Jason Hsu: China Is 'the Last Great Remaining Alpha Reservoir'

Episode Summary

Executive Summary: Jason Su explained why he founded Rayliant to capture China’s “alpha reservoir” and underinvested beta, arguing that China’s retail-dominated, lightly institutionalized market creates persistent opportunities for active managers. He discussed investor performance-chasing, regulatory paternalism, quant investing’s stage in China, the importance of local talent, and how market structure and rules shape returns and behavior.

Main Topics: Why Rayliant Focuses on China: Su founded Rayliant to exploit China’s market inefficiencies, investor behavior, and growth potential, viewing China as a rare place where alpha can still be found and beta remains attractive. Retail-Dominated Market Dynamics and Persistent Alpha: He argued that China’s market is still heavily driven by retail trading, which supports active manager outperformance and helps explain durable alpha opportunities despite the general zero-sum nature of markets. Investor Behavior, Performance Chasing, and Return Gap: Su said Chinese fund investors lag fund returns materially because they chase recent performance, leading to a much larger return gap than in the U.S. Chinese Regulation and Market Structure: He described Chinese regulators as paternalistic and highly interventionist, with rules designed to protect weaker participants and shape market outcomes, sometimes in ways that differ sharply from Western norms. Quant Investing in China and Role of Local Talent: Su said quant investing in China is still early but progressing, and emphasized that local expertise, market knowledge, and on-the-ground talent are essential for extracting reliable alpha. Accounting, SOEs, and Non-Obvious Signals in China: He highlighted unusual China-specific signals such as executive biographies and the behavior of state-owned enterprises, plus accounting quirks like downward earnings management driven by listing rules. Academic Research, Machine Learning, and What Matters: Su praised machine-learning methods for more honest backtests but criticized hype around AI-driven alpha strategies, arguing that ecosystem and client-behavior research is underappreciated.

Key Arguments: China remains a comparatively inefficient market because 85% of trades are retail-driven, creating persistent opportunities for active managers. The average Chinese mutual fund has delivered roughly 2% to 4% net alpha over the last decade because retail participation is large and slow to institutionalize. Chinese fund investors underperform fund returns by about 9% because they chase performance, a much larger gap than the roughly 2% return gap in the U.S. Regulators in China are not simply arbitrary; they act as paternalistic “tiger moms and tiger dads,” trying to protect weaker investors through iterative rule changes. Foreign asset managers often fail in China because they underestimate cultural, language, and local-management challenges; success requires real autonomy for local teams. Quant investing in China is in an early “inning two” stage, with simple factors working first and more advanced ML/HFT approaches only beginning to emerge. Local talent is essential for China investing because market understanding, data interpretation, and regulator interaction cannot be done effectively from afar. State-owned enterprises are not homogeneous; central SOEs can outperform while local SOEs often lag, and executive bios can reveal both governance quality and policy support. Chinese firms often manipulate earnings downward, not upward, because exchange rules punish negative earnings and encourage smoothing. The most valuable academic research is not just about new machine-learning models, but about better backtesting, ecosystem incentives, and investor behavior.

Data Points: Retail share of trades in China: 85% - Su used this to explain why active managers can still generate alpha in China. Average net alpha of Chinese mutual funds over 10 years: 2% to 4% - He said this is the approximate net-of-fee alpha delivered by the average mutual fund in China. U.S. fund investor return gap: About 2% - Su contrasted this with China to illustrate the scale of performance-chasing. China fund investor return gap: About 9% - He said investors in China lag the funds’ returns by roughly nine percentage points. Typical mutual fund manager tenure in China: 2 years - He cited this as evidence of a young industry and high turnover. SOE underperformance for locally connected firms: About 6% behind the market - He said city- or town-level politically connected SOEs tend to underperform on average. SOE outperformance for centrally connected firms: 2% per annum - He said centrally connected SOEs tend to outperform the broad market. China market beta correlation characteristic: Lower correlation in distress periods than in upturns - Su described China as having asymmetric low-correlation characteristics that make beta itself attractive. Chinese listing requirements vs. U.S. listings: U.S. requirements are among the lowest globally - He used this to explain why lower-quality Chinese firms may list in the U.S. Chinese market development stage for quant investing: Inning 2 - He characterized the current state of quant investing in China as still early.

Pivotal Quotes: "It’s about skating to where the puck will be rather than where the puck is." — Jason Su: Explaining why he founded Rayliant to focus on China’s future opportunity rather than mature developed markets. "Everything takes twice as long and it’s three times as hard." — Jason Su: Reflecting on the biggest surprise of building his firm as an entrepreneur. "I think the regulators actually work doubly hard, not just to make sure that rules are transparent and there’s enforcement, but they’re constantly modifying rules to protect the weaker participants." — Jason Su: Describing China’s paternalistic regulatory approach and how it shapes markets.

Implications: For investors, China offers real opportunity but demands local knowledge, patience, and discipline. Performance chasing is especially dangerous, regulation is active and evolving, and simple U.S. playbooks often fail without adaptation.