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Archive for Opinions – Page 5

31 Dec

An AI system has reached human level on a test for ‘general intelligence’. Here’s what that means

By Michael Timothy Bennett, Australian National University and Elija Perrier, Stanford University 

A new artificial intelligence (AI) model has just achieved human-level results on a test designed to measure “general intelligence”.

On December 20, OpenAI’s o3 system scored 85% on the ARC-AGI benchmark, well above the previous AI best score of 55% and on par with the average human score. It also scored well on a very difficult mathematics test.

Creating artificial general intelligence, or AGI, is the stated goal of all the major AI research labs. At first glance, OpenAI appears to have at least made a significant step towards this goal.

While scepticism remains, many AI researchers and developers feel something just changed. For many, the prospect of AGI now seems more real, urgent and closer than anticipated. Are they right?

Generalisation and intelligence

To understand what the o3 result means, you need to understand what the ARC-AGI test is all about. In technical terms, it’s a test of an AI system’s “sample efficiency” in adapting to something new – how many examples of a novel situation the system needs to see to figure out how it works.

An AI system like ChatGPT (GPT-4) is not very sample efficient. It was “trained” on millions of examples of human text, constructing probabilistic “rules” about which combinations of words are most likely.

The result is pretty good at common tasks. It is bad at uncommon tasks, because it has less data (fewer samples) about those tasks.

Until AI systems can learn from small numbers of examples and adapt with more sample efficiency, they will only be used for very repetitive jobs and ones where the occasional failure is tolerable.

The ability to accurately solve previously unknown or novel problems from limited samples of data is known as the capacity to generalise. It is widely considered a necessary, even fundamental, element of intelligence.

Grids and patterns

The ARC-AGI benchmark tests for sample efficient adaptation using little grid square problems like the one below. The AI needs to figure out the pattern that turns the grid on the left into the grid on the right.

Several patterns of coloured squares on a black grid background.
An example task from the ARC-AGI benchmark test.
ARC Prize

Each question gives three examples to learn from. The AI system then needs to figure out the rules that “generalise” from the three examples to the fourth.

These are a lot like the IQ tests sometimes you might remember from school.

Weak rules and adaptation

We don’t know exactly how OpenAI has done it, but the results suggest the o3 model is highly adaptable. From just a few examples, it finds rules that can be generalised.

To figure out a pattern, we shouldn’t make any unnecessary assumptions, or be more specific than we really have to be. In theory, if you can identify the “weakest” rules that do what you want, then you have maximised your ability to adapt to new situations.

What do we mean by the weakest rules? The technical definition is complicated, but weaker rules are usually ones that can be described in simpler statements.

In the example above, a plain English expression of the rule might be something like: “Any shape with a protruding line will move to the end of that line and ‘cover up’ any other shapes it overlaps with.”

Searching chains of thought?

While we don’t know how OpenAI achieved this result just yet, it seems unlikely they deliberately optimised the o3 system to find weak rules. However, to succeed at the ARC-AGI tasks it must be finding them.

We do know that OpenAI started with a general-purpose version of the o3 model (which differs from most other models, because it can spend more time “thinking” about difficult questions) and then trained it specifically for the ARC-AGI test.

French AI researcher Francois Chollet, who designed the benchmark, believes o3 searches through different “chains of thought” describing steps to solve the task. It would then choose the “best” according to some loosely defined rule, or “heuristic”.

This would be “not dissimilar” to how Google’s AlphaGo system searched through different possible sequences of moves to beat the world Go champion.

You can think of these chains of thought like programs that fit the examples. Of course, if it is like the Go-playing AI, then it needs a heuristic, or loose rule, to decide which program is best.

There could be thousands of different seemingly equally valid programs generated. That heuristic could be “choose the weakest” or “choose the simplest”.

However, if it is like AlphaGo then they simply had an AI create a heuristic. This was the process for AlphaGo. Google trained a model to rate different sequences of moves as better or worse than others.

What we still don’t know

The question then is, is this really closer to AGI? If that is how o3 works, then the underlying model might not be much better than previous models.

The concepts the model learns from language might not be any more suitable for generalisation than before. Instead, we may just be seeing a more generalisable “chain of thought” found through the extra steps of training a heuristic specialised to this test. The proof, as always, will be in the pudding.

Almost everything about o3 remains unknown. OpenAI has limited disclosure to a few media presentations and early testing to a handful of researchers, laboratories and AI safety institutions.

Truly understanding the potential of o3 will require extensive work, including evaluations, an understanding of the distribution of its capacities, how often it fails and how often it succeeds.

When o3 is finally released, we’ll have a much better idea of whether it is approximately as adaptable as an average human.

If so, it could have a huge, revolutionary, economic impact, ushering in a new era of self-improving accelerated intelligence. We will require new benchmarks for AGI itself and serious consideration of how it ought to be governed.

If not, then this will still be an impressive result. However, everyday life will remain much the same.The Conversation

About the Author:

Michael Timothy Bennett, PhD Student, School of Computing, Australian National University and Elija Perrier, Research Fellow, Stanford Center for Responsible Quantum Technology, Stanford University

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

 

Opinions Programming
30 Dec

NASA’s micro-mission Lunar Trailblazer will make macro-measurements of the lunar surface in 2025

By César León Jr., Washington University in St. Louis 

NASA’s upcoming Artemis II mission is slated to return astronauts to the Moon no sooner than April 2026. Astronauts were last on the Moon in 1972 during the Apollo 17 mission.

Artemis II will utilize NASA’s Space Launch System, which is an extremely powerful rocket that will enable human space exploration beyond Earth’s atmosphere. The crew of four will travel in an Orion spacecraft, which the agency launched around the Moon and successfully returned during the Artemis I mission.

But before Artemis II, NASA will send two missions to scout the surface of the lunar south pole for resources that could sustain human space travel and enable new scientific discoveries.

Planetary geologists like me are interested in data from Lunar Trailblazer, one of these two scouting missions. The data from this mission will help us understand how water forms and behaves on rocky planets and moons.

Starting with scientific exploration

PRIME-1, or the Polar Resources Ice Mining Experiment, will be mounted on a lunar lander. It’s scheduled for launch in January 2025.

Aboard the lander are two instruments: The Regolith and Ice Drill for Exploring New Terrain, TRIDENT, and the Mass Spectrometer for Observing Lunar Operations, MSOLO. TRIDENT will dig down up to 3 feet (1 meter) and extract samples of lunar soil, and MSOLO will evaluate the soil’s chemical composition and water content.

Joining the lunar mining experiment is Lunar Trailblazer, a satellite launching on the same Falcon 9 rocket.

Think of this setup as a multimillion-dollar satellite Uber pool, or a rideshare where multiple missions share a rocket and minimize fuel usage while escaping Earth’s gravitational pull.

Bethany Ehlmann, a planetary scientist, is the principal investigator of Lunar Trailblazer and is leading an operating team of scientists and students from Caltech’s campus. Trailblazer is a NASA Small, Innovative Mission for PLanetary Exploration, or SIMPLEx.

These missions intend to provide practical operations experience at a lower cost. Each SIMPLEx mission is capped at a budget of US$55 million – Trailblazer is slightly over budget at $80 million. Even over budget, this mission will cost around a quarter of a typical robotic mission from NASA’s Discovery Program. Discovery Program missions typically cost around $300 million, with a maximum budget of $500 million.

Building small but mighty satellites

Decades of research and development into small satellites, or SmallSats, opened the possibility for Trailblazer. SmallSats take highly specific measurements and complement data sourced from other instruments.

