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Because Exchange Traded Funds (ETFs) are traded on stock exchanges and are available across several asset classes, they too are exposed to the same market movements as ordinary shares and managed funds. While volatility is a natural part of market cycles, nobody likes seeing their ETF or investment portfolio fall in value. Sharemarket turbulence can lead to investors making panicked or rash decisions, but those who are successful remain composed and use downturns to reassess their position and search for opportunities to strengthen their portfolio.

Here are five key strategies successful ETF investors employ during volatile times:

1. Clear goals that shape investment strategy

The first step to any investment strategy starts with having a clear understanding of what you want to achieve and establishing a plan to reach it.

Whether it is building a retirement nest egg, saving for children, looking to buy your first apartment or family home, or any other wealth accumulation purpose, successful investors have a plan.

These goals are usually well defined, have step-by-step instructions and are recorded to give investors a reference point during volatile markets. For example, an instruction could be to create a stop-loss order on your ETF or other shares - this potentially limits your losses (or realises gains) by automatically selling when your asset reaches a pre-determined price.

By having clear goals and sharp investment strategies, investors are less likely to make impulsive decisions based on short-term market fluctuations. Instead, they remain focused on their long-term plans, adjusting their portfolios only when necessary.

2. Diversification: The shield against uncertainty

Investing in ETFs are a simple and potentially cost-effective way to spread your portfolio across different asset classes, specific sectors and geographic regions. Successful investors diversify their portfolio to mitigate the impact of a downturn in any single area.

It is tough to predict which asset class will outperform as nobody can accurately predict the future, but good investors ensure they are well-diversified to protect against sudden market movements.

3. Dollar-cost averaging: Regular top-ups spread the load

Dollar-cost averaging is common among successful investors because it aims to reduce the impact of market volatility. By investing a fixed amount regularly, you can average out the purchase price of your investment over time.

Here is an example:

Imagine you decide to invest about $100 in a particular ETF every month for six months. Here is how it might look:

• Month 1: ETF price is $10, you buy 10 units ($100)
• Month 2: ETF price is $12, you buy 9 units ($108)
• Month 3: ETF price is $8, you buy 13 units ($104)
• Month 4: ETF price is $11, you buy 9 units ($99)
• Month 5: ETF price is $9, you buy 11 units ($99)
• Month 6: ETF price is $10, you buy 10 units ($100)

After six months, you have invested $610 and purchased a total of 62 units. The average cost per unit is approximately $9.84, which is lower than the highest price you paid ($12) and higher than the lowest price ($8).

4. Long-term perspective: Patience pays off

Another key trait of successful ETF investors is to maintain a long-term perspective, especially during periods of volatility. These investors understand that markets over the shorter term can be unpredictable and trying to time the market can be near impossible.

If you invested $10,000 in June 1994 and tracked the S&P/ASX All Ordinaries Total Return Index (the top 500 companies in the ASX), you would now have more than $135,000¹. This is even though the market went through a dot.com bubble burst, the global financial crisis and most recently the COVID-19 pandemic.

By staying invested and patient, investors benefit from the compounding returns. This perspective allows them to ride out market downturns with confidence, knowing that over time, the market has historically trended upwards. Their focus remains on the long-term growth of their portfolio rather than short-term gains or losses.

5. Bear markets as opportunities for future gains

While a bear market can test an investor's patience, those who have a successful mindset see it as an opportunity over a setback.

These investors believe long-term markets will continue to rise, meaning after a period of volatility there is usually a strong recovery. By remaining invested during the downturn and even adding to their portfolios when in a bear market, these investors position themselves to benefit as asset prices recover.

Moreover, bear markets provide opportunities to buy high-quality assets at lower prices, which can lead to significant gains when the market rebounds. Successful investors maintain their conviction during these periods, understanding that patience and a well-thought-out strategy will lead to strong returns.

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What happens when you ask artificial intelligence (AI) to predict how it might change the job of fund management - and then invite a leading AI researcher to respond? 

