The Psychological effect behind the Stock Markets’ Most Volatile Month. Generally, the volatility in October has been well-above average, and this does have a psychological effect on investors’ minds. The biggest market crashes – Black Monday/Tuesday and other turmoil had occurred in October making it the “Jinx Month”.
The sharp and sudden drop that occurred last week shows that October is living up to its reputation of being the Stock Market Most Volatile Month. It could be investors being superstitious, but so far, there are not known drivers only some theories which include: The return from summer vacations The federal government’s fiscal year which begins on the first of October The third-quarter corporate earnings. On average, more daily moves above 1% are recorded in October.
The S&P500 recorded three more than 1% daily moves already which kind of justified the belief. World Equity Indices (% Change) – Month-to-date Source: Bloomberg Terminal Besides the myth, rising yields are set to be the challenge for this quarter and appear to be the primary driver behind the recent surge in volatility. The prospect of more instability is high and quite alarming given that the US stock markets are already inflated.
The actions by the Fed have also put the stock markets in a dangerous bubble. Are the markets prone to more volatility? Alternatively, does the recent fluctuations signal a bear market?
The recent weeks of volatility are evidence that trading equity will likely remain choppy in the short-term. At this stage, it is difficult to recognise whether the bull market has reached the top and investors need to get out before the bear market or whether investors should stay away from the “buy the dip” strategy in the emerging and Asian equity markets. All in all, short-term investors might find it hard to catch the rhythm of the stock markets, but if investors were to maintain a long-term view, it might be worth listening to Warren Buffet advice: “Buy, Hold and Don’t watch too closely when the market sells off.”
By
GO Markets
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2025 has seen a material decline in the fortunes of the greenback. A technical structure breakdown early in the year was followed by a breach of the 200-day moving average (MA) at the end of Q1. The index then entered correction territory, printing a three-year low at the end of Q2.
Since then, we have seen attempts to build a technical base, including a re-test of the end-of-June lows in mid-September. However, buying pressure has not been strong enough to push price back above the technically critical and psychologically important 100 level.
What the levels suggest from here
As things stand, the index remains more than 10% lower for 2025. On this technical view, the index may revisit the 96 area. However, technical levels can fail and outcomes depend on multiple factors.
US dollar index
Source: TradingView
The key question for 2026
The key question remains: are we likely to see further losses in the early part of next year and beyond, or will current support hold?
We cannot assess the US dollar in isolation and any outlook is shaped by internal and global factors, not least its relative strength versus other major currencies. Many of these drivers are interrelated, but four potential headwinds stand out for any US dollar recovery. Collectively, they may keep downside pressure in play.
Four headwinds for any US dollar recovery
1. The US dollar as a safe-haven trade
One scenario where US dollar support has historically been evident is during major global events, slowdowns and market shocks. However, the more muted response of the US dollar during risk-off episodes this year suggests a shift away from the historical norm, with fewer sustained US dollar rallies.
Instead, throughout 2025, some investors appearedto favour gold, and at other times, FX and even equities, rather than into the US dollar. If this change in behaviour persists through 2026, it could make recovery harder, even if global economic pressure builds over the year ahead.
2. US versus global trade
Trade policy is harder to measure objectively, and outcomes can be difficult to predict. That said, trade battles driven by tariffs on US imports are often viewed as an additional potential drag on the US dollar.
The impact may be twofold if additional strain is placed on the US economy through:
a slowdown in global trade volumes as impacted countries seek alternative trade relationships, with supply chain distortions that may not favour US growth
pressure on US corporate profit margins as tariffs lift costs for importers
3. Removal of quantitative tightening
The Fed formally halted its balance sheet reduction, quantitative tightening (QT), as of 1 December 2025, ending a program that shrank assets by roughly US$2.4 trillion since mid-2022.
Traditionally, ending QT is seen as marginally negative for the US dollar because it stops the withdrawal of liquidity, can ease global funding conditions, and may reduce the scarcity that can support dollar demand. Put simply, more dollars in the system can soften the currency’s support at the margin, although outcomes have varied historically and often depend on broader financial conditions.
