Interview
David Harding: The Future of Quantitative Investing
- The firm relies on serial loss correlations identified over its career to challenge efficient market theory orthodoxy.
- Long-term historical analysis is prioritized to statistically validate strategy properties following drawdowns.
- External studies on long-term history are recognized as a bellwether for institutional investment trends.
- Skepticism regarding current AI efficacy persists, though the speaker acknowledges recent breakthroughs since 2014-2015.
- The current AI and machine learning landscape is characterized as 90% hype and 10% substance, with many discussed technologies existing for 30 years.
- Conflicting interests among customers, staff, regulators, the public, and shareholders are expected to occur periodically.
- Future financial markets are anticipated to continue cycles of euphoria, despair, and catastrophe.
- Portfolio construction aims to withstand extreme scenarios, such as a 35% overnight stock market drop, rather than assuming normal distribution returns.
- The business model assumes increasing industry trust in machine-based investment applications.
- Any future drawdowns are projected to be less severe than previous occurrences, reinforcing the beneficial nature of negative experiences.