That's the premise behind this article, which explores how AI could reshape the investment management process within the next five years. 

We began by asking ChatGPT - powered by GPT-4-turbo - to generate a series of predictions, both optimistic and cautionary, about the future of fund management. 

Then we invited Prof. Sotirios Sabanis, who leads a team of AI researchers at the University of Edinburgh that's been collaborating with us at the Centre for Investing Innovation, to weigh in.

The result is an exchange between machine-generated foresight and human expertise - a glimpse into what the future might hold when algorithms and people routinely work side by side:

Portfolio construction gets a sidekick

AI's take: AI will assist fund managers by rapidly generating optimised portfolios based on real-time data, investor preferences, and risk constraints -- not replacing the manager, but acting as a tireless quant assistant.

Prof. Sabanis responds: Rapidly generated, optimised portfolios rely heavily - and will continue to do so - on finely-tuned optimisation algorithms, advanced statistical and stochastic modelling, and adequate computational resources. AI introduces a new level of automation by integrating the collection and processing of diverse data inputs - including some available in real time - with these core components. The result is the creation of information-rich decision-making tools that enable fund managers to gain deeper, faster and more granular insights. When developed with the right level of sophistication, these AI tools can act as supercharged quantitative assistants, offering fund managers unprecedented real-time analytical capabilities.

Sentiment as a signal

AI's take: Natural language processing will mine news, earnings calls, and social media to detect shifts in sentiment before they show up in prices. But beware: signal or noise?

Prof. Sabanis responds: Recent breakthroughs in natural language processing (NLP) have led to significant scientific advances, the full implications of which are only beginning to be explored across a wide range of application domains - including asset management - where early results have been promising. Sentiment analysis has seen a notable improvement in predictive power, driven by these NLP developments. However, sentiment cannot be fully captured through textual data alone. To enhance predictive accuracy and reduce noise in relevant datasets, complementary analytical tools are required. The integration of multimodal data - such as video, audio and text - enabled by emerging AI technologies, offers a promising path forward.

Forecasting gets a facelift

AI's take: AI models will enhance macroeconomic forecasting by identifying nonlinear patterns and hidden correlations -- though they may still struggle with black swan events and regime shifts.

Prof. Sabanis responds: There is growing evidence that AI models will significantly enhance macroeconomic forecasting by leveraging diverse, information-rich datasets and uncovering deeper correlations and interdependencies. A compelling example comes from recent advances in AI's predictive capabilities for inherently unstable systems, such as weather forecasting. These developments offer a glimpse into AI's potential in navigating complex, dynamic environments. However, accurately modelling extreme events well in advance remains a substantial challenge. The rarity of such events, coupled with their non-linear, non-stationary and non-periodic characteristics, limits the availability of training data and constrains the predictive accuracy - even for today's most advanced AI systems.

Human bias, meet machine bias

AI's take: AI may reduce some behavioural biases -- but introduce new ones. Overfitting, data drift, and model opacity could create false confidence in flawed outputs.

Prof. Sabanis responds: AI can act as a powerful behavioural counterweight - but only when its limitations are well-understood and carefully managed. In other words, AI holds significant promise as a behavioural stabiliser in financial decision-making - particularly through its capacity to reduce common cognitive biases such as overreaction, recency bias and confirmation bias. By anchoring investment strategies in structured data and statistical learning, rather than emotion or narrative, AI can enhance objectivity.

However, its effectiveness is not unconditional. AI systems are not neutral; they can introduce machine-driven biases rooted in their training data, algorithmic architecture and feedback loops. This creates a new class of risk - systemic, often opaque and harder to detect than human behavioural patterns. Risks such as overfitting, data drift and model opacity are particularly dangerous because they give an illusion of precision and reliability, often masked by high back test performance or technical complexity. If left unchecked, these flaws may propagate through portfolios, investment committees and client reporting chains - ultimately undermining the very trust that AI is meant to bolster. Hence, robust stress testing, continuous validation and model interpretability frameworks are essential safeguards to ensure that AI adds real value to the investment process - rather than introducing new risks or causing unexpected problems.

