Jason Hsu, co-founder of Research Affiliates and CIO of Rayliant, shares insights on smart beta and quantitative investing. He argues that simply investing in the S&P 500 is outdated, highlighting the inefficiencies in Asian markets due to retail speculation and governance risks. Jason discusses the evolution of factor investing, emphasizing its advantages over traditional indexing. He also explains how Rayliant employs machine learning to create robust portfolios and shifts retail investors from short-term trading to long-term value creation.
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Build Multi-Factor Products For Larger Markets
Focus product development on larger, more liquid Asian markets like Taiwan, Japan, Korea, Hong Kong, and China.
Use multi-factor models and machine learning to build smarter smart-beta 2.0 products for those markets.
insights INSIGHT
Factors Are The Portfolio's Nutrients
Factors act like portfolio 'nutrients' that reveal structural strengths and weaknesses across holdings.
They simplify portfolio construction by focusing on macro building blocks rather than complex single-stock stories.
insights INSIGHT
Two Sources Of Factor Returns
Factor returns stem from two sources: risk premia and behavioral biases.
In Asia, behavioral biases provide more 'free-lunch' opportunities than in the highly efficient US market.
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Should you invest in the S&P 500, or look for smarter ways to beat the market? Jason Hsu, Co-Founder of Research Affiliates ($159B AUM) and now CIO of Rayliant, explains why simply buying the index or asking “should I invest in ETFs” isn’t enough. In this episode, he breaks down smart beta vs S&P 500, systematic investing, and how factor investing strategies and fundamental indexing can deliver some of the best long-term investment strategies for investors who want to know how to beat the market beyond traditional index funds.Asian markets are less efficient than the US, Jason says. With higher retail speculation, governance risks, and volatility, opportunities open up for quant investing through Asian ETFs, China stock market investing, and emerging markets investing strategies that capture inefficiencies. As CIO of Rayliant, Hsu shows how his team builds factor-based portfolios across China, Japan, Korea, Taiwan, and other emerging markets to turn inefficiency into alpha.We also cover:- How Jason Hsu cofounded Research Affiliates, scaling systematic strategies to manage $159B AUM- Launching the PIMCO All Asset Fund in 2002 and bringing multi-asset investing strategies to retail investors- The origin of smart beta ETFs and why fundamental indexing offers a better alternative to cap-weighted indexes- How the tech bubble exposed flaws in traditional indexing and set the stage for factor investing strategies- Why governance factors and valuation discipline are especially important in emerging markets- Building Rayliant’s smart beta 2.0 products using multi-factor models and machine learning in investing- How factors in investing reveal the “nutrients” of a portfolio for long-term compounding- The difference between risk premia and behavioral biases as drivers of factor returns- Examples of behavioral investing mistakes in Asia and how professionals can capture alpha from retail flows- Why low-frequency quant strategies align better with pension funds and sovereign wealth funds than high-frequency trading (HFT)- The future of quant investing explained: machine learning, non-linear models, and portfolio construction- Jason’s career advice for young professionals navigating the hedge fund and asset management career path00:00 Intro01:42 Founding Research Affiliates and early startup days03:02 Launching the PIMCO All Asset Fund in 200204:26 Smart beta ETFs explained and how they started09:19 Spinning off Rayliant and focus on Asia10:26 Why Asian markets are less efficient than the US11:43 Opportunities from inefficiency and alpha in China13:38 Gambling analogy and retail speculation in Asia16:53 Liquidity challenges in smaller emerging markets20:41 Rayliant’s product offerings and smart beta 2.020:57 What factors reveal about markets and portfolios23:34 Risk premia vs behavioral biases in factors25:39 Governance, valuation, and smart money factors in Asia28:27 Using machine learning in Rayliant’s strategies34:05 Can discretionary managers still have edge today38:39 Poker, luck, and systematic investing advantages41:00 Future of discretionary managers and pod firms42:44 Are high-frequency trading firms sustainable long term46:22 Rayliant’s mission and value to society50:00 Career advice for young finance professionals53:14 Closing thoughts
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