A diagram showing four small satellites scanning Earth's science and taking layers of science data.
Missions like NASA’s TROPICS use a network of small satellites to take more data than one satellite would be able to do alone.
NASA Applied Sciences

Multiple SmallSats working together in a constellation can take various measurements simultaneously for a high-resolution view of the Earth’s or Moon’s surface.

SIMPLEx missions can use these SmallSats. Because they’re small and more affordable, they allow researchers to study questions that come with a higher technical risk. Lunar Trailblazer, for example, uses commercial off-the-shelf parts to keep the cost down.

These low-cost, high-risk experimental missions may help geologists further understand the origin of the solar system, as well as what it’s made of and how it has changed over time. Lunar Trailblazer will focus specifically on mapping the Moon.

A brief timeline of water discoveries on the Moon

Scientists have long been fascinated by the surface of our closest celestial neighbor, the Moon. As early as the mid-17th century, astronomers mischaracterized ancient volcanic eruptions as lunar mare, derived from the Latin word for “seas.”

Nearly two centuries later, astronomer William Pickering’s calculations suggested that the Moon had no atmosphere. This led him to conclude the Moon could not have water on its surface, as that water would vaporize.

However, in the 1990s, NASA’s Clementine mission detected water on the Moon. Clementine was the first mission to completely map the surface of the Moon, including the lunar poles. This data detected the presence of ice within permanently shadowed regions on the Moon in low resolution.

Scientists’ first water detection prompted further exploration. NASA launched the Lunar Prospector in 1998 and the Lunar Reconnaissance Orbiter in 2009. The India Space Research Organization launched its Chandrayaan-1 mission with the Moon Mineralogy Mapper, M3, instrument in 2008. M3, although not designed to detected liquid water, unexpectedly did find it in sunlit areas on the Moon.

These missions collectively provided maps showing how hydrous minerals – minerals containing water molecules in their chemical makeup – and ice water are distributed on the lunar surface, particularly in the cold, dark, permanently shadowed regions.

Novel mission, novel science

But how does the temperature and physical state of water on the Moon change from variations in sunlight and crater shadows?

Lunar Trailblazer will host two instruments, the Lunar Thermal Mapper, LTM, and an evolution of the M3 instrument, the High-resolution Volatiles and Minerals Moon Mapper, HVM3.

The LTM instrument will map surface temperature, while the HVM3 will measure how lunar rocks absorb light. These measurements will allow it to detect and distinguish between water in liquid and ice forms.

In tandem, these instruments will provide thermal and chemical measurements of hydrous lunar rock. They’ll measure water during various times of the lunar day, which is about 29.5 Earth days, to try to show how the chemical composition of water varies depending on the time of day and where it is on the Moon.

These results will tell researchers what phase – solid or liquid – the water is found in.

Scientific significance and what’s next

There are three leading theories for where lunar water came from. It could be water that’s been stored inside the Moon since its formation, in its mantle layer. Some geologic processes may have allowed it to slowly escape to the surface over time.

Or, the water may have arrived on asteroids and comets that collided with the lunar surface. It may even have been created by interactions with the solar wind, which is a stream of particles that comes from the Sun.

Lunar Trailblazer may shed light on these theories and help researchers make progress on several other big science questions, including how water behaves on rocky bodies like the Moon and whether future astronauts will be able to use it.The Conversation

About the Author:

César León Jr., Ph.D. Student of Planetary Geology, Washington University in St. Louis

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

 

Economics & Fundamentals Opinions
30 Dec

Language AIs in 2024: Size, guardrails and steps toward AI agents

By John Licato, University of South Florida 

I research the intersection of artificial intelligence, natural language processing and human reasoning as the director of the Advancing Human and Machine Reasoning lab at the University of South Florida. I am also commercializing this research in an AI startup that provides a vulnerability scanner for language models.

From my vantage point, I observed significant developments in the field of AI language models in 2024, both in research and the industry.

Perhaps the most exciting of these are the capabilities of smaller language models, support for addressing AI hallucination, and frameworks for developing AI agents.

Small AIs make a splash

At the heart of commercially available generative AI products like ChatGPT are large language models, or LLMs, which are trained on vast amounts of text and produce convincing humanlike language. Their size is generally measured in parameters, which are the numerical values a model derives from its training data. The larger models like those from the major AI companies have hundreds of billions of parameters.

There is an iterative interaction between large language models and smaller language models, which seems to have accelerated in 2024.

First, organizations with the most computational resources experiment with and train increasingly larger and more powerful language models. Those yield new large language model capabilities, benchmarks, training sets and training or prompting tricks. In turn, those are used to make smaller language models – in the range of 3 billion parameters or less – which can be run on more affordable computer setups, require less energy and memory to train, and can be fine-tuned with less data.

No surprise, then, that developers have released a host of powerful smaller language models – although the definition of small keeps changing: Phi-3 and Phi-4 from Microsoft, Llama-3.2 1B and 3B, and Qwen2-VL-2B are just a few examples.

These smaller language models can be specialized for more specific tasks, such as rapidly summarizing a set of comments or fact-checking text against a specific reference. They can work with their larger cousins to produce increasingly powerful hybrid systems.

What are small language model AIs – and why would you want one?

Wider access

Increased access to highly capable language models large and small can be a mixed blessing. As there were many consequential elections around the world in 2024, the temptation for the misuse of language models was high.

Language models can give malicious users the ability to generate social media posts and deceptively influence public opinion. There was a great deal of concern about this threat in 2024, given that it was an election year in many countries.

And indeed, a robocall faking President Joe Biden’s voice asked New Hampshire Democratic primary voters to stay home. OpenAI had to intervene to disrupt over 20 operations and deceptive networks that tried to use its models for deceptive campaigns. Fake videos and memes were created and shared with the help of AI tools.

Despite the anxiety surrounding AI disinformation, it is not yet clear what effect these efforts actually had on public opinion and the U.S. election. Nevertheless, U.S. states passed a large amount of legislation in 2024 governing the use of AI in elections and campaigns.

Misbehaving bots

Google started including AI overviews in its search results, yielding some results that were hilariously and obviously wrong – unless you enjoy glue in your pizza. However, other results may have been dangerously wrong, such as when it suggested mixing bleach and vinegar to clean your clothes.

Large language models, as they are most commonly implemented, are prone to hallucinations. This means that they can state things that are false or misleading, often with confident language. Even though I and others continually beat the drum about this, 2024 still saw many organizations learning about the dangers of AI hallucination the hard way.

Despite significant testing, a chatbot playing the role of a Catholic priest advocated for baptism via Gatorade. A chatbot advising on New York City laws and regulations incorrectly said it was “legal for an employer to fire a worker who complains about sexual harassment, doesn’t disclose a pregnancy or refuses to cut their dreadlocks.” And OpenAI’s speech-capable model forgot whose turn it was to speak and responded to a human in her own voice.

Fortunately, 2024 also saw new ways to mitigate and live with AI hallucinations. Companies and researchers are developing tools for making sure AI systems follow given rules pre-deployment, as well as environments to evaluate them. So-called guardrail frameworks inspect large language model inputs and outputs in real time, albeit often by using another layer of large language models.

And the conversation on AI regulation accelerated, causing the big players in the large language model space to update their policies on responsibly scaling and harnessing AI.

But although researchers are continually finding ways to reduce hallucinations, in 2024, research convincingly showed that AI hallucinations are always going to exist in some form. It may be a fundamental feature of what happens when an entity has finite computational and information resources. After all, even human beings are known to confidently misremember and state falsehoods from time to time.

