Category: 3. Business

Now and then, people get excited about news they should’ve expected. That’s what’s happening with this new wave of optimism about US–China trade. Suddenly, the Singapore dollar is firming up, ASEAN equities are bouncing, and headlines are popping up with phrases like “renewed confidence” and “regional momentum.”

We have nothing against good news. But if you’re an investor or anyone hoping to understand what’s happening in the markets, you must separate noise from signal. And most importantly, you need to know what lasts.

Trade normalization is rational

US–China trade cooperation is not a breakthrough. It’s just two superpowers realizing that full-scale economic divorce is not wise. Tariffs hurt both sides, and deglobalization is expensive. The smarter move is to keep competing and keep trading at the same time.

That’s why the markets are cheering. According to a December 2024 report from Krungsri Research, ASEAN countries have picked up the slack as businesses look to diversify out of China. It’s the classic “China+1” model. These companies don’t abandon China but hedge their bets by expanding elsewhere. Vietnam, Malaysia, and Indonesia are at the center of that trend. So is Singapore, in a more refined, financial way.

You don’t have to be a genius to see what this means: more capital, trade, and flows into regional currencies like the Singapore dollar. But again, this isn’t some magical transformation. It’s just basic arithmetic.

Singapore dollar shows strength

The Singapore dollar (SGD) has been firming up lately. Why? Because Singapore has credibility. It manages its currency through a basket-based exchange rate system that reacts to inflation and economic conditions, not short-term political moves.

When capital starts flowing into ASEAN as a side effect of China-US sanity returning, the SGD is one of the first to benefit. Investors like clarity, and Singapore offers that. Add in decent inflation control and a solid current account, and it is no surprise that the SGD is strong, outpacing peers like the Thai baht and Malaysian ringgit. That’s earned trust.

ASEAN markets get a positive return

Markets love optimism. ASEAN equities have seen a healthy uptick in the first half of 2025, especially in logistics, tech, and manufacturing. Vietnam’s FPT Corp, which handles cloud infrastructure and digital services, is up over 18 percent. Thanks to EV demand, Indonesia’s nickel sector is drawing investment from every direction.

The US Chamber of Commerce and ADB note that the ASEAN region is now a strategic production hub. That’s because it’s not China, but it’s not far from it either. It’s a diversified, young, cheap labor market with governments that mostly want foreign money.

But before you jump in headfirst, remember that it’s not supposed to be easy. There are power supply issues in Vietnam and Indonesia, as well as complex land laws that require careful navigation.

So yes, the trend is real, but will it last?

Don’t confuse movement with progress

A lot of people get excited when markets heat up. But if you’re playing this game seriously, you need to ask yourself: what is changing underneath?

You want structural reforms. You want legal systems that work. You want power grids that don’t black out every time demand spikes. Without those things, the ASEAN story stalls.

Now, some of those improvements are happening. The Regional Comprehensive Economic Partnership (RCEP) is making it easier for goods to move around the region. The Digital Economy Framework Agreement (DEFA) aims to make ASEAN a digital powerhouse by 2030. These are serious moves—if they stick.

But again, you must look at what’s real and what’s wishful thinking. Is the infrastructure being built? Is corruption going down? Are rules being enforced?

Most people don’t ask the crucial questions. That’s why these people are taking more risks with their money.

Where the long-term value might be

If you’re looking for something to own, here’s what might be interesting:

• Singapore-listed firms with real cash flow and exposure to ASEAN expansion.
• Digital infrastructure players across ASEAN data centers, telecoms, and logistics.
• Selective ETFs like iShares MSCI ASEAN, which give you diversified exposure, though they’re far from perfect.

And always consider currency effects. If you’re holding Malaysian or Vietnamese assets, be aware of the risks in their currencies. SGD offers a little protection, but nothing’s bulletproof.

Use smart tools to spot opportunities

There’s a saying in poker: if you can’t spot the sucker in the first 30 minutes, it’s you. The same applies to investing.

