NEWS

14 Nov 2025 - Hedge Clippings |14 November 2025
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Hedge Clippings | 14 November 2025 Any lingering chances of a rate cut before Christmas - already unlikely given the uptick in the September quarter CPI to 3.2% - went out of the window yesterday when the October Labour Force figures were released, showing unemployment had dropped to 4.3% after rising 4.5% in September. The number of unemployed people dropped by 17,000, while the ranks of the employed increased by 42,000. If that's the case, there's no RBA meeting in January, so speculation will have to wait until February, but mid-2026 or even beyond is looking more likely. At the same time, the chances of a rate cut in the US - widely expected just a few weeks ago - also dropped to around 50/50 as a result of conflicting economic signals, thanks in part to the lack of data as a result of the US Government shutdown, a weakening labour market, and concerns about sticky inflation. There's time for the odds to move either way before the next FOMC meeting due on the 9th-10th of December. Meanwhile, among signs of stretched valuations and increasing equity market volatility - not only in the tech sector, but also amongst some local high-flying small-cap stocks we have taken a look at the performance of managed funds in the Australian Small to Mid-cap Peer Group. Not surprisingly, given the performance of the respective ASX200 and ASX Small Ordinaries indices, small caps outperformed large cap funds. The average return of all 97 funds in the small cap universe, broadly in line with the index over 1 - 5 years, will give encouragement to those who advocate passive investing via ETF's with their accompanying low fees. By filtering the list using AFM's Star Rankings, and only selecting funds with 4 or 5 Stars over 3 & 5 years, we created a portfolio of Top 10 funds which significantly outperformed the index. Using the same process to look at those funds with only 1 or 2 Stars, to come up with the bottom 10 funds, the result was equally predictable. Obviously, as in stock selection, manager selection is essential. Given that past performance cannot be guaranteed, how does one look at historical fund performance? We would suggest ignoring (or at least not jumping at) one-year performance. It can be misleading unless 3, 5 or 7 years is equally good. Taking 5 years covers a sufficient period, and then ensuring that the shorter-term performance is consistent. AFM's Star Ranking enables filtering on both performance and risk/volatility, and while not in-depth, it provides an initial, and significantly effective, and fast process to sort the wheat from the chaff and then allow concentrated analysis of the resulting funds. Of significant interest was the analysis of the funds at the bottom of the list based on recommended research ratings. In spite of these having a track record of being in the 3rd or 4th quartile performance over 1, 3, and 5 years, most of them came with "recommended" and in a couple of cases "highly recommended" research ratings from Zenith, Lonsec, Morningstar, or SQM. In fact, only one of them (yes, 1 out of 10) didn't have a rating. Which leads us to the question: What value can you put on (paid) research? News | Insights New Funds on FundMonitors.com Research house scrutiny needed | Fundmonitors.com Trip Insights: The US | 4D Infrastructure October 2025 Performance News Bennelong Emerging Companies Fund Quay Global Real Estate Fund (Unhedged) Bennelong Twenty20 Australian Equities Fund |
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14 Nov 2025 - Performance Report: ECCM Systematic Trend Fund
[Current Manager Report if available]