The rise of agents

Large language models, particularly those powered by variants of the transformer architecture, are still driving the most significant advances in AI. For example, developers are using large language models to not only create chatbots, but to serve as the basis of AI agents. The term “agentic AI” shot to prominence in 2024, with some pundits even calling it the third wave of AI.

To understand what an AI agent is, think of a chatbot expanded in two ways: First, give it access to tools that provide the ability to take actions. This might be the ability to query an external search engine, book a flight or use a calculator. Second, give it increased autonomy, or the ability to make more decisions on its own.

For example, a travel AI chatbot might be able to perform a search of flights based on what information you give it, but a tool-equipped travel agent might plan out an entire trip itinerary, including finding events, booking reservations and adding them to your calendar.

AI agents can perform multiple steps of a task on their own.

In 2024, new frameworks for developing AI agents emerged. Just to name a few, LangGraph, CrewAI, PhiData and AutoGen/Magentic-One were released or improved in 2024.

Companies are just beginning to adopt AI agents. Frameworks for developing AI agents are new and rapidly evolving. Furthermore, security, privacy and hallucination risks are still a concern.

But global market analysts forecast this to change: 82% of organizations surveyed plan to use agents within 1-3 years, and 25% of all companies currently using generative AI are likely to adopt AI agents in 2025.The Conversation

About the Author:

John Licato, Associate Professor of Computer Science, Director of AMHR Lab, University of South Florida

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

Opinions Programming
22 Dec

US Dollar Index Speculator bets rise for 1st time in 7 weeks, AUD bets plunge

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 December 17th 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 Bets led by Swiss Franc & EuroFX

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

Leading the gains for the currency markets was the Swiss Franc (13,192 contracts) with the EuroFX (9,678 contracts), the US Dollar Index (8,865 contracts), the Mexican Peso (6,686 contracts) and Bitcoin (891 contracts) also having positive weeks.

The currencies seeing declines in speculator bets on the week were the Australian Dollar (-70,016 contracts), the Japanese Yen (-19,791 contracts), the New Zealand Dollar (-14,300 contracts), the British Pound (-5,478 contracts), the Brazilian Real (-4,544 contracts) and with the Canadian Dollar (-501 contracts) also recording lower bets on the week.

US Dollar Index Speculator bets rise for 1st time in 7 weeks, AUD bets plunge

Highlighting the COT currency’s data this week is the increase in the speculator’s positioning in the US Dollar Index. The large speculative US Dollar Index positions jumped this week for the first time in the past seven weeks and by the highest weekly amount (+8,865 contracts) since June.

The Dollar Index bets had fallen for seven straight weeks and spec positions were in bearish territory for the past five weeks before this week’s gain. Now, the Dollar Index is back in a bullish standing and at the highest level since September. Speculators had been cutting their bullish bets despite the strong buying action in the markets for the Dollar.

The Dollar Index futures (DX) rose again this week for a third consecutive week and have now been higher in ten out of the past twelve weeks. This week’s high level over 108 is the highest point touched since 2022 and the DX managed to close over the 107.00 level for the first time since November.

Australian dollar bets plunge

The Australian dollar speculator positions fell by the most on record this week with a huge drop by -70,016 contracts. This surpasses the previous most bearish weekly plunge of -56,065 contracts that took place in 2007 around the time of the Great Financial Crisis.

The Australian Dollar exchange versus the US Dollar has been falling sharply with declines in eleven out of the past twelve weeks as well. The AUD exchange level closed at 0.6267 on Friday, marking the lowest close since the fourth quarter of 2022.

The strong Dollar has been laying waste to most of the other major currencies as the Euro, Canadian dollar, Australian dollar, New Zealand dollar, Mexican peso and the Brazilian real are all trading at or near multi-year lows.

Currencies Net Speculators Leaderboard

Legend: Weekly Speculators Change | Speculators Current Net Position | Speculators Strength Score compared to last 3-Years (0-100 range)

Strength Scores led by Japanese Yen & Swiss Franc

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 Japanese Yen (76 percent) and the Swiss Franc (57 percent) lead the currency markets this week. Bitcoin (55 percent) comes in as the next highest in the weekly strength scores.

On the downside, the New Zealand Dollar (0 percent), the EuroFX (4 percent), the Canadian Dollar (6 percent) and the US Dollar Index (18 percent) come in at the lowest strength levels currently and are in Extreme-Bearish territory (below 20 percent).

3-Year Strength Statistics:
US Dollar Index (18.4 percent) vs US Dollar Index previous week (0.0 percent)
EuroFX (3.7 percent) vs EuroFX previous week (0.0 percent)
British Pound Sterling (45.8 percent) vs British Pound Sterling previous week (48.3 percent)
Japanese Yen (76.0 percent) vs Japanese Yen previous week (83.9 percent)
Swiss Franc (56.7 percent) vs Swiss Franc previous week (30.0 percent)
Canadian Dollar (6.4 percent) vs Canadian Dollar previous week (6.6 percent)
Australian Dollar (32.6 percent) vs Australian Dollar previous week (82.3 percent)
New Zealand Dollar (0.0 percent) vs New Zealand Dollar previous week (19.5 percent)
Mexican Peso (36.2 percent) vs Mexican Peso previous week (32.8 percent)
Brazilian Real (32.2 percent) vs Brazilian Real previous week (36.5 percent)
Bitcoin (55.0 percent) vs Bitcoin previous week (35.6 percent)

Bitcoin & Japanese Yen top the 6-Week Strength Trends

COT Strength Score Trends (or move index, calculates the 6-week changes in strength scores) showed that Bitcoin (36 percent) and the Japanese Yen (20 percent) lead the past six weeks trends for the currencies. The Swiss Franc (17 percent) and the US Dollar Index (11 percent) are the next highest positive movers in the 3-Year trends data.

The Australian Dollar (-66 percent) leads the downside trend scores currently with the New Zealand Dollar (-47 percent), EuroFX (-17 percent) and the British Pound (-11 percent) following next with lower trend scores.

3-Year Strength Trends:
US Dollar Index (11.5 percent) vs US Dollar Index previous week (-10.2 percent)
EuroFX (-16.8 percent) vs EuroFX previous week (-9.6 percent)
British Pound Sterling (-10.5 percent) vs British Pound Sterling previous week (-17.6 percent)
Japanese Yen (20.0 percent) vs Japanese Yen previous week (20.2 percent)
Swiss Franc (16.6 percent) vs Swiss Franc previous week (-2.0 percent)
Canadian Dollar (-3.1 percent) vs Canadian Dollar previous week (-6.3 percent)
Australian Dollar (-65.6 percent) vs Australian Dollar previous week (-13.5 percent)
New Zealand Dollar (-46.8 percent) vs New Zealand Dollar previous week (-35.5 percent)
Mexican Peso (-8.4 percent) vs Mexican Peso previous week (-13.8 percent)
Brazilian Real (-8.0 percent) vs Brazilian Real previous week (-11.5 percent)
Bitcoin (35.5 percent) vs Bitcoin previous week (25.1 percent)

Individual COT Forex Markets:

US Dollar Index Futures:

US Dollar Index Forex Futures COT ChartThe US Dollar Index large speculator standing this week recorded a net position of 5,641 contracts in the data reported through Tuesday. This was a weekly rise of 8,865 contracts from the previous week which had a total of -3,224 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 18.4 percent. The commercials are Bullish with a score of 79.6 percent and the small traders (not shown in chart) are Bearish with a score of 41.3 percent.