The return of US–China trade optimism is good. The SGD firming up is a clear result of it. The ASEAN market rally makes sense on paper. But don’t kid yourself. Optimism isn’t a strategy. The winners are the ones studying what’s happening behind the scenes.

Smart tools like TradingView Singapore help you play the game wisely. With no delays, TradingView lets you monitor currency pairs like SGD/USD or SGD/VND in real time. You get snapshots of daily, monthly, and annual moves, technical indicators, and crowd-sourced analysis.

More importantly, it allows you to:

• Set price or volume alerts directly on the SGD chart so you’re notified the moment your conditions are met
• Use built-in tools like oscillators and moving averages to spot trends and reversals
• Explore the “Ideas” section, where traders share real-world insights and strategies that can sharpen your thinking
• Trade directly through integrated brokers like Saxo or CMC using one login and one interface

So use smart tools. Watch the fundamentals. Read the boring reports. Ask the hard questions. If you want to win in the markets, be skeptical of anything that sounds too easy.

Trade hopes don’t make an economy

The US–China trade cheer has turned up the volume in ASEAN, lifting currencies, stocks, and spirits. The Singapore dollar benefits from being the adult in the room, and ASEAN markets are finally getting the attention they’ve earned.

But optimism isn’t a strategy. Long-term value only comes from hard-earned advantages. These include good governance, innovative infrastructure, and sound currency management. ASEAN has parts of that, and Singapore has most of it. But don’t get carried away by headlines.

You make money by understanding what’s real, not blindly following the crowd.

 

BBC

Vivergo/ PA Media

‘Way forward’

A compensation scheme for drivers over the mis-selling of car loans could cost as much as £18bn, the financial regulator has said.

The Supreme Court ruled on Friday that hidden commissions from lenders to dealers on car loans were not unlawful, meaning millions of motorists will not be able to claim.

However, the judgement left open the possibility of compensation claims for particularly large commissions which the Supreme Court said were unfair.

Following the ruling, the Financial Conduct Authority (FCA) has said it will consult on running a payout scheme – estimated to cost between £9bn and £18bn.

It said that “most individuals will probably receive less than £950 in compensation”, with the first payouts expected next year if the scheme goes ahead.

Those who have already complained do not need to do anything, the FCA said, advising those who have yet to complain to contact their car loan provider rather than using a claims management company.

The regulator has said it will consult on who will be eligible for the payouts, adding that the Supreme Court ruling provided “clarity”.

On Friday, the Supreme Court reversed earlier court rulings which said that hidden commissions on car loans were unlawful.

However, it ruled in favour of Marcus Johnson, saying the commission paid to the dealer was so significant – 55% of the total charge or credit including interest and fees – that it was a “powerful indication” the relationship between Mr Johnson and lender FirstRand was unfair.

The Supreme Court awarded Mr Johnson the amount of a commission plus interest.

The FCA said Friday’s judgement “helps us because we have been looking at what is unfair and, prior to this judgment, there were different interpretations of the law coming from different courts”.

High doses of aumolertinib, a third-generation EGFR tyrosine kinase inhibitor (TKI), conferred a long-term survival benefit among patients with untreated EGFR-variant non–small cell lung cancer (NSCLC) and brain metastases, according to findings from the phase 2 ACHIEVE trial (NCT04808752) published in JAMA Oncology.¹

After a median follow-up of 28.8 months (95% CI, 27.0-29.8), the median progression-free survival (PFS) was 20.5 months (95% CI, 12.0-26.9), which included a 12-month rate of 62.1% (95% CI, 48.7%-73.0%) and a 24-month rate of 40.8% (95% CI, 28.2%-53.1%). With a data maturity rate of 33.3%, the median intracranial PFS was not reached (NR; 95% CI, 22.3-NR); the 12-month and 24-month rates were 76.8% (95% CI, 63.2%-85.9%) and 62.3% (95% CI, 46.9%-74.4%), respectively.

