Infundly Weekly Briefing - 28 August 2026
This week’s Infundly Research Reads focuses on how investment outcomes are shaped not just by asset allocation, but by the structures, assumptions and decision frameworks behind it. The selected research covers active versus passive success rates, target-date glidepaths, AI-enabled investment processes, capital-market assumptions, fund flows and tactical asset allocation, all with direct relevance for professional fund selectors and allocators.
What changed: Morningstar’s latest study, covering roughly 32,000 Europe-domiciled active and passive funds, shows the one-year success rate for active equity managers falling to 28.4%, while the 10-year success rate is only 11.9%. Fixed income remains more favourable to active management: the corresponding one-year success rate is 46.8% and the 10-year rate 33%. The dispersion across categories is substantial; active emerging-market equity managers have recently fared much better than UK or eurozone large-cap managers.
Worth reading because: The interesting conclusion is not simply “passive beats active”. It is that the opportunity set for active management differs materially by market structure. Concentration, index construction, security breadth and the scope for country, duration or credit selection all influence the probability of success. This provides a useful empirical starting point for deciding where manager-selection resources are most worth deploying.
What changed: Published on 25 August, Amundi revisits lifecycle investing in the context of longer working lives, changing employment patterns and greater individual responsibility for retirement outcomes. It argues that glidepaths increasingly need to consider human capital, broader diversification, less reliable stock-bond correlations and potentially private assets rather than relying solely on traditional equity-to-bond de-risking.
Worth reading because: This is particularly relevant to target-date and default-fund due diligence. The question should not just be whether a provider has a sensible-looking glidepath, but what assumptions determine its shape: retirement behaviour, contribution patterns, sequencing risk, expected correlations, liquidity and the role of illiquid assets. Two superficially similar target-date propositions can therefore embody quite different investment judgements.
What changed: Amundi’s research brings together the rapidly developing literature on autonomous AI agents in finance, including orchestration, tool use and reinforcement-learning techniques. Its most important practical conclusion is that hallucination and reproducibility remain major constraints, meaning current financial applications still require human verification rather than unfettered autonomy.
Worth reading because: This moves AI due diligence beyond whether an asset manager “uses AI”. Selectors should understand which parts of research and portfolio construction are delegated, whether outputs are reproducible, what data and tools agents can access, how errors are detected and where human accountability re-enters the process. It is increasingly an investment-governance question, not merely a technology question.
What changed: Amundi’s 19 August update increases expected returns for several government-bond and selected equity markets, primarily because valuations changed during the first half of 2026. More broadly, it argues that geopolitical, technological and energy-related structural shifts are reinforcing a regime characterised by stickier inflation, larger fiscal deficits and greater macro uncertainty.
Worth reading because: Capital-market assumptions often look deceptively precise. This update illustrates how quickly expected returns can change when starting valuations move, even if the longer-term economic thesis does not. For multi-asset manager due diligence, it is useful to ask how frequently assumptions are refreshed, what actually drives revisions and how sensitive strategic allocations are to relatively small changes in expected returns.
What changed: Invesco’s tactical framework currently characterises the global economy as being in a slowdown regime, growth remains above trend but is decelerating. Resilient earnings support risk assets, while persistent inflation, energy-market uncertainty and an unclear Federal Reserve path argue for maintaining diversification. Its model remains modestly overweight equities versus fixed income rather than making a strong directional call.
Worth reading because: It is a useful example of how a systematic multi-asset framework translates ambiguous macro evidence into portfolio positioning. For manager research, the more interesting questions are how regimes are defined, what causes the model to change state, how quickly positioning responds and whether supposedly diversified signals become correlated precisely when markets become stressed.
Infundly takeaway
This week’s research highlights why fund selection increasingly requires separating the investment decision from the structure through which it is expressed. Active-management success varies significantly by market, a target-date glidepath embeds assumptions that may not be obvious from its headline allocation, and the growth of active ETFs shows that investment style and vehicle choice are becoming increasingly independent decisions.
A second theme is the importance of understanding the models behind portfolio decisions. Whether it is an AI research agent, a capital-market assumption or a tactical allocation framework, selectors should look beyond the output and ask what inputs drive it, how frequently those assumptions change, where judgement enters the process and what happens when the model is wrong. In each case, transparency around the decision architecture is as important as the resulting portfolio.
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