Price Trend-Following Model: Strong Uptrend

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

US DOLLAR INDEX StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:60.925.19.1
– Percent of Open Interest Shorts:47.242.95.0
– Net Position:5,641-7,3491,708
– Gross Longs:25,14510,3533,753
– Gross Shorts:19,50417,7022,045
– Long to Short Ratio:1.3 to 10.6 to 11.8 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):18.479.641.3
– Strength Index Reading (3 Year Range):Bearish-ExtremeBullishBearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:11.5-14.418.4

 

Euro Currency Futures:

Euro Currency Futures COT ChartThe Euro Currency large speculator standing this week recorded a net position of -65,895 contracts in the data reported through Tuesday. This was a weekly increase of 9,678 contracts from the previous week which had a total of -75,573 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 97.7 percent and the small traders (not shown in chart) are Bearish-Extreme with a score of 10.6 percent.

Price Trend-Following Model: Strong Downtrend

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

EURO Currency StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:26.158.412.3
– Percent of Open Interest Shorts:37.450.19.3
– Net Position:-65,89548,08917,806
– Gross Longs:152,671340,83772,006
– Gross Shorts:218,566292,74854,200
– Long to Short Ratio:0.7 to 11.2 to 11.3 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):3.797.710.6
– Strength Index Reading (3 Year Range):Bearish-ExtremeBullish-ExtremeBearish-Extreme
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-16.816.2-7.4

 

British Pound Sterling Futures:

British Pound Sterling Futures COT ChartThe British Pound Sterling large speculator standing this week recorded a net position of 21,647 contracts in the data reported through Tuesday. This was a weekly reduction of -5,478 contracts from the previous week which had a total of 27,125 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 45.8 percent. The commercials are Bullish with a score of 54.9 percent and the small traders (not shown in chart) are Bullish with a score of 50.9 percent.

Price Trend-Following Model: Strong Downtrend

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

BRITISH POUND StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:47.238.313.3
– Percent of Open Interest Shorts:35.647.016.2
– Net Position:21,647-16,258-5,389
– Gross Longs:88,26571,72824,964
– Gross Shorts:66,61887,98630,353
– Long to Short Ratio:1.3 to 10.8 to 10.8 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):45.854.950.9
– Strength Index Reading (3 Year Range):BearishBullishBullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-10.515.6-33.4

 

Japanese Yen Futures:

Japanese Yen Forex Futures COT ChartThe Japanese Yen large speculator standing this week recorded a net position of 5,961 contracts in the data reported through Tuesday. This was a weekly decline of -19,791 contracts from the previous week which had a total of 25,752 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 76.0 percent. The commercials are Bearish with a score of 27.4 percent and the small traders (not shown in chart) are Bullish with a score of 56.8 percent.

Price Trend-Following Model: Strong Downtrend

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

JAPANESE YEN StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:48.031.419.1
– Percent of Open Interest Shorts:44.733.120.7
– Net Position:5,961-3,101-2,860
– Gross Longs:87,20857,10734,688
– Gross Shorts:81,24760,20837,548
– Long to Short Ratio:1.1 to 10.9 to 10.9 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):76.027.456.8
– Strength Index Reading (3 Year Range):BullishBearishBullish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:20.0-18.50.6

 

Swiss Franc Futures:

Swiss Franc Forex Futures COT ChartThe Swiss Franc large speculator standing this week recorded a net position of -21,800 contracts in the data reported through Tuesday. This was a weekly advance of 13,192 contracts from the previous week which had a total of -34,992 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 56.7 percent. The commercials are Bullish with a score of 65.3 percent and the small traders (not shown in chart) are Bearish-Extreme with a score of 0.0 percent.

Price Trend-Following Model: Strong Downtrend

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

SWISS FRANC StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:19.771.78.0
– Percent of Open Interest Shorts:45.224.629.7
– Net Position:-21,80040,379-18,579
– Gross Longs:16,87261,4076,853
– Gross Shorts:38,67221,02825,432
– Long to Short Ratio:0.4 to 12.9 to 10.3 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):56.765.30.0
– Strength Index Reading (3 Year Range):BullishBullishBearish-Extreme
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:16.61.4-38.3

 

Canadian Dollar Futures:

Canadian Dollar Forex Futures COT ChartThe Canadian Dollar large speculator standing this week recorded a net position of -182,055 contracts in the data reported through Tuesday. This was a weekly reduction of -501 contracts from the previous week which had a total of -181,554 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 6.4 percent. The commercials are Bullish-Extreme with a score of 96.6 percent and the small traders (not shown in chart) are Bearish-Extreme with a score of 2.6 percent.

Price Trend-Following Model: Strong Downtrend

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

CANADIAN DOLLAR StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:3.988.85.9
– Percent of Open Interest Shorts:41.448.58.7
– Net Position:-182,055195,457-13,402
– Gross Longs:19,170430,97228,710
– Gross Shorts:201,225235,51542,112
– Long to Short Ratio:0.1 to 11.8 to 10.7 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):6.496.62.6
– Strength Index Reading (3 Year Range):Bearish-ExtremeBullish-ExtremeBearish-Extreme
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-3.16.0-20.4

 

Australian Dollar Futures:

Australian Dollar Forex Futures COT ChartThe Australian Dollar large speculator standing this week recorded a net position of -61,531 contracts in the data reported through Tuesday. This was a weekly decrease of -70,016 contracts from the previous week which had a total of 8,485 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 32.6 percent. The commercials are Bullish with a score of 72.3 percent and the small traders (not shown in chart) are Bearish with a score of 23.9 percent.

Price Trend-Following Model: Strong Downtrend

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

AUSTRALIAN DOLLAR StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:19.164.412.7
– Percent of Open Interest Shorts:54.723.318.3
– Net Position:-61,53171,099-9,568
– Gross Longs:32,929111,27621,997
– Gross Shorts:94,46040,17731,565
– Long to Short Ratio:0.3 to 12.8 to 10.7 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):32.672.323.9
– Strength Index Reading (3 Year Range):BearishBullishBearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-65.661.3-27.3

 

New Zealand Dollar Futures:

New Zealand Dollar Forex Futures COT ChartThe New Zealand Dollar large speculator standing this week recorded a net position of -42,507 contracts in the data reported through Tuesday. This was a weekly reduction of -14,300 contracts from the previous week which had a total of -28,207 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 0.0 percent. The commercials are Bullish-Extreme with a score of 100.0 percent and the small traders (not shown in chart) are Bearish-Extreme with a score of 4.9 percent.

Price Trend-Following Model: Strong Downtrend

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

NEW ZEALAND DOLLAR StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:11.684.13.7
– Percent of Open Interest Shorts:64.026.98.4
– Net Position:-42,50746,323-3,816
– Gross Longs:9,37068,1082,967
– Gross Shorts:51,87721,7856,783
– Long to Short Ratio:0.2 to 13.1 to 10.4 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):0.0100.04.9
– Strength Index Reading (3 Year Range):Bearish-ExtremeBullish-ExtremeBearish-Extreme
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-46.847.8-30.5

 

Mexican Peso Futures:

Mexican Peso Futures COT ChartThe Mexican Peso large speculator standing this week recorded a net position of 14,614 contracts in the data reported through Tuesday. This was a weekly rise of 6,686 contracts from the previous week which had a total of 7,928 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 36.2 percent. The commercials are Bullish with a score of 67.9 percent and the small traders (not shown in chart) are Bearish-Extreme with a score of 10.1 percent.

