is “bag of words,” which emphasizes that the models are collections of words – for example, all English words found on the internet – with a mechanism for giving you the best set of words based on your prompt.

These ways of thinking about large language models were never meant to be complete accounts of the systems. But the metaphors serve an important cognitive purpose: They push back against the idea that fluent language is necessarily caused by humanlike understanding.

But as the AI systems people use are increasingly powerful agents capable of stringing together actions on their own, it’s important for people to have a different kind of mental model: one that explains how they act. One place to find such a model is in earlier research on AI systems that learned to play Atari 2600 games. These systems didn’t understand the games the way humans do, but they still managed to rack up a lot of points.

The simple loop: Act, observe, adjust

Imagine a neural network, a relatively simple kind of AI model, placed into a video game it has never seen before. It does not “understand” the game like a human would. It has no idea whether it’s shooting space invaders or navigating an ancient pyramid. It doesn’t know the goals or rules.

Instead, it learns to play through a simple loop: Take an action – move left, jump, shoot – observe what changes, and then adjust. If an action leads to a good outcome, such as gaining points, it adjusts to become more likely to take similar actions in similar situations. If it leads to a bad outcome, such as losing a life, it adjusts in the opposite direction.

Even this simple mechanism can produce surprisingly capable behavior. Over time, by repeating this loop, the neural networks learned to play a wide range of Atari games – but not all games.

There is one game that famously stumped these early neural networks: Montezuma’s Revenge. To make progress, a player must carry out a long sequence of actions – climbing ladders, avoiding obstacles, retrieving keys – before receiving any reward at all. Unlike simpler games, most actions offer very little immediate feedback. The game required something like goal-directed, long-term planning.

Early neural networks would try a few actions, receive no reward and fail to make further progress through Montezuma’s underground pyramid. From the system’s perspective, all actions looked equally useless. But researchers made a breakthrough by changing the feedback signal. Instead of rewarding only success, they also rewarded the system for doing something new. The rewards were for visiting parts of the game it had not seen before or trying actions it had not previously taken. This tweak encouraged exploration.

In 2016, Google DeepMind rewarded its AI model for exploration – try something, see what happens, adjust – while playing the Atari 2600 game Montezuma’s Revenge, which dramatically improved the AI’s performance on the game that’s notoriously difficult for AIs.

With that change, performance improved dramatically. The neural network began navigating obstacles, taking multiple steps toward goals and adapting when things went wrong. From the outside, this kind of behavior can look like planning or problem-solving. But what looks like planning was not caused by sophisticated planning abilities. The underlying mechanism is still the same simple loop: act, observe, adjust.

This kind of system isn’t a stochastic parrot or a bag of words. It’s closer to a button-pushing explorer: something that doesn’t understand the world in a human sense but moves forward by pushing buttons, seeing what happens and adjusting what it does next.

From video games to modern AI agents

Today’s AI systems can do far more than play games like Montezuma’s Revenge. They can coordinate tools, write and run code, and carry out multistep projects. The range of possible actions is much larger, and the environments in which they operate are increasingly complex.

But these agents are still fundamentally button-pushing explorers. The behavior can be sophisticated, but the process that produces it is not. Humans can often infer how a new environment works after just a few observations. Systems that rely on these feedback loops cannot. They need to try many actions and see what happens before they can make progress.

This helps explain both the strengths of these AI systems and some of their most concerning failures. What these agents learn depends on what is being rewarded. And in real-world systems, those reward signals are often imperfect.

AI systems that conduct negotiations aim to maximize their client’s interests, sometimes with deceptive tactics. Rental pricing software used by landlords ends up price fixing. Marketing tools generate persuasive but misleading reviews.

These systems aren’t trying to be evil or greedy. They are adjusting to the signals they are given. From the button-pushing explorer perspective, these failures are downright predictable.