The study treatment elicited a systemic objective response rate (ORR) of 88.9% (95% CI, 78.4%-95.4%) and an intracranial ORR of 82.5% (95% CI, 70.9%-90.9%) across the full analysis set, with data showing a systemic and intracranial disease control rate (DCR) of 100% (95% CI, 94.3%-100.0%). In the central nervous system (CNS) evaluable-for-response group, the systemic and intracranial ORR was 87.8% (95% CI, 75.2%-95.4%) and 85.7% (95% CI, 72.8%-94.1%), respectively.

The median duration of systemic response was 21.4 months (95% CI, 12.9-26.2) across the full analysis set and 20.7 months (95% CI, 9.4-25.9) in the CNS evaluable-for-response group. Additionally, data showed a median duration of intracranial response of 26.2 months (95% CI, 16.8-NR) based on RECIST v1.1 guidelines and 30.6 months (95% CI, 18.7-NR) per Response Assessment in Neuro-Oncology-Brain Metastases (RANO-BM) criteria in the CNS evaluable-for-response group.

“The results of this nonrandomized clinical trial suggest the potential of high-dose aumolertinib as first-line treatment for this population,” lead study author Hui Li, MD, from the Department of Thoracic Medical Oncology at Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, wrote with coauthors.¹ “The findings of this nonrandomized clinical trial suggest that high-dose aumolertinib treatment is associated with long survival benefit in patients with untreated EGFR-variant metastatic NSCLC and brain metastases, with a manageable safety profile. It deserves further validation in a randomized clinical trial.”

In the multi-center, single-arm, nonrandomized phase 2 ACHIEVE trial, a total of 63 patients were assigned to receive treatment with aumolertinib at 165 mg orally once daily in 28-day cycles until progressive disease, unacceptable toxicity, withdrawal of consent, or death. Additionally, 49 patients accounted for the CNS evaluable-for-response group, which included those with at least 1 measurable brain lesion. Investigators conducted brain MRI and CT scans at baseline and every 8 weeks thereafter.

The trial’s primary end point was the 12-month PFS rate. Secondary end points included intracranial PFS, systemic ORR, intracranial ORR, duration of systemic response, duration of intracranial response, systemic DCR, intracranial DCR, overall survival, and safety.

Patients 18 to 75 years old with histologically or cytologically confirmed advanced NSCLC with brain metastases, tumor tissue samples or blood samples showing EGFR-sensitive disease, and no prior systemic treatment were eligible for enrollment on the trial.² Other eligibility criteria included having a stable brain condition for at least 2 weeks before beginning study treatment and an ECOG performance status of 0 or 1.

The median age was 60 years (range, 47-76) in the full analysis set and 59 years (range, 47-76) in the CNS evaluable-for-response group, and most patients in each group were female (61.9% vs 67.3%). A majority of each respective group had an ECOG performance status of 1 (73.0% vs 71.4%), no prior smoking history (73.0% vs 75.5%), stage IVB disease (69.8% vs 71.4%), and extracranial metastases (66.7% vs 67.3%). A higher proportion of patients in the full analysis set had EGFR exon 19 deletion mutations (52.4%), while most in the CNS evaluable-for-response set had EGFR exon 21 L858R mutations (53.1%).

Treatment-related adverse effects (TRAEs) of any grade affected 92.1% of patients; 31.7% experienced grade 3 or higher TRAEs. The most common any-grade TRAEs included increased blood creatine phosphokinase (68.3%), increased aspartate aminotransferase (49.2%), increased alanine aminotransferase (38.1%), and increased blood lactate dehydrogenase (27.0%). Grade 3 or higher TRAEs included elevated blood creatine phosphokinase (27.0%) and increased alanine aminotransferase (3.2%).

Overall, 11.1% of patients experienced TRAEs leading to dose reductions, and 3.2% discontinued treatment due to grade 2 pneumonia. No patients died due to treatment.