4. Interest rate differential
Interest rate differential (IRD) is likely to be a primary driver of US dollar strength, or otherwise, in the months ahead. The latest FOMC meeting delivered the expected 0.25% cut, with attention on guidance for what may come next.
Even after a softer-than-expected CPI print, markets have been reluctant to price aggressive near-term easing. At the time of writing, less than a 20% chance of a January cut is priced in, and it may be March before we see the next move.
The Fed is balancing sticky inflation against a jobs market under pressure, with the headline rate back at levels last seen in 2012. The practical takeaway is that a more accommodative stance may add to downward pressure on the US dollar.
Current expectations imply around two rate cuts through 2026, with the potential for further easing beyond that, broadly consistent with the median projections shown in the chart below. These are forecasts rather than guarantees, and they can shift as economic data and policy guidance evolve.
Source: US Federal Reserve, Summart of Economic Projections
The “Magnificent Seven” technology companies are expected to invest a combined $385 billion into AI by the end of 2025.
Microsoft is positioning itself as the platform leader. Nvidia dominates the underlying AI infra. Google leads in research. Meta is building open-source tech. Amazon – AI agents. Apple — on-device integration. And Tesla pioneering autonomous vehicles and robots.
The “Big 4” tech companies' AI spending alone is forecast at $364 billion.
With such enormous sums pouring into AI, is this a winner-take-all game?
Or will each of the Mag Seven be able to thrive in the AI future?
Microsoft: The AI Everywhere Strategy
Microsoft has made one of the biggest bets on AI out of the Mag Seven — adopting the philosophy that AI should be everywhere.
Through its deep partnership with OpenAI, of which it is a 49% shareholder, the company has integrated GPT-5 across its entire ecosystem.
Key initiatives:
GPT-5 integration across consumer, enterprise, and developer tools through Microsoft 365 Copilot, GitHub Copilot, and Azure AI Foundry
Azure AI Foundry for unified AI development platform with model router technology
Copilot ecosystem spanning productivity, coding, and enterprise applications with real-time model selection
$100 billion projected AI infrastructure spending for 2025
Microsoft’s centrepiece is Copilot, which can now detect whether a prompt requires advanced reasoning and route to GPT-5's deeper reasoning model.
This (theoretically) means high-quality AI outputs become invisible infrastructure rather than a skill users need to learn.
However, this all-in bet on OpenAI does come with some risks. It is putting all its eggs in OpenAI's basket, tying its future success to a single partnership.
Elon Musk warned that "OpenAI is going to eat Microsoft alive"[/caption]
Google: The Research Strategy
Google’s approach is to fund research to build the most intelligent models possible. This research-first strategy creates a pipeline from scientific discovery to commercial products — what it hopes will give it an edge in the AI race.
Key initiatives:
Over 4 million developers building with Gemini 2.5 Pro and Flash
Ironwood TPU offering 3,600 times better performance compared to Google’s first TPU
AI search overviews reaching 2 billion monthly users across Google Search
DeepMind breakthroughs: AlphaEvolve for algorithm discovery, Aeneas for ancient text interpretation, AlphaQubit for quantum error detection, and AI co-scientist systems
Google’s AI research branch, DeepMind, brings together two of the world's leading AI research labs — Google Brain and DeepMind — the former having invented the Transformer architecture that underpins almost all modern large language models.
The bet is that breakthrough research in areas like quantum computing, protein folding, and mathematical reasoning will translate into a competitive advantage for Google.
Today, we're introducing AlphaEarth Foundations from @GoogleDeepMind , an AI model that functions like a virtual satellite which helps scientists make informed decisions on critical issues like food security, deforestation, and water resources. AlphaEarth Foundations provides a… pic.twitter.com/L1rk2Z5DKk
Meta has made a somewhat contrarian bet in its approach to AI: giving away their tech for free. The company's Llama 4 models, including recently released Scout and Maverick, are the first natively multi-modal open-weight models available.
