In the dynamic world of wealth management, where investment landscapes are constantly evolving, Morningstar's Nicolas Gisbert offers a compelling perspective on smarter fund selection. His insights, shared at the Hubbis Malaysia Wealth Management Forum 2026, shed light on the critical aspects of navigating this complex terrain. Gisbert's presentation, titled 'Smarter Fund Selection and Monitoring in a Changing Investment Landscape', emphasizes the need for a disciplined, transparent, and fundamentally driven approach to fund selection. This is particularly relevant in a market where investment choice is expanding, client preferences are becoming more personalized, and AI is reshaping research consumption.
The Changing Investment Landscape
Gisbert begins by highlighting three key forces that are reshaping the investment landscape. Firstly, the expansion of investment choice, which has moved beyond mutual funds to include ETFs, private assets, and alternatives. This broader universe presents both opportunities and complexities, requiring wealth managers to assess a wide range of products. Secondly, the data and technology revolution, particularly the role of AI, is transforming how data is collected, analyzed, and consumed. Morningstar is leveraging AI-enabled tools to enhance client access to research, but Gisbert stresses the importance of high-quality, verified data and analyst-reviewed content.
Lastly, personalisation is becoming a structural trend, with investors increasingly seeking portfolios that reflect their specific preferences, such as ESG considerations, Shariah compliance, and industry exclusions. Gisbert notes that AI is likely to play a significant role in making these customized portfolios more scalable.
Morningstar's Mission and Research Framework
At the heart of Morningstar's approach is its mission to empower investor success. This mission is underpinned by three principles: transparency, independence, and long-term focus. Transparency, in particular, has been a cornerstone of Morningstar's approach since its founding over 40 years ago, when investors often had limited visibility into the contents of mutual funds. Gisbert uses the analogy of dim sum, where the contents are not always obvious, to illustrate the importance of transparency in fund selection.
Morningstar's research framework is designed to provide a clearer and more forward-looking evaluation of funds. It combines data, qualitative judgment, portfolio context, and ongoing monitoring. Gisbert outlines a five-step process: defining the universe, quantitative screening, qualitative assessment, due diligence, and portfolio integration and monitoring. This framework is supported by Morningstar's Medalist Rating, which evaluates funds based on three pillars: People, Process, and Parent, to assess future alpha potential.
Looking Beyond Past Performance
One of the key messages from Gisbert is the need to look beyond past performance in fund selection. While past performance is visible, it is not sufficient. Gisbert emphasizes the importance of risk-adjusted returns, consistency of alpha generation, peer comparisons, fees, active share, and qualitative factors. He highlights the role of fees in eroding outcomes and the need for wealth managers to assess costs properly. The message is clear: fund selection should combine quantitative evidence with qualitative judgment.
The Morningstar Medalist Rating Framework
The Medalist Rating is a qualitative assessment tool used by Morningstar to evaluate funds on a forward-looking basis. It is built around three pillars: People, Process, and Parent. The People pillar assesses the quality, experience, and alignment of the investment team, while the Process pillar evaluates security selection, idea generation, and risk management. The Parent pillar looks at the asset management firm itself, including ownership, financial strength, and regulatory standards. Together, these pillars support Morningstar's ratings, which indicate the fund's positive alpha potential and conviction.
Due Diligence and Portfolio Fit
Gisbert stresses the importance of due diligence and portfolio fit in fund selection. Wealth managers must understand the manager's interaction points and assess whether the fund is supported by a coherent operating model. Portfolio fit is equally crucial, as a selected fund must make sense within the client's broader asset allocation. Gisbert highlights risk budgeting, portfolio look-through analysis, and correlation assessment as key tools for ensuring a fund's suitability.
Common Pitfalls in Fund Selection
Gisbert identifies several common pitfalls in fund selection, including chasing performance, ignoring fees, poor diversification, neglecting risk assessment, and overlooking fund manager changes. These pitfalls underscore the need for a repeatable framework rather than a selection process driven by recent returns or manager marketing. The emphasis is on combining transparency, independent research, long-term thinking, data quality, and disciplined monitoring into a coherent process.
AI, Data, and the Future of Research Consumption
In the context of AI, Gisbert emphasizes the importance of high-quality, verified data and analyst-reviewed content. Morningstar has developed an MCP server to connect its universe, database, and research with AI tools, allowing users to query data and insights directly. This is changing client conversations, as clients can now access research more efficiently through AI-enabled workflows. However, Gisbert stresses that AI is only as good as the data and research behind it, and the research framework still matters.
A More Disciplined Standard for Fund Selection
In conclusion, Gisbert argues that fund selection is becoming more demanding. The broader investment universe, personalized client preferences, and evolving technology all require wealth managers to adopt a more disciplined approach. The key message is that better fund selection involves combining transparency, independent research, long-term thinking, data quality, and disciplined monitoring into a coherent process. This approach is fundamentally about improving investor outcomes, ensuring that fund selection is not just about past performance but also about understanding the fund's contents, its role in the portfolio, and its ability to continue serving the client's objectives.
