Recently, I sat down with Jason Hsu, founder of Reliance Global Advisors and chief economist at East West Bank, to discuss the evolution of factor investing, the challenges facing the asset management industry, and the opportunities offered by modern technologies and methods.

This interview is A conversation with Frank Fabozzi, CFA Series, sponsored by the Center for Research and Policy. The series aims to invite leading experts in finance and economics to engage in dialogue to explore the key issues shaping the future of the industry. Hsu is a recognized leader in quantitative asset management and co-founder of Research Affiliates. You can register here for my upcoming conversation with Lori Heinel, CFA, Executive Vice President and Global Chief Investment Officer of State Street Global Advisors.

Xu’s reflections at this session highlighted the changing investment paradigm, the growing pressure on asset managers to differentiate themselves, and the critical role of governance, innovation and long-term thinking in an increasingly competitive and complex environment.

expanding factor universe

Hsu begins by tracing the origins and evolution of factor-based strategies. Originally rooted in academic finance, these strategies have become a staple of institutional and retail investing. Traditional factors such as value, momentum and size continue to play an important role, but Xu highlighted growing interest in broadening the range of factors.

Today, asset managers are increasingly combining macroeconomic signals, such as interest rate changes or inflation dynamics, with behavioral factors driven by market psychology. The expansion of the factor toolkit reflects both a reaction to the commoditization of markets and a recognition that traditional factors, while still valuable, cannot alone address the complexities of modern financial markets.

One of Xu’s key points is the importance of a factor-based strategy based on clear economic principles. He warns against overreliance on historical data or data mining methods that lack a theoretical basis. While backtesting can produce impressive results, strategies developed without a full understanding of their underlying drivers may fail under real-world conditions.

Xu believes that robust factor strategies should be built on empirical evidence and an intuitive understanding of how and why certain relationships persist across different market environments. This combination ensures that factors remain relevant and valid even if market dynamics change.

The commoditization of fundamentals strategies is a central theme discussed by Xu. As quantitative tools and techniques have become more readily available, barriers to implementing traditional factor models have decreased. This has led to lower fees and increased competition among asset managers, forcing companies to innovate to differentiate themselves.

Hsu noted that differentiation often requires exploring new or customized factors, but it also requires maintaining transparency and aligning with customer expectations. Companies must strike a balance between pushing the boundaries of innovation and delivering strategies that investors can understand and trust.

Structural challenges in asset management

Xu also addresses structural challenges within the asset management industry, particularly those related to governance and incentives. He criticized the pervasive short-termism that dominates many investment decisions, arguing that this mentality is often inconsistent with the long-term goals of institutions and retail investors.

Pressure to deliver quarterly results often results in strategies that prioritize immediate performance over sustainable value creation. Koh advocates for establishing governance structures that reward long-term thinking and encourages asset managers to focus on delivering outcomes that align with their clients’ broader goals.

The role of technology in reshaping asset management was another key focus of the interviews. Xu acknowledges the transformative potential of machine learning and artificial intelligence in modern portfolio management. These technologies enable asset managers to discover complex patterns, process large data sets and develop more complex models.

Hsu warned against misuse of the technology, highlighting the risk of overfitting and lack of interpretability in many machine learning models. In finance, decisions often have significant consequences, and failure to explain how a model reaches its conclusions can undermine its practical value.

Hsu advocates a balanced approach that combines machine learning (ML) with traditional financial and economic theory. Machine learning should not replace existing methods but complement them by enhancing understanding of complex relationships and providing new insights. This integration ensures that models remain robust and interpretable, allowing portfolio managers to take advantage of higher-order analytics without sacrificing transparency or trust.

The need for a rigorous, data-driven approach to ESG

The growing prominence of environmental, social and governance (ESG) investing formed another key theme in my conversation with Hsu. He noted that demand for sustainable investment strategies has grown significantly, driven by changes in institutional mandates and societal expectations.

However, incorporating ESG considerations into the investment process poses unique challenges, particularly in quantifying and integrating ESG impacts into traditional portfolio frameworks.

Hsu emphasized that ESG investing requires a rigorous, data-driven approach to ensure it goes beyond superficial claims or “greenwashing.” By integrating ESG metrics with broader financial objectives, asset managers can develop strategies that are both impactful and economically viable.

Diversity of the investment team is another area where Hsu sees significant opportunity for improvement. He believes fostering intellectual diversity and encouraging collaboration are critical to success in the evolving world of asset management.

Diverse teams bring different perspectives and approaches to problem solving, which can enhance creativity and adaptability. In an industry where market conditions and customer needs are constantly changing, the ability to think critically and adapt quickly is invaluable.

One of the most compelling aspects of my conversation with Hsu was his discussion of the challenges and opportunities of implementing factor-based strategies in real-world market dynamics. He pointed out that value and momentum are not static, but change as the market changes. This evolution requires constant re-evaluation and adjustment of strategies to ensure their continued relevance. Hsu emphasized the importance of stress testing factor models under different scenarios to assess their robustness and potential vulnerabilities.

Customization is key

Xu also reflected on the growing role of customization in asset management. As customers demand more tailored solutions, companies must develop strategies that meet specific needs and goals. This customization often involves creating unique combinations of factors or integrating non-traditional data sources (such as alternative data sets) to improve forecast accuracy. By aligning strategies with clients’ specific goals, asset managers can deliver greater value and differentiate themselves in a competitive market.

The future of asset management

The interview ended with a forward-looking outlook on the future of asset management. Hsu envisions a continued shift toward greater reliance on technology, customization and the integration of non-traditional sources. He emphasized the importance of adaptability both at the company level and within individual teams to cope with the complexities of the modern market. Xu’s insights underscore the need for a holistic approach to asset management that combines innovation, rigorous analysis and a commitment to long-term value creation.