Price Trend-Following Model: Downtrend

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

MEXICAN PESO StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:43.850.32.9
– Percent of Open Interest Shorts:32.260.14.7
– Net Position:14,614-12,332-2,282
– Gross Longs:55,52963,9013,680
– Gross Shorts:40,91576,2335,962
– Long to Short Ratio:1.4 to 10.8 to 10.6 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):36.267.910.1
– Strength Index Reading (3 Year Range):BearishBullishBearish-Extreme
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-8.47.510.1

 

Brazilian Real Futures:

Brazil Real Futures COT ChartThe Brazilian Real large speculator standing this week recorded a net position of -20,937 contracts in the data reported through Tuesday. This was a weekly decrease of -4,544 contracts from the previous week which had a total of -16,393 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 32.2 percent. The commercials are Bullish with a score of 70.1 percent and the small traders (not shown in chart) are Bearish-Extreme with a score of 13.1 percent.

Price Trend-Following Model: Strong Downtrend

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

BRAZIL REAL StatisticsSPECULATORSCOMMERCIALSSMALL TRADERS
– Percent of Open Interest Longs:41.055.62.7
– Percent of Open Interest Shorts:67.826.94.5
– Net Position:-20,93722,320-1,383
– Gross Longs:31,93143,3122,103
– Gross Shorts:52,86820,9923,486
– Long to Short Ratio:0.6 to 12.1 to 10.6 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):32.270.113.1
– Strength Index Reading (3 Year Range):BearishBullishBearish-Extreme
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:-8.08.4-3.0

 

Bitcoin Futures:

Bitcoin Crypto Futures COT ChartThe Bitcoin large speculator standing this week recorded a net position of 171 contracts in the data reported through Tuesday. This was a weekly boost of 891 contracts from the previous week which had a total of -720 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 55.0 percent. The commercials are Bullish with a score of 51.4 percent and the small traders (not shown in chart) are Bearish with a score of 33.4 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:77.84.03.9
– Percent of Open Interest Shorts:77.45.23.1
– Net Position:171-483312
– Gross Longs:33,0731,7201,639
– Gross Shorts:32,9022,2031,327
– Long to Short Ratio:1.0 to 10.8 to 11.2 to 1
NET POSITION TREND:
– Strength Index Score (3 Year Range Pct):55.051.433.4
– Strength Index Reading (3 Year Range):BullishBullishBearish
NET POSITION MOVEMENT INDEX:
– 6-Week Change in Strength Index:35.5-35.2-16.2

 

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
21 Dec

Speculator Extremes: New Zealand Dollar, Euro & CAD lead Bearish 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 December 17th.

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)

 

Here Are This Week’s Most Bullish Speculator Positions:

Lean Hogs


The Lean Hogs speculator position comes in as the most bullish extreme standing this week. The Lean Hogs speculator level is currently at a 100.0 percent score of its 3-year range.

The six-week trend for the percent strength score totaled 17.0 this week. The overall net speculator position was a total of 93,410 net contracts this week with a rise of 1,888 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.

Live Cattle


The Live Cattle speculator position comes next in the extreme standings this week. The Live Cattle speculator level is now at a 98.4 percent score of its 3-year range.

The six-week trend for the percent strength score was 34.5 this week. The speculator position registered 110,778 net contracts this week with a weekly gain by 8,077 contracts in speculator bets.

Nasdaq


The Nasdaq speculator position comes in third this week in the extreme standings. The Nasdaq speculator level resides at a 95.1 percent score of its 3-year range.

The six-week trend for the speculator strength score came in at 31.0 this week. The overall speculator position was 36,082 net contracts this week with an increase by 509 contracts in the weekly speculator bets.

Ultra U.S. Treasury Bonds


The Ultra U.S. Treasury Bonds speculator position comes up number four in the extreme standings this week. The Ultra U.S. Treasury Bonds speculator level is at a 90.6 percent score of its 3-year range.

The six-week trend for the speculator strength score totaled a change of 17.2 this week. The overall speculator position was -219,304 net contracts this week with a change of -2,932 contracts in the speculator bets.

Coffee


The Coffee speculator position rounds out the top five in this week’s bullish extreme standings. The Coffee speculator level sits at a 86.5 percent score of its 3-year range. The six-week trend for the speculator strength score was -0.8 this week.

The speculator position was 62,147 net contracts this week with an edge higher by 73 contracts in the weekly speculator bets.

This Week’s Most Bearish Speculator Positions:

New Zealand Dollar


The New Zealand Dollar speculator position comes in as the most bearish extreme standing this week. The New Zealand Dollar speculator level is at a 0.0 percent score of its 3-year range.

The six-week trend for the speculator strength score was -46.8 this week. The overall speculator position was -42,507 net contracts this week with a drop by -14,300 contracts in the speculator bets.

Euro


The Euro speculator position comes in next for the most bearish extreme standing on the week. The Euro speculator level is at a 3.7 percent score of its 3-year range.

The six-week trend for the speculator strength score was -16.8 this week. The speculator position was -65,895 net contracts this week with a rise of 9,678 contracts in the weekly speculator bets.

Canadian Dollar


The Canadian Dollar speculator position comes in as third most bearish extreme standing of the week. The Canadian Dollar speculator level resides at a 6.4 percent score of its 3-year range.

The six-week trend for the speculator strength score was -3.1 this week. The overall speculator position was -182,055 net contracts this week with a dip of -501 contracts in the speculator bets.

Soybean Meal


The Soybean Meal speculator position comes in as this week’s fourth most bearish extreme standing. The Soybean Meal speculator level is at a 9.0 percent score of its 3-year range.

The six-week trend for the speculator strength score was -24.3 this week. The speculator position was -44,844 net contracts this week with a decline by -15,616 contracts in the weekly speculator bets.

5-Year Bond


Finally, the 5-Year Bond speculator position comes in as the fifth most bearish extreme standing for this week. The 5-Year Bond speculator level is at a 11.7 percent score of its 3-year range.

The six-week trend for the speculator strength score was 0.3 this week. The speculator position was -1,762,317 net contracts this week with a rise of 28,113 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 Forex and Currency News Opinions
20 Dec

Argentina’s soaring poverty levels don’t seem to be hurting president Javier Milei – but the honeymoon could be over

By Nicolas Forsans, University of Essex 

Argentina, a nation once ranked among the wealthiest in the world, has found itself grappling with severe economic challenges over the past 25 years. Then, one year ago, provocative libertarian economist Javier Milei was inaugurated as its new president.

Known for his flamboyant persona and radical views, Milei is one of the most polarising figures in global politics, celebrated by some as a visionary reformer and dismissed by others as “El Loco” (“the mad one”). He pledged to take a “chainsaw” to the state and promote a free-market approach.

His pro-capitalism stance extends to the promotion of culture wars. Last month, he fired his foreign secretary for voting along with 186 other countries against the US embargo on Cuba at the United Nations. Only the US and Israel voted against it. He withdrew Argentina’s delegation of negotiators to the UN climate summit in Baku, claiming human-caused climate change is “a socialist lie”.

Yet Milei owes his 2023 victory to Argentina’s deep economic crisis. It was an economy suffering from the third highest inflation rate in the world, at 211% year on year, a poverty rate north of 40% (it’s now climbed even higher), and an economy in crisis for decades.

Argentina’s economic woes are deeply rooted. Once one of the world’s richest nations thanks to its fertile Pampas plains, its prosperity was built on agricultural exports and integration into global markets.

Political instability, excessive protectionism and fiscal mismanagement disrupted its trajectory. Peronism, a political movement based on economic independence and social justice, has dominated Argentine politics for decades. While it lifted the working class, critics argue it entrenched inefficiency and dependence on the state.