References

1. Li H, Chen K, Gong L, et al. High-dose aumolertinib for untreated EGFR-variant non–small cell lung cancer with brain metastases: the ACHIEVE phase 2 nonrandomized clinical trial. JAMA Oncol. Published online June 26, 2025. doi:10.1001/jamaoncol.2025.1779
2. Almonertinib as first-line treatment in patients with EGFR mutations positive in advanced NSCLC with brain metastases (ACHIEVE). ClinicalTrials.gov. Updated April 22, 2024. Accessed July 31, 2025. https://tinyurl.com/mw9th5jx

Shortly after Hunter Lightman joined OpenAI as a researcher in 2022, he watched his colleagues launch ChatGPT, one of the fastest-growing products ever. Meanwhile, Lightman quietly worked on a team teaching OpenAI’s models to solve high school math competitions.

Today that team, known as MathGen, is considered instrumental to OpenAI’s industry-leading effort to create AI reasoning models: the core technology behind AI agents that can do tasks on a computer like a human would.

“We were trying to make the models better at mathematical reasoning, which at the time they weren’t very good at,” Lightman told TechCrunch, describing MathGen’s early work.

OpenAI’s models are far from perfect today — the company’s latest AI systems still hallucinate and its agents struggle with complex tasks.

But its state-of-the-art models have improved significantly on mathematical reasoning. One of OpenAI’s models recently won a gold medal at the International Math Olympiad, a math competition for the world’s brightest high school students. OpenAI believes these reasoning capabilities will translate to other subjects, and ultimately power general-purpose agents that the company has always dreamed of building.

ChatGPT was a happy accident — a lowkey research preview turned viral consumer business — but OpenAI’s agents are the product of a years-long, deliberate effort within the company.

“Eventually, you’ll just ask the computer for what you need and it’ll do all of these tasks for you,” said OpenAI CEO Sam Altman at the company’s first developer conference in 2023. “These capabilities are often talked about in the AI field as agents. The upsides of this are going to be tremendous.”

Whether agents will meet Altman’s vision remains to be seen, but OpenAI shocked the world with the release of its first AI reasoning model, o1, in the fall of 2024. Less than a year later, the 21 foundational researchers behind that breakthrough are the most highly sought-after talent in Silicon Valley.

Mark Zuckerberg recruited five of the o1 researchers to work on Meta’s new superintelligence-focused unit, offering some compensation packages north of $100 million. One of them, Shengjia Zhao, was recently named chief scientist of Meta Superintelligence Labs.

The rise of OpenAI’s reasoning models and agents are tied to a machine learning training technique known as reinforcement learning (RL). RL provides feedback to an AI model on whether its choices were correct or not in simulated environments.

RL has been used for decades. For instance, in 2016, about a year after OpenAI was founded in 2015, an AI system created by Google DeepMind using RL, AlphaGo, gained global attention after beating a world champion in the board game, Go.

Around that time, one of OpenAI’s first employees, Andrej Karpathy, began pondering how to leverage RL to create an AI agent that could use a computer. But it would take years for OpenAI to develop the necessary models and training techniques.

By 2018, OpenAI pioneered its first large language model in the GPT series, pretrained on massive amounts of internet data and a large clusters of GPUs. GPT models excelled at text processing, eventually leading to ChatGPT, but struggled with basic math.

It took until 2023 for OpenAI to achieve a breakthrough, initially dubbed “Q*” and then “Strawberry,” by combining LLMs, RL, and a technique called test-time computation. The latter gave the models extra time and computing power to plan and work through problems, verifying its steps, before providing an answer.

This allowed OpenAI to introduce a new approach called “chain-of-thought” (CoT), which improved AI’s performance on math questions the models hadn’t seen before.

“I could see the model starting to reason,” said El Kishky. “It would notice mistakes and backtrack, it would get frustrated. It really felt like reading the thoughts of a person.”

