In the dynamic landscape of wealth management, where investment choices proliferate and client preferences diversify, Morningstar's Nicolas Gisbert offers a roadmap for smarter fund selection. Gisbert's insights, shared at the Hubbis Malaysia Wealth Management Forum 2026, underscore the evolving nature of fund selection and the need for a disciplined, transparent, and fundamentally driven approach. His presentation, titled 'Smarter Fund Selection and Monitoring in a Changing Investment Landscape', delves into the principles underpinning Morningstar's research framework, the expanding complexity of the investment universe, and the imperative for wealth managers to adapt their fund selection processes.
The Evolving Investment Landscape
Gisbert begins by highlighting three forces reshaping the fund selection landscape: expanding investment choice, the data and technology revolution, and personalisation. The investment universe, once dominated by mutual funds, now encompasses a broad spectrum of assets, including ETFs, private markets, and alternatives. This expansion, while offering more opportunities, also introduces complexity. The data and technology revolution, particularly the advent of AI, is transforming how data is collected, cleaned, analysed, and consumed, creating both internal productivity gains and external client-facing opportunities for Morningstar.
Personalisation, driven by ESG considerations and client preferences, is another significant trend. Gisbert notes that AI is poised to make customisation more scalable, allowing investors to build portfolios that reflect their specific objectives, constraints, and preferences. This trend underscores the importance of understanding client needs and tailoring investment strategies accordingly.
Morningstar's Expanding Research Universe
Morningstar's data, Gisbert asserts, has become part of the global language of investing. The firm covers a wide range of investment types and market participants, including managed investments, public and private companies, ESG-rated securities, DBRS credit-rated securities, ETFs, model portfolios, private market data, and retirement solutions. This breadth is crucial, as fund selection now requires assessing products across diverse asset classes, strategies, and mandates.
Morningstar's capabilities span research and ratings, data and analytics, indexes, managed portfolios, credit ratings, private market insights, and ESG research. Gisbert argues that this broader ecosystem reflects the interconnected nature of investment selection, where fund research, portfolio construction, index design, asset allocation, and client advice all depend on consistent data and comparable analysis.
A Five-Step Fund Selection Process
Gisbert outlines a five-step fund selection framework: identification, quantitative screening, qualitative screening, product and operational due diligence, and portfolio integration and monitoring. The identification step involves defining the relevant universe by asset class, sector, region, domicile, category, and other criteria. Quantitative screening, while considering performance, emphasises the need for alternative assessment methods such as multi-factor analysis, attribution, and risk-adjusted return metrics.
Qualitative screening, a cornerstone of Morningstar's Medalist Rating framework, evaluates funds based on 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 examines the asset management firm's ownership, financial strength, and organisational stability.
Looking Beyond Past Performance
Gisbert stresses that past performance should not be the sole basis for fund selection. He advocates for a holistic approach, considering risk-adjusted returns, consistency of alpha generation, peer comparisons, fees, active share, and qualitative factors. Fees, in particular, are highlighted as a critical determinant of outcomes, with Gisbert noting that fee pressure is intensifying across asset management.
The broader message is that fund selection should combine quantitative evidence with qualitative judgement. Numbers can identify candidates, but they do not always explain whether a manager has a repeatable edge. Morningstar's Medalist Rating, Gisbert explains, is designed to assess whether a fund is set up to generate future alpha.
Due Diligence and Portfolio Fit
Fund selection, Gisbert emphasises, should not stop once a shortlist is created. Due diligence remains essential, involving an understanding of the manager's interaction points and the fund's operational infrastructure. Portfolio fit is equally crucial, requiring risk budgeting, portfolio look-through analysis, and correlation assessment to ensure the fund aligns with the client's risk profile and asset allocation.
Monitoring, Gisbert notes, is as important as selection. Regular performance reviews, risk metric tracking, material change assessment, and long-term consistency evaluation are necessary to ensure the fund continues to serve the client's objectives. Manager changes, in particular, require careful consideration, as they can affect the original basis for selection.
Common Pitfalls in Fund Selection
Gisbert identifies several recurring mistakes 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.
AI, Data, and the Future of Research Consumption
Gisbert concludes by discussing Morningstar's position in the AI revolution. He emphasises the importance of combining trusted data, accumulated research, and analyst-reviewed content to enhance AI's utility. Morningstar's MCP server, he notes, connects the firm's universe, database, and research with AI tools, allowing users to query data and insights directly through AI-enabled workflows.
The implications for fund selection are significant. AI can help collect, process, and surface information more efficiently, but the research framework still matters. Without strong data, consistent methodology, and human oversight, AI can amplify weak inputs rather than improve decision-making. Gisbert's message is clear: better fund selection requires a combination of transparency, independent research, long-term thinking, data quality, and disciplined monitoring.
In the evolving landscape of wealth management, Gisbert's insights offer a practical roadmap for wealth managers in Malaysia. By embracing a smarter fund selection process, they can navigate the complexities of the investment universe, meet client preferences, and ultimately improve investor outcomes. Gisbert's emphasis on transparency, independent research, and disciplined monitoring serves as a guiding principle for wealth managers seeking to enhance their fund selection practices and deliver better results for their clients.