By 2023, Argentina’s crisis had reached unprecedented levels and the peso had lost most of its value.

Argentines turned to Milei, an outsider who pledged to dismantle the state’s bloated bureaucracy, privatise key sectors and adopt policies rooted in libertarian principles.

Sweeping reforms and painful cuts

Now in power for a year, he has slashed government spending by a third, dismantling price controls and cutting subsidies on energy and transport. Last December, he devalued the peso by 54%.

Around 30,000 state jobs were cut, as were more than half of government ministries. Milei also allowed inflation to eat into the real value of pensions and salaries. This has generated fiscal surpluses, but also deepened the country’s worst economic crisis in two decades.

The result is unprecedented levels of poverty. As the cost of food and basic products increased, around 53% of Argentines now live in poverty – up from around 42% in 2023 and the highest level in 30 years. Another 15% of the population is in “extreme poverty”. An extra 5.5 million Argentines became poor during Milei’s first six months in office.

Despite the pain, Milei’s approval ratings have remained stable at around 50%. His success seems to rest on his unrelenting attacks on the country’s establishment and workers’ unions. The only large-scale protests occurred when Milei imposed cuts to free public universities. Argentines seem to have accepted the doctor’s prescription.

Milei’s key legislative victory was his controversial “omnibus” reform bill. This was originally aimed at slashing government spending, privatising public entreprises (whether or not they were profitable) and enforcing a zero-deficit policy.

Although the bill was watered down, economic indicators improved significantly. Monthly inflation dropped to 2.7% in October from its peak of 26% last December. The peso has strengthened considerably and is now overvalued, hurting exporters and raising the prospect of a devaluation – and with it, more inflation. Argentina’s country risk index (which measures the risk of investing in a state) has fallen significantly.

But the economy is not out of the woods. Growth remains elusive – the IMF forecast a 3.5% economic contraction this year. Growth of 5.2% next year will only return per-capita GDP, a measure of individual wealth, to where it was by the time COVID lockdowns ended in 2021. Reducing inflation further won’t be easy, as it has hovered around the 3% monthly level since July.

Meanwhile, Milei’s 2025 budget proposal aims for a budget surplus of over 1.3% of the country’s GDP, requiring further spending cuts. But calls to restart frozen public works and boost pensions and wages will inevitably grow louder next year.

And Argentina still has heavy capital controls, making it hard for investors to get money out of the country. They will think twice before investing.

Meanwhile, the opposition is waking up. Milei’s veto of the bill increasing university budgets brought 250,000 people out in protest in November, prompting some to suggest the president had miscalculated.

Former president Cristina Fernández de Kirchner, still Argentina’s dominant leftist, is poised to take over the leadership of main Peronist party ahead of next year’s midterm elections. While her influence has greatly diminished, she still enjoys reasonable approval ratings. Both Kirchner and Milei are polarising figures, so it is unclear if her return will help the left.

The re-election of Donald Trump could prove to be Milei’s best card. While Argentina is a small trade partner, Milei will leverage his relationship with the US president-elect to convince the IMF to roll over the remainder of the US$44 billion debt (£35 billion) acquired in 2018 during Trump’s first term in office. Another US$10 billion is needed to bolster the central bank’s international reserves which remain critically low.

This source of money will be critical for Milei to start lifting capital controls. Only then can economic stability translate into sustainable growth.The Conversation

About the Author:

Nicolas Forsans, Professor of Management and Co-director of the Centre for Latin American & Caribbean Studies, University of Essex

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

 

Economics & Fundamentals Opinions
20 Dec

Week Ahead: US500 “Santa Rally” still in play?

By ForexTime 

  • US500 ↑ 23% year-to-date
  • December: Produced returns 70% of time since 1995
  • Gained on average 1% in December over past 30 years
  • Prices bearish on D1 but RSI oversold
  • Technical levels: 21-Day SMA,5900 & 100-day SMA

FXTM’s US500, which tracks the benchmark S&P 500 index is on track for its worst trading week since September.

But bulls could make a return if the “Santa Claus rally” kicks off in the week ahead:

Saturday, 21st December

  • Deadline for avoiding partial US government shutdown
  • CN50: China’s National People’s Congress standing committee

Monday, 23rd December

  • SG20: Singapore CPI
  • TWN: Taiwan industrial production, jobless rate
  • GBP: UK GDP (final)
  • USDInd: US Conference Board consumer confidence

Tuesday, 24th December

  • AU200: RBA meeting minutes
  • JP225: BoJ meeting minutes

Wednesday, 25th December

  • Stock markets closed – Christmas Day

Thursday, 26th December

  • Boxing Day Holiday
  • SG20: Singapore industrial production
  • US500: US initial jobless claims

Friday, 27th December

  • JP225: Japan Tokyo CPI, unemployment, industrial production, retail sales

The lowdown…

US equities tumbled on Wednesday following the Fed’s hawkish pivot.

Interest rates were cut as widely expected but the Fed signalled a slower pace of easing in 2025.

Traders are now only pricing in a 54% probability of a 25 basis point Fed cut by March 2025 with this jumping to 75% by May 2025. 

This sent the US500 tumbling 3%, dragging prices below 5900 for the first time since mid-November.

US5001

US equity bears are back in the scene with the threat of a potential partial US government shutdown weighing on sentiment.

The question is whether the latest developments have reduced the chance of a Santa rally?

What is the Santa rally?

This financial phenomenon is where stocks generally gain in the last week of December and the first two trading days of the new year.

It’s unclear whether this is fueled by psychology or triggered by underlying financial forces.

Nevertheless, history has shown that this is a recurring seasonal pattern.

Indeed, December has been a historically positive month for the S&P500 which has produced positive returns 70% of the time since 1995.

On average, over the past 30 years the S&P 500 has delivered returns of 1% in December.

 

The bigger picture…

The US500 is up 23% year-to-date – its second straight year of returns above 20%.

It has notched 57 record highs thanks to the AI mania, Fed rate cuts and Trump’s election win.

A Santa Clause rally could push prices back toward the psychological 6000 level, paving a path back to 6100 and higher.

 

Technical forces

The sharp selloff last Wednesday has placed bears in a position of power. Prices are trading below the 21 and 50-day SMA but the Relative Strength Index (RSI) is near oversold levels.

  • Sustained weakness below 5900 may encourage a decline toward the 100-day SMA and 5700.
  • A move back above 5900 could trigger an incline toward 21-day SMA and 6100.

US500 23

Forex-Time-LogoArticle by ForexTime

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

Economics & Fundamentals Opinions
19 Dec

The US Federal Reserve cut rates by 0.25% but signaled a more hawkish approach next year.

By JustMarkets

At Wednesday’s close, the Dow Jones Index (US30) was down 2.58%. The S&P 500 Index (US500) fell by 2.95%. The Nasdaq Technology Index (US100) lost 3.60%. The US stocks fell on Wednesday as the Federal Reserve cut interest rates by 25 bps but signaled fewer cuts than previous estimates for next year, triggering a market sell-off. A widely expected Fed rate cut to the target range of 4.25%–4.5% was overshadowed by an estimate that the rate would be cut by just two points in 2025, down from the four previously expected, dampening investor sentiment. As the Central Bank lowered its unemployment prognosis and raised expectations for core inflation and economic growth, Treasury yields rose sharply, putting additional pressure on stock prices. The odds of pausing rate cuts in January rose to 88%, up from 80% before the FOMC decision. The US dollar strengthened, with the biggest gains against the Australian dollar, euro, British pound, and yen.