Though individually these techniques weren’t novel, OpenAI uniquely combined them to create Strawberry, which directly led to the development of o1. OpenAI quickly identified that the planning and fact checking abilities of AI reasoning models could be useful to power AI agents.

“We had solved a problem that I had been banging my head against for a couple of years,” said Lightman. “It was one of the most exciting moments of my research career.”

With AI reasoning models, OpenAI determined it had two new axes that would allow it to improve AI models: using more computational power during the post-training of AI models, and giving AI models more time and processing power while answering a question.

“OpenAI, as a company, thinks a lot about not just the way things are, but the way things are going to scale,” said Lightman.

Shortly after the 2023 Strawberry breakthrough, OpenAI spun up an “Agents” team led by OpenAI researcher Daniel Selsam to make further progress on this new paradigm, two sources told TechCrunch. Although the team was called “Agents,”  OpenAI didn’t initially differentiate between reasoning models and agents as we think of them today. The company just wanted to make AI systems capable of completing complex tasks.

Eventually, the work of Selsam’s Agents team became part of a larger project to develop the o1 reasoning model, with leaders including OpenAI co-founder Ilya Sutskever, chief research officer Mark Chen, and chief scientist Jakub Pachocki.

OpenAI would have to divert precious resources — mainly talent and GPUs — to create o1. Throughout OpenAI’s history, researchers have had to negotiate with company leaders to obtain resources; demonstrating breakthroughs was a surefire way to secure them.

“One of the core components of OpenAI is that everything in research is bottom up,” said Lightman. “When we showed the evidence [for o1], the company was like, ‘This makes sense, let’s push on it.’”

Some former employees say that the startup’s mission to develop AGI was the key factor in achieving breakthroughs around AI reasoning models. By focusing on developing the smartest-possible AI models, rather than products, OpenAI was able to prioritize o1 above other efforts. That type of large investment in ideas wasn’t always possible at competing AI labs.

The decision to try new training methods proved prescient. By late 2024, several leading AI labs started seeing diminishing returns on models created through traditional pretraining scaling. Today, much of the AI field’s momentum comes from advances in reasoning models.

In many ways, the goal of AI research is to recreate human intelligence with computers. Since the launch of o1, ChatGPT’s UX has been filled with more human-sounding features such as “thinking” and “reasoning.”

When asked whether OpenAI’s models were truly reasoning, El Kishky hedged, saying he thinks about the concept in terms of computer science.

“We’re teaching the model how to efficiently expend compute to get an answer. So if you define it that way, yes, it is reasoning,” said El Kishky.

Lightman takes the approach of focusing on the model’s results and not as much on the means or their relation to human brains.

“If the model is doing hard things, then it is doing whatever necessary approximation of reasoning it needs in order to do that,” said Lightman. “We can call it reasoning, because it looks like these reasoning traces, but it’s all just a proxy for trying to make AI tools that are really powerful and useful to a lot of people.”

OpenAI’s researchers note people may disagree with their nomenclature or definitions of reasoning — and surely, critics have emerged — but they argue it’s less important than the capabilities of their models. Other AI researchers tend to agree.

Nathan Lambert, an AI researcher with the non-profit AI2, compares AI reasoning modes to airplanes in a blog post. Both, he says, are manmade systems inspired by nature — human reasoning and bird flight, respectively — but they operate through entirely different mechanisms. That doesn’t make them any less useful, or any less capable of achieving similar outcomes.

A group of AI researchers from OpenAI, Anthropic, and Google DeepMind agreed in a recent position paper that AI reasoning models are not well understood today, and more research is needed. It may be too early to confidently claim what exactly is going on inside them.

The AI agents on the market today work best for well-defined, verifiable domains such as coding. OpenAI’s Codex agent aims to help software engineers offload simple coding tasks. Meanwhile, Anthropic’s models have become particularly popular in AI coding tools like Cursor and Claude Code — these are some of the first AI agents that people are willing to pay up for.