Effective AI literacy means holding two ideas at once: These systems can do surprisingly complex things, and they are not doing them the way humans do. If AI is seen as humanlike or magical, its outputs feel authoritative. But if it is understood, even imperfectly, as a button-pushing explorer shaped by feedback, people are likely to ask better questions: Why is it doing this? What shaped this behavior? What might it be missing?

That’s the difference between being impressed by AI and being able to reason about it.

About the Author:

Ji Y. Son, Professor of Psychology, California State University, Los Angeles and Alice Xu, Ph.D. Student in Developmental Psychology, University of California, Los Angeles

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

Opinions Programming

13 May

The missing link in America’s critical minerals push isn’t mining – it’s processing expertise

By Hélène Nguemgaing, University of Maryland and Alan Collins, West Virginia UniversityThe United States is spending billions of dollars to secure access to critical minerals – minerals and metals that are essential to modern technology, from electric vehicles to smartphones and military systems.

But amid the push to dig more, one question gets far too little attention: Who will actually process what comes out of the ground?

Between mining and the finished product lies a complex chain of separation, refining and advanced manufacturing. Since the 1990s, however, the United States has lost much of its critical mineral processing capacity.

Rebuilding domestic mineral supply chains will depend not only on resource availability and funding, but also on whether the U.S. can rebuild the technical expertise and industrial systems required to process those materials on a large scale.

MP Materials’ Mountain Pass mine and processing facility in California was for years the only U.S. rare earth elements mine.
Tmy350/Wikimedia Commons, CC BY-SA

How America lost its lead

The United States was a global leader in rare earth minerals from 1965 through the mid-1980s. It produced about 15,000 metric tons a year, about three times the amount produced by the rest of the world.

The Mountain Pass mine in California supplied the majority of the world’s rare earth elements used in electronics and the defense industry. American metallurgists, chemical engineers and processing facilities had significant expertise in its production and processing.

However, environmental damage, including wastewater pipeline leaks that released radioactive wastewater into the Mojave Desert during the 1980s and 1990s, and tightening regulations increased operating costs in the United States. During that period, much of the world’s manufacturing base for rare earth elements shifted to China, where labor costs were lower and environmental regulations were less stringent.

As production grew abroad, U.S. production of rare earth elements fell sharply – to near zero by the early 2000s, according to the U.S. Geological Survey.

In recent years, as much as 90% of the rare earth minerals extracted in the United States and allied countries have been shipped to China for processing. In 2024, the U.S. relied on imports for about 80% of its rare earth compounds and metals.

Why bringing processing back is not simple

The U.S. government is now pushing to increase domestic critical minerals production, citing national security. But building a processing facility is not like opening a warehouse.

These facilities require years of permitting, highly specialized equipment and a workforce trained in metallurgy, chemical engineering and industrial systems operation. The time from investment decision to production can stretch across a decade.

The U.S. currently has two domestic rare earth mining locations. One is in southeast Georgia, which extracts rare earth elements as a byproduct of heavy mineral sand mining. The other is Mountain Pass, which produces bastnaesite, a rare earth carbonate mineral. The mines produced about 51,000 metric tons of rare earth mineral concentrates in 2025, while the U.S. imported about 21,000 metric tons of rare earth compounds, most of them from China, according to 2025 U.S. Geological Survey data.

The U.S. has also lost expertise. Mining and mineral engineering education programs now produce only a few hundred graduates per year, well below the levels of past decades. The number of accredited programs has declined since the 1980s. Many faculty members are nearing retirement.

Industry projections estimate that the mining workforce will need to grow significantly in the coming years to meet rising demand. Specialized skills in areas such as rare earth separation, metallurgical testing and environmental systems design require years of training and practical experience. And while mining can produce high-paying jobs, the industry also has a reputation for environmental damage and hazardous conditions.

Environmental compliance is part of the skill set

Processing critical minerals is a dirty industry. That fact has made it more difficult for processing and refining companies to operate in the U.S.