Equity markets in Europe were mostly up on Wednesday. The German DAX (DE40) was down 0.02%, the French CAC 40 (FR40) closed up 0.26%, the Spanish IBEX 35 (ES35) added 0.26%, and the British FTSE 100 (UK100) closed up 0.05%. The Eurozone’s annualized inflation rate for November 2024 rose to 2.2% from 2% in October but below the 2.3% preliminary estimate. The increase towards the end of the year was expected mainly due to base effects, as last year’s sharp decline in energy prices is no longer factored into the annualized rate. Annual core inflation was confirmed at 2.7%, which aligns with the forward data. The UK’s annual core inflation rate rose to 3.5% in November 2024, up from 3.3% in the previous month, the highest since August. However, the figure was slightly below market estimates of 3.6%. The annualized services CPI remained unchanged at 5.0%.

In the oil market, data from the EIA showed that US crude oil inventories fell by nearly 1 million barrels in the second week of December, extending a 1.4 million barrel decline from the previous week. In addition, according to the same report, the US oil exports rose to 1.8 million barrels, the highest since July. In turn, other reports indicated that Kazakhstan intends to honor the lengthy oil production cuts mandated by OPEC+ for next year, abandoning previous signals that it would increase output to an initial level of 190,000 barrels per day. This added to the signal that other members of the organization, notably the UAE, were sticking to extending the production cuts.

Asian markets traded flat yesterday. Japan’s Nikkei 225 (JP225) lost 0.72%, China’s FTSE China A50 (CHA50) added 1.06%, Hong Kong’s Hang Seng (HK50) increased by 0.83%, and Australia’s ASX 200 (AU200) was negative 0.06%.

New Zealand’s economy contracted by 1% in September 2024, which was worse than the 0.4% contraction expected by the market. This is the second consecutive quarter of contraction and the sharpest contraction since September 2021. On an annualized basis, GDP fell by 1.5% after 0.5% contraction in the second quarter. The New Zealand dollar hit a two-year low on the back of this data, as well as a rise in the Dollar Index.

The Australian dollar fell to its lowest level in more than two years, after a hawkish rate cut by the US Federal Reserve, which strengthened the dollar. Further pressure came from weak economic data from China and the risk of renewed US tariffs under a possible Trump administration, given Australia’s close trade ties with China. Domestically, concerns over slowing economic activity persist, with Australian Consumer Confidence declining and markets raising expectations for the Reserve Bank of Australia’s (RBA) first rate cut amid growing signs of economic weakness.

Bank Indonesia kept its benchmark interest rate at 6% at its December 2024 meeting, in line with market expectations. The decision reflects the Central Bank’s desire to keep inflation under control within the target range of 2.5%, plus-minus 1%, for 2024 and 2025, as well as stabilize the rupiah exchange rate amid heightened global uncertainty. Indonesia’s annual inflation rate fell to 1.55% in November 2024 from 1.71% in the previous month, the lowest since July 2021, and remained within the target range.

The Bank of Thailand kept its key interest rate unchanged at 2.25% at its final meeting in 2024 after an unexpected 25 bps cut in October, as expected. The decision was made against the backdrop of accelerating inflation and GDP growth and maintaining long-term macro-financial stability. Inflation remained below the Central Bank’s target for most of this year, but rose to a six-month high of 0.95% in November, nearing the lower end of the 1–3% target range.

S&P 500 (US500) 5,872.16 −178.45 (−2.95%)

Dow Jones (US30) 42,326.87 −1,123.03 (−2.58%)

DAX (DE40) 20,242.57 −3.80 (−0.02%)

FTSE 100 (UK100) 8,199.11 +3.91 (+0.05%)

USD Index 108.25 +1.30 (+1.21%)

News feed for: 2024.12.19

  • Japan BoJ Interest Rate Decision at 05:00 (GMT+2);
  • Japan BoJ Monetary Policy Statement at 05:00 (GMT+2);
  • Japan BoJ Press Conference at 06:30 (GMT+2);
  • German GfK Consumer Confidence (m/m) at 09:00 (GMT+2);
  • Sweden Riksbank Rate Decision (m/m) at 10:30 (GMT+2);
  • Norway Norges Bank Rate Decision (m/m) at 11:00 (GMT+2);
  • UK BoE Interest Rate Decision at 14:00 (GMT+2);
  • UK BoE  Monetary Policy Statement at 14:00 (GMT+2);
  • US GDP (q/q) at 15:30 (GMT+2);
  • US Initial Jobless Claims (w/w) at 15:30 (GMT+2);
  • US Existing Home Sales (m/m) at 17:00 (GMT+2);
  • US Natural Gas Storage (w/w) at 17:30 (GMT+2);
  • Mexico Banxico Interest Rate Decision at 21:00 (GMT+2);
  • New Zealand Trade Balance (m/m) at 23:45 (GMT+2).

By JustMarkets

 

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Economics & Fundamentals Opinions
19 Dec

When AI goes shopping: AI agents promise to lighten your purchasing load − if they can earn your trust

By Tamilla Triantoro, Quinnipiac University 

Online shopping often involves endless options and fleeting discounts. A single search for running shoes can yield hundreds of results across multiple platforms, each promising the “best deal.” The holiday season brings excitement, but it also brings a blend of decision fatigue and logistical nightmares.

What if there were a tool capable of hunting for the best prices, navigating endless sales and making sure your purchases arrive on time?

The next evolution in artificial intelligence is AI agents that are capable of autonomous reasoning and multistep problem-solving. AI shopping agents not only suggest what you might like, but they can also act on your behalf. Major retailers and AI companies are developing AI shopping assistants, and the AI company Perplexity released Buy with Pro on Nov. 18, 2024.

Picture this: You prompt AI to find a winter coat under $200 that’s highly rated and will arrive by Sunday. In seconds, it scans websites, compares prices, checks reviews, confirms availability and places the order, all while you go about your day.

image of a webpage showing two small photos of women's coats
Perpelexity’s recently released AI shopping agent can search for items across the web using multiple free-form variables sucgh as color, size, price and shipping time.
Screenshot by Tamilla Triantoro

Unlike traditional recommendation engines, AI agents learn your preferences and handle tasks autonomously. The agents are built with machine learning and natural language processing. They learn from their interactions with the people using them and become smarter and more efficient over time from their collective interactions.

Looking ahead, AI agents are likely to not only master personal shopping needs but also negotiate directly with corporate AI systems. They will not only learn your preferences but will likely be able to book tailored experiences, handle payments across platforms and coordinate schedules.

As a researcher who studies human-AI collaboration, I see how AI agents could make the future of shopping virtually effortless and more personalized than ever.

How AI agents help shoppers

Marketplaces such as Amazon and Walmart have been using AI to automate shopping. Google Lens offers a visual search tool for finding products.

Perplexity’s Buy with Pro is a more powerful AI shopping agent. By providing your shipping and billing information, you can place orders directly on the Perplexity app with free shipping on every order. The shopping assistant is part of the company’s Perplexity Pro service, which has free and paid tiers.

For those looking to build custom AI shopping agents, AutoGPT and AgentGPT are open-source tools for configuring and deploying AI agents.

Consumers today are focused on value, looking for deals and comparing prices across platforms. Having an assistant perform these tasks could be a tremendous time saver. But can AI truly learn your preferences?

A recent study using the GPT-4o model achieved 85% accuracy in imitating the thoughts and behaviors of over 1,000 people after they interacted with the AI for just two hours. This breakthrough finding suggests that digital personas can understand and act on people’s preferences in ways that will transform the shopping experience.