However, general purpose AI agents like OpenAI’s ChatGPT Agent and Perplexity’s Comet struggle with many of the complex, subjective tasks people want to automate. When trying to use these tools for online shopping or finding a long-term parking spot, I’ve found the agents take longer than I’d like and make silly mistakes.

Agents are, of course, early systems that will undoubtedly improve. But researchers must first figure out how to better train the underlying models to complete tasks that are more subjective.

“Like many problems in machine learning, it’s a data problem,” said Lightman, when asked about the limitations of agents on subjective tasks. “Some of the research I’m really excited about right now is figuring out how to train on less verifiable tasks. We have some leads on how to do these things.”

Noam Brown, an OpenAI researcher who helped create the IMO model and o1, told TechCrunch that OpenAI has new general-purpose RL techniques which allow them to teach AI models skills that aren’t easily verified. This was how the company built the model which achieved a gold medal at IMO, he said.

OpenAI’s IMO model was a newer AI system that spawns multiple agents, which then simultaneously explore several ideas, and then choose the best possible answer. These types of AI models are becoming more popular; Google and xAI have recently released state-of-the-art models using this technique.

“I think these models will become more capable at math, and I think they’ll get more capable in other reasoning areas as well,” said Brown. “The progress has been incredibly fast. I don’t see any reason to think it will slow down.”

These techniques may help OpenAI’s models become more performant, gains that could show up in the company’s upcoming GPT-5 model. OpenAI hopes to assert its dominance over competitors with the launch of GPT-5, ideally offering the best AI model to power agents for developers and consumers.

But the company also wants to make its products simpler to use. El Kishky says OpenAI wants to develop AI agents that intuitively understand what users want, without requiring them to select specific settings. He says OpenAI aims to build AI systems that understand when to call up certain tools, and how long to reason for.

These ideas paint a picture of an ultimate version of ChatGPT: an agent that can do anything on the internet for you, and understand how you want it to be done. That’s a much different product than what ChatGPT is today, but the company’s research is squarely headed in this direction.

While OpenAI undoubtedly led the AI industry a few years ago, the company now faces a tranche of worthy opponents. The question is no longer just whether OpenAI can deliver its agentic future, but can the company do so before Google, Anthropic, xAI, or Meta beat them to it?

New Delhi, Aug 3 (PTI) Commodity prices witnessed sharp swings last week, with base metal copper dropping nearly 2 per cent while gold gained over 1 per cent after US President Donald Trump imposed a 25 per cent tariff on most Indian goods.
    Fresh tariff announcements by Trump fanned fears of a global trade war, leading to a rout in base metals while safe-haven demand lifted precious metal gold last week, analysts said.
    They said the unexpected US tariff structure, especially on semi-finished copper goods, sent shockwaves across global commodity markets, with copper prices tumbling sharply and crude oil showing mixed trends amid geopolitical concerns.