Key initiatives:
Llama 4 Scout and Maverick - first open-weight natively multi-modal models
AI Studio that enables the creation of hundreds of thousands of AI characters
$65-72 billion projected AI infrastructure spending for 2025
This open-source strategy directly challenges the closed-source big players like GPT and Claude. By making AI models freely available, Meta is essentially commoditizing what competitors are trying to monetize. Meta's bet is that if AI models become commoditized, the real value will be in the infrastructure that sits on top. Meta's social platforms and massive user base give it a natural advantage if this eventuates.
Meta's recent quarter was also "the best example to date of AI having a tangible impact on revenue and earnings growth at scale," according to tech analyst Gene Munster.
H1 relative performance of the Magnificent Seven stocks. Source: KoyFin, Finimize
However, it hasn’t been all smooth sailing for Meta. Their most anticipated release, Llama Behemoth, has all but been scrapped due to performance issues. And Meta is now rumored to be developing a closed-source Behemoth alternative, despite their open-source mantra.
Amazon: The AI Agent Strategy
Amazon’s strategy is to build the infrastructure for AI that can take actions — booking meetings, processing orders, managing workflows, and integrating with enterprise systems.
Rather than building the best AI model, Amazon has focused its efforts on becoming the platform where all AI models live.
Key initiatives:
Amazon Bedrock offering 100+ foundation models from leading AI companies, including OpenAI models.
$100 million additional investment in AWS Generative AI Innovation Center for agentic AI development
Amazon Bedrock AgentCore enabling deployment and scaling of AI agents with enterprise-grade security
$118 billion projected AI infrastructure spending for 2025
The goal is to become the “orchestrator” that lets companies mix and match the best models for different tasks.
Amazon’s AgentCore will provide the underlying memory management, identity controls, and tool integration needed for these companies to deploy AI agents safely at scale.
This approach offers flexibility, but does carry some risks. Amazon is essentially positioning itself as the middleman for AI. If AI models become commoditized or if companies prefer direct relationships with AI providers, Amazon's systems could become redundant.
Nvidia: The Infra Strategy
Nvidia is the one selling the shovels for the AI gold rush. While others in the Mag Seven battle to build the best AI models and applications, Nvidia provides the fundamental computing infrastructure that makes all their efforts possible.
This hardware-first strategy means Nvidia wins regardless of which company ultimately dominates. As AI advances and models get larger, demand for Nvidia's chips only increases.
Key initiatives:
Blackwell architecture achieving $11 billion in Q2 2025 revenue, the fastest product ramp in company history
New chip roadmap: Blackwell Ultra (H2 2025), Vera Rubin (H2 2026), Rubin Ultra (H2 2027)
Data center revenue reaching $35.6 billion in Q2, representing 91% of total company sales
Manufacturing scale-up with 350 plants producing 1.5 million components for Blackwell chips
With an announced product roadmap of Blackwell Ultra (2025), Vera Rubin (2026), and Rubin Ultra (2027), Nvidia has created a system where the AI industry must continuously upgrade to Nvidia’s newest tech to stay competitive.
This also means that Nvidia, unlike the others in the Mag Seven, has almost no direct AI spending — it is the one selling, not buying.
However, Nvidia is not indestructible. The company recently halted its H20 chip production after the Chinese government effectively blocked the chip, which was intended as a workaround to U.S. export controls.
Apple: The On-Device Strategy
Apple's AI strategy is focused on privacy, integration, and user experience. Apple Intelligence, the AI system built into iOS, uses on-device processing and Private Cloud Compute to help ensure user data is protected when using AI.
Key initiatives:
Apple Intelligence with multi-model on-device processing and Private Cloud Compute
Enhanced Siri with natural language understanding and ChatGPT integration for complex queries
Direct developer access to on-device foundation models, enabling offline AI capabilities
$10-11 billion projected AI infrastructure spending for 2025
The drawback of this on-device approach is that it requires powerful hardware from the user's end. Apple Intelligence can only run on devices with a minimum of 8GB RAM, creating a powerful upgrade cycle for Apple but excluding many existing users.
Tesla: The Robo Strategy
Tesla's AI strategy focuses on two moonshot applications: Full Self-Driving vehicles and humanoid robots.
This is the 'AI in the physical world' play. While others in the Mag Seven are focused on the digital side of AI, Tesla is building machines that use AI for physical operations.