For example, separating rare earth elements typically involves chemical processing with acids and solvents. When waste streams are poorly managed, these processes can produce toxic wastewater and air pollution and contribute to soil erosion. In parts of China where rare earth production expanded rapidly in the 1990s and 2000s, contamination from mining and processing has polluted rivers and damaged nearby farmland, and the wastewater can seep into soil and groundwater.

In the U.S., modern facilities must meet strict federal and state standards for air quality, water discharge and waste management that raise the cost of processing. These regulations were developed in response to environmental disasters, like the Cuyahoga River fire of 1969, when industrial oil and waste on the river burned, and hazardous waste crises like the Love Canal disaster that led to landmark environmental laws.

Operating a refinery or separation facility in compliance with regulatory standards today requires expertise in pollution control, waste treatment and sustainable process design. That requires a workforce skilled in materials science and engineering and with knowledge of environmental systems. Without environmental expertise, operational risks, regulatory challenges and project delays can increase, affecting long-term viability.

How to build a US supply chain

Rebuilding U.S. supply chains will require more than expanding extraction.

Canada’s critical minerals strategy offers an example. It connects mining projects to battery and electric vehicle manufacturing by funding processing facilities, developing regional supply chain hubs and investing in workforce training programs tied to those industries.

Australia has combined critical minerals policies with incentives and public financing to encourage domestic mineral processing, while also expanding university and vocational training in mining, metallurgy and mineral processing.

The United States has many of the key ingredients needed to rebuild its processing capacity, including research universities and workers with transferable industrial skills. Land-grant and technical universities could expand programs that integrate mining, materials science, environmental restoration and recycling. In regions such as Appalachia, where coal’s decline has left workers with skills but few job opportunities, retraining programs for new mineral recovery jobs could help people transition to a new industry.

A few federal programs support parts of this transition, including research hubs that develop new extraction and processing technologies, apprenticeship initiatives and university-industry partnerships. However, these efforts are spread across multiple agencies, with limited coordination to align priorities and investment.

The real bottleneck

America’s critical minerals strategy is often discussed in terms of geology and geopolitics – where resources are located and who has access to them.

But supply chains depend on people and systems. That’s America’s real bottleneck in creating a domestic supply chain.

A successful domestic supply chain will require workers who know how to separate neodymium from praseodymium, operate solvent extraction circuits and maintain hydrometallurgical plants within regulatory standards. These are highly specialized skills that take years to develop.

The United States has significant mineral resources and growing policy support. Now, it needs to pay attention to the workforce and industrial capacity needed to transform those resources into usable materials.

This gap developed over decades. Addressing it will likely require sustained investment alongside broader mineral policy changes such as permitting reforms and investment in domestic processing facilities.

About the Author:

Hélène Nguemgaing, Assistant Clinical Professor of Critical Resources & Sustainability Analytics, University of Maryland and Alan Collins, Professor of Natural Resource Economics, West Virginia University

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

Energy Opinions

13 May

Most people don’t know what they don’t know, but think they do – correcting your metaknowledge can make you a better teacher and learner

By Tommy Blanchard, Tufts University

Do you know what the Apple logo looks like?

Chances are, you think you do. It’s ubiquitous and iconic. How could you not know it?

But when tested, it turns out very few people can remember all the features of the logo. One study of 85 people found that only about half could pick the correct logo out of a lineup of similar ones. And only one person could correctly draw it.

This isn’t an isolated example. A classic study from 1979 found that people similarly couldn’t draw a penny accurately or pick out a correctly drawn penny from incorrect ones.

People aren’t just bad at remembering things they see all the time, but also in actually knowing how they work. In a 2006 study, many people made significant errors when drawing a bicycle, like putting the chain around the front wheel as well as the back wheel. More than just a forgotten detail, putting the chain around both wheels shows a deeper misunderstanding of how a bicycle works. A bicycle with a chain around both wheels wouldn’t be able to turn.