The rise of the 'explainable AI' arms race

AI's take: As AI becomes more embedded in decision-making, the pressure to explain its logic to clients, regulators, and boards will intensify. Transparency will be a competitive edge.

Prof. Sabanis responds: As AI becomes increasingly embedded in financial decision-making, explainability is no longer optional - it's a strategic imperative. Clients, regulators and boards will expect straightforward, credible answers as to how AI models arrive at key insights or investment decisions. Transparency will not only be a compliance requirement but a source of competitive differentiation.

Firms that can articulate how their AI systems work - what data is used, how decisions are made and where risks lie - will build stronger client confidence and demonstrate operational maturity. The future edge will lie not just in building powerful AI, but in making it explainable and trustworthy. This will require investment in interpretable models, documentation frameworks and governance protocols. Transparency serves not only as a means of mitigating downside risk, but also as a critical enabler of broader adoption, a catalyst for attracting institutional capital and a foundation for establishing long-term strategic credibility within an AI-driven financial ecosystem.

Globally equity markets rallied strongly in June. In the US, the S&P 500 increased +5.0%, the Nasdaq rose +6.6%, whilst in the UK, the FTSE fell -0.1%. Domestically, the All-Ordinaries Accumulation index underperformed its US peers, rising +1.4%. On the ASX, the top performing sector was energy, as the Middle East conflict pushed up energy prices. The worst performing sector was gold, as investors took profits following a 6-12 month period of strong outperformance. In global news, the conflict in the Middle East continued to create volatility in financial markets, with the Brent crude oil price rising +6% in June. In Australia, the key piece of news was a lower than expected inflation data point which has resulted in expectations of a 25 basis point reduction in the official cash rate (currently 3.85%) in July.

In bond markets, the US 10-year bond yield fell -19 basis points (bp) to 4.25%, whilst its Australian counterpart declined -11bp to close at 4.16%. The Australian dollar was stronger in June, closing at US$0.658, up 1.5 cents.

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Apparently, Confucius did not say "One Picture is Worth Ten Thousand Words" after all. It was an advertisement in a 1920s trade journal for the use of images in ads on the sides of streetcars...

We get a bit fixated on US equity valuations, with Vanguard highlighting the high hurdles required to achieve another decade of >12% returns; even as the 10 year bond yield has been more competitive and tariffs drive negative earnings revisions in the US. The Ex-US equities world has been fairing better out of the tariff scenario. We check in on interest rate expectations in the US and Australia. Bain bemoans a softer global buyout environment lately while The Information tackles the private market liquidity problem graphically. Finally, the US office CMBS delinquency rate charts near-vertical while Aussie mortgage arrears have been on the rise.

Total market cap of S&P 500 firms >10x Price/Sales (US$ trillion)

 

Source: Kailash Capital Research

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Repeat of returns >12% for US equities requires record-high profit margins, record-high valuations, or a combination of both

 

Source: Vanguard

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S&P 500 Price/Free Cash Flow (cap weighted and equal weighted) v index level

 

Source: Fidelity

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US 10-year government bond yield

 

Source: FRED

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Consensus S&P 500 earnings by calendar year

 

Source: Columbia Threadneedle

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Forward 12-month global earnings forecasts & net balance of upgrades v downgrades

 

Source: Barclays Private Bank

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% of companies with downwards earnings revisions & impact of tariffs

 

Source: Schwab

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The rest of the world is ahead since Feb 19 - in common currency

 

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US market still looking for a Fed Reserve cash rate cut in September - but not quite as sure as a week ago

 

Source: CME

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Australian market backing in a 25 basis points rate cut by the RBA in July

 

Source: ASX

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Global buyout deal value, by region ($US billion)

 

Source: Bain, Dealogic

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Scarcity of opportunities to convert private investments into cash

 

Source: The Information

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Australia mortgage arrears

 

Source: cotality

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US Office CMBS delinquency rate %

 

Source: Trepp, WOLFSTREET.com

July 2025 Edition