14 Nov 2025 - Performance Report: Cyan C3G Fund
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14 Nov 2025 - What Really Causes a Market Crash
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What Really Causes a Market Crash Marcus Today October 2025 4-minute read
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What could tip the market over? Well, let me ask you this question - what caused the 1987 crash? Don't know? No, you don't know because there wasn't one specific reason for the 1987 crash. When the dam cracks, you don't go looking at the first drop that came through the first crack and analyse it to find out why it cracked. Because the reason the dam cracked is that, in the year and a half prior to the 1987 crash, the Australian market went up over 100%. What cracked the dam and caused the crash was a build-up of pressure over a long period of time, which eventually broke. Analysing the first drop - why it happened - was irrelevant. We were sitting in Buckmaster & Moore in the UK, and we had one guy on the desk who was a young bloke. He had a client in the US, and doing a few tickets in those days was meaningful because the commissions were about 1.6%, and they went up if the order was larger. Anyway, this young guy started ripping off red tickets - red ticket, red ticket - writing out another one, couldn't write fast enough. He was on the direct line to the dealer at one particular institution - red ticket, another red ticket, another order, another order. By lunchtime, the partners, who sat on the plinth higher than everybody else (such was the hierarchy in those days), took us all out to lunch at the Mithras Bar for pints of Pimm's to celebrate how much business he'd done. We came back from lunch, and he started writing more red tickets - more and more. The hilarity and joy turned to concern. The partners started ringing up people in the industry to check whether these orders were legitimate. They rang the bosses of the dealer at the fund manager, and they said, "Yes, it's okay - keep doing the orders. They're legitimate. He's not a rogue trader." So, the first thing the partners did was start selling their own shares. Then they started ringing their best clients and saying, "This fund manager's selling - you need to start selling, because the market is way up there." And this big institutional fund manager started to sell. Before you knew where you were, everybody was trying to get ahead of everybody else selling. It turned out that these partners had rung other brokers to ask, "Are you, by any chance, getting a particular institution selling a lot of stock?" And they said yes. It turned out that this institution, which was US-based, had sat in its ivory tower in New York and decided it was going to reduce its equity exposures across the world. That meant, in the UK, it had so many billion pounds of stock to sell. It passed the order to the UK office, and the UK office, in order to get it done, had to go to every broker and give them a whole load of orders - and everything cascaded from there. That's what starts a sell-off - a big institution, part of the herd, takes the lead and starts selling. So, what we have to watch out for when the market's up here - and we're not in a bubble at the moment, but we're certainly elevated - is this: For some reason, and it won't necessarily be logical or obvious (and the guy who writes the morning report in the newspaper won't know, but he'll make something up), someone is going to sit in an asset allocation meeting in New York or Singapore or Sydney and decide that their funds management group, which is running hundreds of billions, is going to start exiting Big Tech or exiting equities. And before you know it, you're going to see sell orders coming into the market. The moment the herd - already sensitive to a top - hears that, it'll join in, and the market will cascade. It may not need a catalyst. There may be no headline that day. It'll just start. If it hasn't really got a catalyst, it probably won't last long - it'll probably come back pretty quickly. But it can happen when the market's under pressure, as it is now, with this year's performance and the benefit of the doubt still being given to Big Tech earnings. All the Big Tech stocks are priced for perfection - and many others as well. We're vulnerable to some big fund manager deciding to sell. And everybody will chase them. We're doing 200 miles an hour with our hair on fire. Fund managers aren't stupid - they know one of them will change their mind. Look out for that day. DISCLAIMER: This content is for general information purposes only and does not constitute personal financial advice. Please consider your own circumstances or seek professional advice before making investment decisions. |
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13 Nov 2025 - Performance Report: Airlie Australian Share Fund
[Current Manager Report if available]