Tesla’s Optimus robot replicating human tasks
Key initiatives:
Plans for 5,000-10,000 Optimus robots in 2025, scaling to 50,000 in 2026
Robotaxi service targeting availability to half the U.S. population by EOY 2025
AI6 chip development with Samsung for unified training across vehicles, robots, and data centers
$5 billion projected AI infrastructure spending for 2025
This play is exponentially harder to develop than digital AI, and the markets have reflected low confidence that Tesla can pull it off.
TSLA has been the worst-performing Mag Seven stock of 2025, down 18.37% in H1 2025.
However, if Tesla’s strategy is successful, it could be far more valuable than other AI plays. Robots and autonomous vehicles could perform actual labour worth trillions of dollars annually.
The $385 billion Question
The Mag Seven are starting to see real revenue come in from their AI investments. But they're pouring that money (and more) back into AI, betting that the boom is just getting started.
The platform players like Microsoft and Amazon are betting on becoming essential infrastructure. Nvidia’s play is to sell the underlying hardware to everyone. Google and Meta compete on capability and access. While Apple and Tesla target specific use cases.
The $385 billion question is which of the Magnificent Seven has bet the right way? Or will a new player rise and usurp the long-standing tech giants altogether?
You can access all Magnificent Seven stocks and thousands of other Share CFDs on GO Markets.
Over the past 3 months Nvidia has moved through ranges that some stocks don’t do in years, in some cases decades. Having lost over 35 per cent in the June to August sell off, it quickly bounced over 40 per cent in the preceding 20 days once it hit its August low as we build positions ahead of its results. These results delivered Nvidia style numbers with three figure growth on the sales, net profit and earnings lines but this did not appease the market, seeing it fall 22 per cent in a little over 8 days.
Which brings us to now – a new 16 per cent drive as Nivida reports it’s struggling to meet demands and that the AI revolution is translating faster than even it expected. This got us thinking – Where are we right “Now” in the AU players? Thus, it’s time to dive into the drivers for the Nvidia and Co.
AI players. Supersonic As mentioned, Nvidia’s results have been astonishing – and it still has time to do a US$50 billion buyback. It collected the award for becoming the world’s largest company in the shortest timeframe in the post-WWII era, think about that for one second – that’s faster than Amazon, Microsoft, Apple, Google, Shell, BP, ExxonMobil, TV players of the 60s and 70s.
So the question is how does it keep its speed and trajectory? Well that comes from what some are calling the ‘supersonic’ scalers. These are the players like Google, Amazon, Meta and Microsoft that are the users and providers of the AI revolution.
These are the players that have spent hundreds billions thus far on the third digital revolution. Let us once again put that into perspective, the amount of spending is (inflation adjusted) the same as what was spent during the 1960’s on mainframe computing and the 1990’s distribution of fibre-optics. So we have now seen that level of spending in AI the next step is ‘usage’ and that is the inflection point we find ourselves at.
Currently AI is mainly used to train foundational models and chatbots – which is fine but not long-term financially stable. It needs to move into things like productions – that is producing models for corporate clients that forecast, streamline and increase productivity. This is the ‘Grail’ This immediately raises the bigger question for now – can this Grail be achieved?
The Voices To answer that – let us present some arguments from some of AI’s largest “Voices” On the AI potential and the possibility of a profound and rapid technological revolution, Sam Altman, CEO of OpenAI, has claimed that AI represents the "biggest, best, and most important of all technology revolutions," and predicts that AI will become increasingly integrated into all aspects of life. This reflects a belief in AI's far-reaching influence over time. The never subtle McKinsey and Co. has projected that generative AI could eventually contribute up to $8 trillion to the global economy annually.
This figure underscores the massive economic potential of AI. The huge caveat: McKinsey's predictions are never real-world tested and inevitably fall flat in the market. This kind of money is what makes AI so attractive to players in Venture Capital.