    –Copper crashes as US slaps tariffs on semi-finished imports–
    Copper was the worst-hit among base metals as the Trump administration imposed a steep 50 per cent tariff on imports of semi-finished copper goods such as wires, tubes and rods. The new duties will come into force from August 7.
     However, refined copper, ores, and cathodes were excluded from the levy, creating uncertainty in global supply chains.
     On MCX, the August contract of copper fell by Rs 16.35 or 1.82 per cent last week. On July 31, copper prices dropped by Rs 36 or 4 per cent to hit an all-time low of Rs 861.70 per kg.
    On COMEX, copper prices initially fell nearly 22 per cent from peak levels before rebounding by 1.79 per cent to close at USD 4.45 per pound. LME Copper futures ended the week up 0.31 per cent at USD 9,639.60 per tonne.
     “President Trump’s tariff announcements have sent shockwaves across global commodity markets, particularly metals. The US imposed a 50 per cent tariff on semi-finished copper products, a 25 per cent levy on Indian imports, and additional trade penalties related to Russian energy transactions,” Riya Singh – Research Analyst, Commodities and Currency, Emkay Global Financial Services, said.
    “MCX copper prices dropped from Rs 900 to Rs 861 in just three sessions before stabilising. The exclusion of raw forms like cathodes from the tariff list has led to confusion in price discovery,” Singh added.
     She noted that traders exited long positions aggressively, leading to the largest weekly outflow in over a year and adding that “India imported over USD 1.4 billion worth of refined and semi-refined copper in FY24. With the US market restricted, these goods may be diverted to India, risking margin pressure for local fabricators”.
     According to Heena Naik, Research Analyst – Commodities, Angel One, the US administration initially hinted at wide-ranging copper tariffs, causing a rush of shipments into the US ahead of the August 1 deadline.
     “Now, with refined copper excluded from the tariff list, there are concerns of re-exports and a potential oversupply. The sudden narrowing of the tariff scope has disrupted the global copper supply chain,” she said.
     Naik also highlighted China’s indirect exposure, being the world’s top producer of copper products, and added that base metals broadly fell over 1.5 per cent last week amid weak demand and tariff headwinds.
    
    — Gold and silver trade mixed —
    On the Multi-Commodity Exchange (MCX), gold futures for October delivery rose Rs 1,292 or 1.3 per cent last week. In global markets, COMEX gold futures surged USD 51 or 1.52 per cent to settle at USD 3,413.80 per ounce in New York on Saturday.
     Silver, on the other hand, extended losses. MCX silver futures for September delivery plunged Rs 2,829 or 2.5 per cent to end the week lower.
     COMEX silver futures managed marginal gains of 0.59 per cent to close at USD 37.08 per ounce.
     “Gold continues to be viewed as a reliable store of value, especially with the US Fed maintaining a restrictive policy stance and global uncertainties flaring,” said Riya Singh, Research Analyst – Commodities and Currency at Emkay Global Financial Services.
     She noted that gold has gained nearly 25 per cent year-to-date, peaking above USD 3,500 per ounce in April on the back of Middle East tensions and inflation worries.
     “Silver, however, faced a dual impact of industrial weakness and ETF-led support. It is under pressure due to weak Chinese PMI data, but strong ETF holdings and robust COMEX inventories offer a cushion,” Singh added.
    
    — Crude oil sees mixed cues —
     Crude oil futures posted a mixed performance, with the MCX futures for August delivery rising by Rs 100 or 1.73 per cent. Globally, Brent crude futures fell by 3.94 per cent to USD 69.67 per barrel, while WTI crude slipped 2.79 per cent to USD 67.33 per barrel.
     Riya Singh said, “Brent retreated from USD 67.74 to USD 71.26 after failing to sustain five-week highs. Demand concerns from geopolitical uncertainty and trade disruptions kept the rally in check”.
     She highlighted that India’s crude imports are particularly vulnerable, with around 35 per cent coming from Russia. Adding that any secondary sanctions on Russian oil imports could force India to more expensive alternatives, which could impact domestic refiners such as IOC and Reliance, and affect the rupee.
     Heena Naik added that crude surged by over 5 per cent as investors focused on developments on the US President’s tighter deadline for Russia to end the war in Ukraine. However, a weak industrial demand and uncertainty over OPEC+ supply decisions kept oil prices under pressure.
    
    — Commodities to see volatility this week —
     Analysts emphasised that as investors deal with the effects of US tariffs, China’s economic slowdown, and shifting geopolitical tensions, commodity markets are expected to be turbulent in the weeks ahead.
     “Price discovery has been skewed by Trump’s tariff structure, which targets semi-finished goods while excluding raw copper forms. Regarding demand and future trading channels, the market is still unclear,” Singh stated.
     Naik said that investors should prepare for ongoing fluctuations in base metals, energy, and precious metals due to policy uncertainty and the rising US dollar’s impact on global commodities.

Story Continues