Illustration of bike with different components labeled — Do you truly know how a bicycle works?
Al2/Grandiose via Wikimedia Commons, CC BY-SA

It turns out people’s knowledge of how the world works is often fragmented and sketchy at best. They systematically overestimate their understanding of everyday devices and natural phenomena. People will tend to give themselves high ratings on how well they understand something, such as how bicycles or zippers work. But when they’re asked to actually explain the mechanics of these objects, their ratings of their understanding typically drop.

Just like how your knowledge of the world around you is imperfect, your knowledge about your own knowledge – also called metaknowledge – is often flawed. My field of cognitive science has been uncovering various gaps in human metaknowledge for decades.

If people are systematically overconfident about how well they understand things, why don’t they notice when they don’t understand something? And what can people do to better recognize the limits of their own knowledge?

The ability to say ‘I know that I know nothing’ could be considered a sign of wisdom.
Nicolas-André Monsiau/Pushkin Museum of Fine Arts via Wikimedia Commons

Why you think you know more than you do

Researchers have identified several factors behind people’s overconfidence in their knowledge.

One is that people confuse environmental support with understanding: The information is out in the world but not actually in your head. With a bicycle or a zipper, all of the parts are visible to you, and you may confuse this transparency for an internal understanding of how they work. But until you go to use that knowledge by attempting to explain how they work, you may not recognize that you don’t understand how those parts interact.

A second factor is confusing different levels of analysis. People can often describe how something works at a very high level. You know that the engine of a car makes the car go, and the brakes slow and stop the vehicle. But confidence in your high-level understanding of the car may bias you to think you also have a good grasp of the finer details, like how the engine pistons and brake pads work.

Additionally, people can be blind to the ways their knowledge shapes their own perception. In one study, researchers had participants tap out the tune to a popular song. On average, the tappers thought listeners would be able to identify the song about 50% of the time. But when listeners had to identify the tapped song, they actually could identify it only 2.5% of the time. The tappers didn’t realize how much their knowledge was making identifying the song seem easy to them.

A teacher talks to a student before a chalkboard wall filled with equations, chemical structures and graphs — Intellectual humility can help you see your expert blind spot.
Vitaly Gariev/Unsplash, CC BY-SA

This disconnect has consequences beyond whether someone else can understand your Morse code version of a song. When teaching people, whether in formal classroom settings or through casual mentorship, you can sometimes have an expert blind spot: the inability to recognize the difficulties beginners face when learning something you have expertise in.

Building expertise often involves internalizing knowledge to the point where it becomes invisible to you. You draw on knowledge you don’t realize you have, making it hard to relate to learners who lack this knowledge – and, of course, hard for learners to relate to your teaching. You might have experienced this when you’ve gotten partway through explaining something, only to realize you’ve been using jargon you forgot isn’t common knowledge and lost your listener.

How to address metaknowledge failures

Your metaknowledge can fail in two directions: You can think you know more than you do, and you can be blind to how much you’re relying on knowledge you do have. Each calls for a different response to correct it.

When you’re overconfident in your knowledge, the remedy is using that knowledge. You’ll quickly realize how much you actually understand and dial down your confidence. Challenging yourself to actually try to walk through how something works is a great exercise in intellectual humility – that is, recognizing that you may be wrong – and can keep you from getting out over your skis.

Building a greater appreciation for what you know is more difficult. You can’t simply unlearn what you’ve internalized. But what this challenge shows is that, to some extent, knowing a subject and knowing how to teach it are two separate skills. Some experts are great teachers, but not simply by virtue of being experts. Recognizing that you have to approach teaching with humility, and that your expertise doesn’t automatically make you a skilled teacher, can go a long way toward making you a better teacher and mentor.

These aren’t easy and quick fixes to failures of metaknowledge. Both require ongoing intellectual humility and a willingness to distrust your own confidence. But acknowledging the fallibility of your own metaknowledge is a good place to start.

About the Author:

Tommy Blanchard, Research Associate in Cognitive Science, Tufts University

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

Opinions Psychology

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