For the VC watchers out there the one that is catching everyone’s attention is VC accelerator Y Combinator which is fully embracing the technology. Just to put Y Combinator into context, according to Jared Heyman’s Rebel Fund, if anyone had invested in every Y Combinator deal since 2005 (which would have been impossible just to let you know), the average annual return would have been 176%, even after accounting for dilution. Furthermore to the VC story - AI has accounted for over 40 per cent of new unicorns (startups valued at $1 billion or more) in the first half of 2024, and 60 per cent of the increase in VC-backed valuations.
So far in 2024, U.S. unicorn valuations have grown by $162 billion, largely driven by AI’s rapid expansion, according to Pitchbook data. So the Voices certainly believe it can be achieved. But is this a good thing?
The Good, the Bad and the Ugly AI is advancing at such a rapid pace that existing performance benchmarks, such as reading comprehension, image classification and advanced maths, are becoming outdated, necessitating the creation of new standards. This reflects the fast-moving nature of AI progress. For example, look at the success of AlphaFold, an AI-driven algorithm that accurately predicts protein structures.
Some see this as one of the most important achievements in AI’s short history and underscores AI’s transformative impact on science, particularly in fields like biology and healthcare. This is the Good. Then there is the 165-page paper titled "Situational Awareness" by Aschenbrenner which has predicted that by 2030, AI will achieve superintelligence and create a $1 trillion industry.
Also, a positive, but will consume 20 per cent of the U.S. power supply. These incredible predictions emphasise the enormous scale of AI and the impact it will have on industry, infrastructure and people. The latest Google study found that generative AI could significantly improve workforce productivity.
The study suggests that roughly 80 per cent of jobs could see at least 10 per cent of tasks completed twice as fast due to AI, which has implications for industries such as call centres, coding, and professional writing. This highlights AI's capacity to streamline tasks and enhance efficiency across various fields. However it also raises the massive concern around job security, job satisfaction and the socio-economic divide as the majority of those affected by AI ‘productivity’ are in mid to low scales.
Then we come to Elon Musk’s new AI startup, xAI, which raised $6 billion at a valuation of $24 billion this year. The company is planning to build the world’s largest supercomputer in Tennessee to support AI training and inference. This all sounds economically and financially exciting but it has a darker side.
These are the kinds of AI ventures that have seen ‘deep-fake’ creations. For example Musk himself shared a deep-fake video of Vice President Kamala Harris. This is the ugly side of AI and reflects the broader cultural and ethical issues surrounding AI-generated content.
Furthermore – we should always be forecasting both the good and the bad for investment opportunities. These issues are already attracting regulations and compliance responses. How impactful will these be?
And will it halt the AI driven share price appreciation? It is a very real and present issue. Where does this leave us?
The share price future of Nvidia and Co is clearly dependent on the longer-term achievement of the AI revolution. As shown, the supersonic players in technology and venture capital are betting big on AI, with predictions that it will reshape the global economy, industries, and even basic societal structures. However, there is still uncertainty about the exact timeline for these changes and how accurately the market is pricing in AI's potential.
The AI ecosystem is moving at breakneck speed, with new developments outpacing benchmarks and productivity gains reshaping jobs, but whether all these projections that range from trillion-dollar economies to superintelligence materialises remains to be seen. Thus – for now – Nvidia and Co’s recent roller-coaster trading looks set to continue.
Three data levers dominate the US markets in February: growth, labour and inflation. Beyond those, policy communication, trade headlines and geopolitics can still matter, even when they are not tied to a scheduled release date.
Growth: business activity and trade
Early to mid-month indicators provide a read on whether US momentum is stabilising or softening into Q1.
Key dates
Advance monthly retail sales: 10 Feb, 8:30 am (ET) / 11 Feb, 12:30 am (AEDT)
Industrial Production and Capacity Utilisation: 18 Feb, 9:15 am (ET) / 19 Feb, 1:15 am (AEDT)
International Trade in Goods and Services: 19 Feb, 8:30 am (ET) / 20 Feb, 12:30 am (AEDT)
What markets look for
Markets will be watching new orders and output trends in PMIs to gauge underlying demand momentum. Export and import data will offer insights into global trade flows and domestic consumption patterns. Traders will also assess whether manufacturing and services sectors remain in expansionary territory or show signs of contraction.