How AI shopping reshapes business

AI agents are moving beyond recommendations to autonomously executing complex tasks such as automating refunds, managing inventory and approving pricing decisions. This evolution has already begun to reshape how businesses operate and how consumers interact with them.

Retailers using AI agents are seeing measurable benefits. Since October 2024, data from the Salesforce shopping index reveals that digital retailers using generative AI achieved a 7% increase in average order revenue and attributed 17% of global orders to AI-driven personalized recommendations, targeted promotions and improved customer service.

Meanwhile, the nature of search and advertising is undergoing a major shift. Amazon is capturing billions of dollars in ad revenue as shoppers bypass Google to search directly on its platform. Simultaneously, AI-powered search tools such as Perplexity and OpenAI’s web-enabled chat deliver instant, context-aware responses, challenging traditional search engines and forcing advertisers to rethink their strategies.

The outcome of the battle between Big Tech and open-source initiatives to shape the AI ecosystem is also likely to affect how the shopping experience changes.

image of a webpage showing two small photos of insulated travel mugs
Shoppers can have back-and-forth interactions with AI agents.
Screenshot by Tamilla Triantoro

The risks: Privacy, manipulation and dependency

While AI agents offer significant benefits, they also raise critical privacy concerns. AI systems require extensive access to personal data, shopping history and financial information. This level of access increases the risk of misuse and unauthorized sharing.

Manipulation is another issue. AI can be highly persuasive and may be optimized to serve corporate interests over consumer welfare. Such technology can prioritize upselling or nudging shoppers toward higher-margin products under the guise of personalization.

There’s also the risk of dependency. Automating many aspects of shopping could diminish the satisfaction of making choices. Research in human-AI interaction indicates that while AI tools can reduce cognitive load, increased reliance on AI could impair people’s ability to critically evaluate their options.

What’s next?

AI-based shopping is still in its infancy, so how much trust should you place in it?

In our book “Converging Minds,” AI researcher Aleksandra Przegalinska and I argue for a balanced and critical approach to AI adoption, recognizing both its potential and its pitfalls.

As cognitive scientist Gary Marcus points out, AI’s moral limitations stem from technical constraints: Despite efforts to prevent errors, these systems remain imperfect.

This cautious perspective is reflected in the responses from my MBA class. When I asked students whether they were ready to outsource their holiday shopping to AI, the answer was an overwhelming no. Ethan Mollick, a professor at the Wharton School at the University of Pennsylvania, has argued that the adoption of AI in everyday life will be gradual, as societal change typically lags behind technological advancement.

Before people are willing to hand over their credit cards and let AI take the reins, businesses will have to ensure that AI systems align with human values and priorities. The promise of AI is vast, but to fulfill that promise I believe that AI will need to be an extension of human intention – not a replacement for it.The Conversation

About the Author:

Tamilla Triantoro, Associate Professor of Business Analytics and Information Systems, Quinnipiac University

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

 

Opinions Programming
18 Dec

What’s next for Albertsons after calling off its $25B grocery merger with Kroger: More lawsuits

By Christine P. Bartholomew, University at Buffalo 

Albertsons announced on Dec. 11, 2024, that it had called off an attempted merger with Kroger and would sue Kroger for breach of contract. The US$25 billion deal, first announced in 2022, would have combined Cincinnati-based Kroger, already the largest traditional U.S. supermarket chain, with Boise, Idaho-based Albertsons, which is currently the third-biggest grocer.

The Conversation U.S. asked Christine P. Bartholomew, a professor at the University at Buffalo School of Law who researches consumer protection, to explain how the merger failed and why it matters.

Which supermarkets belong to the two companies?

Kroger has 28 subsidiaries with nearly 2,800 supermarkets, including Harris Teeter, Dillon’s, Smith’s, King Soopers, Fry’s, City Market, Owen’s, JayC, Pay Less, Baker’s Gerbes, Pick‘n Save, Metro Market, Mariano’s Fresh Market, QFC, Ralphs and Fred Meyer.

Albertsons owns and operates more than 2,200 supermarkets through its many brands. They include Safeway, Vons, Jewel-Osco, Shaw’s, Acme, Tom Thumb, Randalls, United Supermarkets, Pavilions, Star Market, Haggen, Carrs, Kings Food Market and Balducci’s.

Kroger and Albertsons also operate supermarkets branded with their own names.

Had the merger gone forward, it would have been the largest of its kind in U.S. history, affecting millions of grocery shoppers.

To ward off regulators’ concerns, prior to canceling the transaction, the chains announced in 2023 a plan to sell hundreds of their supermarkets across the United States to C&S Wholesale Grocers. They updated this plan in 2024, pledging to not close any stores.

Why did Kroger want to acquire Albertsons?

The companies argued that they needed to join forces to compete against even bigger online and big box retailers. In recent years, Walmart and Costco have gained market share, while other chains have held steady or lost ground.

The companies also feared stiff competition from dollar stores, one of the fastest-growing segments of U.S. retail.

The federal government opposed the merger, with the U.S. Federal Trade Commission suing to block it. Had the deal gone through, the new company would have cemented its position, ensuring it has the largest market share for grocery purchases after Walmart.

What happened in court?

In February 2024, the FTC, along with state attorneys general representing consumers in eight states – Arizona, California, Illinois, Maryland, Nevada, New Mexico, Oregon and Wyoming – filed a federal lawsuit in Oregon to block the merger. So did the District of Columbia’s attorney general.

This wasn’t the only legal challenge the merger faced. The Washington and Colorado attorneys general both filed suit in their own states to block the merger.

After hearings in both cases and months of uncertainty, the judges in both Oregon and Washington issued their rulings.

U.S. District Court Judge Adrienne Nelson, in Portland, Oregon, on Dec. 10, which blocked the merger pending the outcome of the administrative proceedings before the FTC.

A few hours later, Judge Marshall Ferguson in Seattle issued a permanent injunction barring the merger in Washington state only. Both judges determined that the merger risked significantly reducing competition and that the companies didn’t offer enough evidence that the merger would help consumers.

“We’re standing up to mega-monopolies to keep prices down,” Ferguson said. He called the injunction “an important victory for affordability, worker protections and the rule of law.”

Albertsons and Kroger’s plan to offload stores to C&S didn’t impress the judges. Not only did Nelson find the divestiture insufficient in scale, but she ruled it was “structured in a way that will significantly disadvantage C&S as a competitor.”

Albertsons v. Kroger

The morning after the Washington and Oregon decisions were issued, the deal was dead.

Albertsons announced it terminated the merger agreement, citing the court decisions.

Both companies still face significant legal challenges, though. Five minutes after announcing its intent to back out of the deal, Albertsons issued a second press release announcing it had filed a lawsuit against Kroger.

Albertsons said Kroger willfully breached the deal “by repeatedly refusing to divest assets necessary for antitrust approval, ignoring regulators’ feedback, rejecting stronger divestiture buyers and failing to cooperate with Albertsons.” The suit seeks significant damages, including “billions of dollars” for lost shareholder value and legal costs, as well as a $600 million merger breakup fee.

In response, Kroger said that “Albertsons’ claims are baseless and without merit.”

Albertsons’ suit against Kroger is pending in Delaware Court of Chancery, which hears many legal business disputes. The complaint remains temporarily under seal.

This article includes passages that appeared in an article about the proposed merger that was published on Feb. 28, 2024.The Conversation

About the Author:

Christine P. Bartholomew, Professor of Law, University at Buffalo

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

 

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