13 Nov 2025 - Performance Report: Quay Global Real Estate Fund (Unhedged)
[Current Manager Report if available]

13 Nov 2025 - Performance Report: Bennelong Long Short Equity Fund
[Current Manager Report if available]

13 Nov 2025 - Thirsty servers, hungry investors: how sustainable is AI?
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Thirsty servers, hungry investors: how sustainable is AI? abrdn October 2025 The rapid rise of artificial intelligence (AI) is reshaping industries, economies, and investment strategies. But beneath the surface of this technological revolution lies a complex web of environmental and financial risks - particularly around water and energy consumption. For investors, understanding these dynamics is critical to navigating both the opportunities and the vulnerabilities emerging from AI's infrastructure demands and business models. The overlooked thirst of AIWhile the energy intensity of AI has received widespread attention, its water footprint remains underappreciated. Data centres - the backbone of AI - consume vast amounts of water, both directly and indirectly. Direct use stems from cooling systems, particularly evaporative cooling, which loses up to 80% of the water used. Indirect use arises from power generation and Graphics Processing Unit (GPU), or chip, manufacturing - both of which are water-intensive processes. In 2024, data centres directly consumed 95 billion litres of water worldwide. While this is dwarfed by agricultural irrigation, the projected compound annual growth rate of 80% means data centre water use could reach over one trillion litres by 2028 - that's enough to fill 400,000 Olympic-sized swimming pools. Critically many data centres are in regions of medium-to-high water stress, which amplifies localised environmental and operational risks. Water Usage Effectiveness (WUE) is a metric that helps to measure the water efficiency of data centres. It can be particularly useful to compare efficiency across different locations and cooling technologies. Energy-water trade-offs and cooling constraintsCooling technologies present a trade-off between energy and water efficiency. Evaporative cooling is energy-efficient but water-intensive, while air-cooled systems consume more energy but less water. Innovations such as dry coolers, seawater cooling, and reusing waste heat are emerging, but they are highly location-dependent and often come with higher capital expenditure (capex) and operational complexity. Data centres are essentially racks of servers. Server-level cooling is evolving as AI workloads (specifically the GPU chips required) push the energy consumed within these racks (rack-power densities) beyond 100 kilowatts. As the racks consume more power, they create more heat, which means that traditional air cooling becomes insufficient. Liquid cooling - starting with direct-to-chip systems and then immersion cooling for the most advanced GPUs coming to market in the next couple of years - is likely to become essential. However, these systems introduce new risks, including higher capex costs, complex fluid maintenance, possible cooling failures, regulatory scrutiny [1], and execution challenges. The 'fremium' model: monetisation versus infrastructure costsAI's dominant consumer business model is known as 'freemium'. It's a business strategy where a company offers a basic version of a product or service for free, and charges for premium features, usage, or access. It poses a unique financial challenge for companies, though. While platforms like ChatGPT boast hundreds of millions of users, only a fraction are paying customers. This creates a disconnect between user growth and monetised demand. And it raises questions about the sustainability of the massive infrastructure investments that are required for AI, particularly as newer and more expensive cooling technologies will be required to keep advancing AI systems. AI's capex commitments are booming, with new announcements coming every day from the likes of OpenAI, NVIDIA, Oracle, SoftBank and others. Yet, the monetisation of these platforms remains uncertain, prompting some investors to draw comparisons with the dot-com bubble of the early 2000s. A further complication is the emergence of a 'shadow AI economy', where employees opt to use free consumer AI tools they find effective, rather than the enterprise-grade solutions their companies pay for. This behaviour undermines enterprise adoption and revenue growth, potentially slowing the capital spending cycle and making it harder for providers to justify continued infrastructure investment. Capex intensity and financial strainThe scale of AI infrastructure investment is staggering. Bain & Co estimates that meeting global computer demand will require $500 billion annually in capex. Even if firms shift all technology spending to the cloud and cut sales, marketing, and research and development (R&D) budgets by 20%, there is still a shortfall of $800 billion in revenue by 2030 that's needed to underpin AI infrastructure investments. This financial strain is potentially surfacing in accounting practices. Hyperscalers (large, cloud service providers) are extending the assumed useful life of server assets in their financial filings - an approach that can make profitability appear stronger by spreading costs over a longer period. Amazon has recently bucked this trend, in its latest financial statement, it reversed its previous decision to extend server lifespans and instead shortened the depreciation timeline, explicitly citing AI-investments as the reason. The shift potentially signals that AI infrastructure is more capital-intensive than previously assumed, and that earlier lifespan extensions may have understated the true cost. Notably, other hyperscalers followed Amazon's earlier lead in extending server lifespans, raising questions about whether current assumptions accurately reflect the pace of hardware turnover in the AI era. Strategic implications and opportunitiesDespite the risks, AI infrastructure growth presents opportunities for investors in adjacent sectors - not just in large technology companies but also in supply chains. Clean technology firms, energy providers, and component suppliers stand to benefit from rising electricity demand and hardware requirements. However, the competitive landscape is shifting rapidly. Amazon Web Services' decision to develop its own in-house cooling solution for NVIDIA's Blackwell GPUs - rather than relying on traditional external providers - underscores the fast pace of innovation in the sector. This move highlights how major players are increasingly prioritising bespoke infrastructure to optimise performance, which may disrupt established supply chains and challenge conventional providers to adapt quickly. Governments may also play a role, treating AI as strategic infrastructure and offering support that overrides short-term economics. This could create tailwinds for firms aligned with national priorities. Final thoughts...Investors should remain vigilant for signs of stress in the AI ecosystem. AI's transformative potential is undeniable, but its infrastructure demands - particularly around water and energy - require a holistic risk lens. Investors must integrate environmental, technological, and financial indicators into their due diligence process and portfolio construction. The convergence of water stress, energy intensity, and monetisation challenges creates a complex landscape. But with careful analysis and proactive engagement, investors can identify resilient players, and capture long-term value in the AI-driven future. 1. PFAS (per- and poly-fluoroalkyl substances), for example. These are also known as 'forever chemicals', which are a group of synthetic chemical compounds that don't break down in the environment. They are known to cause environmental and health issues. |
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Funds operated by this manager: abrdn Sustainable Asian Opportunities Fund , abrdn Emerging Opportunities Fund , abrdn Sustainable International Equities Fund , abrdn Global Corporate Bond Fund (Class A) |

12 Nov 2025 - Performance Report: Bennelong Twenty20 Australian Equities Fund
[Current Manager Report if available]

12 Nov 2025 - Performance Report: Bennelong Emerging Companies Fund
[Current Manager Report if available]