Market sensitivities
Stronger growth can be associated with higher yields and a firmer USD, though inflation and policy expectations often dominate the rate response.
Softer activity can be associated with lower yields and improved risk appetite, depending on inflation, positioning, and broader risk conditions.
Labour conditions remain a direct input into rate expectations. The monthly NFP report, alongside the weekly jobless claims released every Thursday, is typically watched for signs of cooling or renewed tightness.
Key dates
Employment Situation (nonfarm payrolls, unemployment, wages): 6 Feb, 8:30 am (ET) / 7 Feb, 12:30 am (AEDT)
What markets look for
Markets will focus on headline payrolls to assess the pace of job creation, the unemployment rate for signals of labour market slack, and average hourly earnings as a gauge of wage pressures. A gradual cooling can support the idea that wage pressures are easing. Persistent tightness may push out expectations for policy easing.
Market sensitivities
Payroll surprises frequently move Treasury yields and the USD quickly, with knock-on effects for equities and commodities.
Inflation releases remain a key input into expectations for the Fed’s policy path.
Key dates
Consumer Price Index (CPI): 11 Feb, 8:30 am (ET) / 12 Feb, 12:30 am (AEDT)
Personal Income and Outlays, including the PCE price index): 20 Feb, 8:30 am (ET) / 21 Feb, 12:30 am (AEDT)
Producer Price Index (PPI): 27 Feb, 8:30 am (ET) / 28 Feb, 12:30 am (AEDT)
What markets look for
Producer prices can act as a pipeline signal. CPI and the PCE price index can help confirm whether inflation pressures are broadening or fading at the consumer level.
How rates and the USD can react
Cooling inflation can support lower yields and a softer USD, though market reactions can vary.
Sticky inflation can keep upward pressure on yields and financial conditions, especially if it shifts policy expectations.
There is no scheduled February FOMC meeting, but speeches and other Fed communication, as well as the minutes cycle from prior meetings, can still influence expectations around the policy path. Without a decision event, markets often react to shifts in tone, or renewed emphasis on inflation persistence and labour conditions.
Trade and geopolitics
Trade flows and energy markets can remain secondary, and the risk profile is typically headline-driven rather than linked to scheduled releases.
The Office of the United States Trade Representative has published fact sheets and policy updates (including on US-India trade engagement) that may occasionally influence sector and supply-chain sentiment at the margin, depending on the substance and market focus at the time.
Separately, volatility tied to Middle East developments and any impact on energy pricing can filter into inflation expectations and bond yields. Weekly petroleum market data from the US Energy Information Administration is one input that markets often monitor for near-term signals.
Every four years, the Olympics does something markets understand very well: it concentrates attention. And when attention concentrates, so do headlines, narratives, positioning… and sometimes, price.
The Olympics isn’t just “two weeks of sport.” For traders, it’s a two-week global marketing and tourism event, delivered in real time, often while Australia is asleep.
So, let’s make this useful.
Scheduled dates: Friday 6 February to Sunday 22 February 2026 Where: Milan, Cortina d’Ampezzo, and alpine venues across northern Italy
What matters (and what doesn’t)
Matters
Money moving early: Infrastructure, transport upgrades, sponsorship, media rights and tourism booking trends.
Narrative amid liquidity: Themed trades can run harder than fundamentals, especially when volume shows up but can also reverse quickly.
Earnings language: Traders often watch whether companies start referencing demand, bookings, ad spend, or guidance tailwinds.
Doesn’t
Medal counts (controversial statement, I know).
Why the Olympics matter to markets
The Olympics are not just two weeks of sport. For host regions, they often reflect years of planning, investment and marketing and then all of that gets shoved into one concentrated global media moment. That’s why markets pay attention, even when the fundamentals haven’t suddenly reinvented themselves.
Here are a few themes host regions may see. Outcomes vary by host, timing, and the macro backdrop.
Theme map: where headlines usually cluster
Construction and materials Logistics upgrades, transport links, and “sustainable” builds.
Luxury and tourism Milan’s fashion-capital status starts turning into demand well before opening night.
Media and streaming Advertising increases as audiences surge and platforms cash in.
Transport and travel Airlines, hotels and travel tech riding the volume, and the expectations.
For Australian-based traders, the key idea is exposure, not geography. Italian listings aren’t required to see the theme while simultaneously, some people look for ASX-listed companies whose earnings may be linked to similar forces (travel demand, discretionary spend). The connection is not guaranteed. It depends on the business, the numbers and the valuation.
The ASX shortlist
The ASX shortlist is simply a way to organise the local market by exposure, so you can see which parts of the index are most likely to pick up the spillover. It is not a forecast and it is not a recommendation, it is a framework for tracking how a narrative moves from headlines into sector pricing, and for separating genuine theme exposure from names that are only catching the noise.
Wesfarmers (WES): broad retail exposure that gives a read on the local consumer.
Flight Centre (FLT): may offer higher exposure to travel cycles across retail and corporate.
Corporate Travel Management (CTD): business travel sensitivity, and it often reacts to conference and event demands.
The Aussie toolkit
The Olympics compresses attention, and when attention compresses, a handful of instruments tend to register it first while everything else just picks up noise. The whole point here is monitoring and discipline, not variety.
FX: the fastest headline absorber
Examples: EUR/USD, EUR/AUD, with AUD/JPY often watched as broader risk-sentiment signals. What it captures: how markets are pricing European optimism, global risk appetite, and where capital is leaning in real time
Index benchmarks: the sentiment dashboard
Examples (index level): Euro Stoxx 50, DAX, FTSE, S&P 500. What it can capture: whether a headline is broad enough to influence wider positioning, or whether it stays contained to a narrow theme.
Commodities: second order, often the amplifier
Examples: copper (industrial sensitivity), Brent/WTI (energy and geopolitics), gold (risk/uncertainty). What it can capture: the bigger drivers (USD, rates, growth expectations, weather and geopolitics) with the Olympics usually acting as the wrapper rather than the engine.
Put together, this is not a prediction, and it is not a shopping list. It is a compact map of where the Olympics story is most likely to show itself first, where it might spread next, and where it sometimes shows up late, after everyone has already decided how they feel about it.
Your calendar is not Europe’s calendar
For Aussie traders, the Olympics is a two-week, overnight headline cycle. Much of the “live” information flow is likely to land during the European and US sessions. However, there are three windows to keep in mind.
Watch this space.
In the next piece, we’ll build the Euro checklist and map the volatility windows around Milano–Cortina so you can see when the market is actually pricing the story, and when it is just reacting to noise.
For over 110 years, the Federal Reserve (the Fed) has operated at a deliberate distance from the White House and Congress.
It is the only federal agency that doesn’t report to any single branch of government in the way most agencies do, and can implement policy without waiting for political approval.
These policies include interest rate decisions, adjusting the money supply, emergency lending to banks, capital reserve requirements for banks, and determining which financial institutions require heightened oversight.
The Fed can act independently on all these critical economic decisions and more.
But why does the US government enable this? And why is it that nearly every major economy has adopted a similar model for their central bank?
The foundation of Fed independence: the panic of 1907
The Fed was established in 1913 following the Panic of 1907, a major financial crisis. It saw major banks collapse, the stock market drop nearly 50%, and credit markets freeze across the country.
At the time, the US had no central authority to inject liquidity into the banking system during emergencies or to prevent cascading bank failures from toppling the entire economy.
J.P. Morgan personally orchestrated a bailout using his own fortune, highlighting just how fragile the US financial system had become.
The debate that followed revealed that while the US clearly needed a central bank, politicians were objectively seen as poorly positioned to run it.
Previous attempts at central banking had failed partly due to political interference. Presidents and Congress had used monetary policy to serve short-term political goals rather than long-term economic stability.
So it was decided that a stand-alone body responsible for making all major economic decisions would be created. Essentially, the Fed was created because politicians, who face elections and public pressure, couldn’t be relied upon to make unpopular decisions when needed for the long-term economy.
Although the Fed is designed to be an autonomous body, separate from political influence, it still has accountability to the US government (and thereby US voters).
The President is responsible for appointing the Fed Chair and the seven Governors of the Federal Reserve Board, subject to confirmation by the Senate.
Each Governor serves a 14-year term, and the Chair serves a four-year term. The Governors' terms are staggered to prevent any single administration from being able to change the entire board overnight.
Beyond this “main” board, there are twelve regional Federal Reserve Banks that operate across the country. Their presidents are appointed by private-sector boards and approved by the Fed's seven Governors. Five of these presidents vote on interest rates at any given time, alongside the seven Governors.
This creates a decentralised structure where no single person or political party can dictate monetary policy. Changing the Fed's direction requires consensus across multiple appointees from different administrations.
The case for Fed independence: Nixon, Burns, and the inflation hangover
The strongest argument for keeping the Fed independent comes from Nixon’s time as president in the 1970s.
Nixon pressured Fed Chair Arthur Burns to keep interest rates low in the lead-up to the 1972 election. Burns complied, and Nixon won in a landslide. Over the next decade, unemployment and inflation both rose simultaneously (commonly referred to now as “stagflation”).
By the late 1970s, inflation exceeded 13 per cent, Nixon was out of office, and it was time to appoint a new Fed chair.
That new Fed chair was Paul Volcker. And despite public and political pressure to bring down interest rates and reduce unemployment, he pushed the rate up to more than 19 per cent to try to break inflation.
The decision triggered a brutal recession, with unemployment hitting nearly 11 per cent.
But by the mid-1980s, inflation had dropped back into the low single digits.
Pre-Volcker era inflation vs Volcker era inflation | FRED
Volcker stood firm where non-independent politicians would have backflipped in the face of plummeting poll numbers.
The “Volcker era” is now taught as a masterclass in why central banks need independence. The painful medicine worked because the Fed could withstand political backlash that would have broken a less autonomous institution.
Are other central banks independent?
Nearly every major developed economy has an independent central bank. The European Central Bank, Bank of Japan, Bank of England, Bank of Canada, and Reserve Bank of Australia all operate with similar autonomy from their governments as the Fed.
However, there are examples of developed nations that have moved away from independent central banks.
In Turkey, the president forced its central bank to maintain low rates even as inflation soared past 85 per cent. The decision served short-term political goals while devastating the purchasing power of everyday people.
Argentina's recurring economic crises have been exacerbated by monetary policy subordinated to political needs. Venezuela's hyperinflation accelerated after the government asserted greater control over its central bank.
The pattern tends to show that the more control the government has over monetary policy, the more the economy leans toward instability and higher inflation.
Independent central banks may not be perfect, but they have historically outperformed the alternative.
Turkey’s interest rates dropped in 2022 despite inflation skyrocketing
Why do markets care about Fed independence?
Markets generally prefer predictability, and independent central banks make more predictable decisions.
Fed officials often outline how they plan to adjust policy and what their preferred data points are.
Currently, the Consumer Price Index (CPI), Personal Consumption Expenditures (PCE) index, Bureau of Labor Statistics (BLS) monthly jobs reports, and quarterly GDP releases form expectations about the future path of interest rates.
This transparency and predictability help businesses map out investments, banks to set lending rates, and everyday people to plan major financial decisions.
When political influence infiltrates these decisions, it introduces uncertainty. Instead of following predictable patterns based on publicly released data, interest rates can shift based on electoral considerations or political preference, which makes long-term planning more difficult.
The markets react to this uncertainty through stock price volatility, potential bond yield rises, and fluctuating currency values.
The enduring logic
The independence of the Federal Reserve is about recognising that stable money and sustainable growth require institutions capable of making unpopular decisions when economic fundamentals demand them.
Elections will always create pressure for easier monetary conditions. Inflation will always tempt policymakers to delay painful adjustments. And the political calendar will never align perfectly with economic cycles.
Fed independence exists to navigate these eternal tensions, not perfectly, but better than political control has managed throughout history.
That's why this principle, forged in financial panics and refined through successive crises, remains central to how modern economies function. And it's why debates about central bank independence, whenever they arise, touch something fundamental about how democracies can maintain long-term prosperity.
