Practical tools: price-implied expectations and premortems

Why do smart investors lose money? Alix Pasquet, Managing Partner of Prime Macaya Capital Management, breaks down the paradox at the heart of hedge fund investing psychology—why high IQ often hurts investors more than it helps. Drawing on decades of experience allocating to top quant funds and running capital, Alix explains how hedge fund managers fall into classic strategy mistakes, why competing against other smart people is a losing game, and how temperament, meta-rationality, and emotional intelligence determine long-term returns. He shares lessons from poker, backgammon, and behavioral finance investing, showing how overconfidence, overfitting, and complexity bias cause even the most analytical investors to underperform—and what it really takes to develop a resilient hedge fund manager mindset that consistently outperforms.We also dive into how AI in finance 2025 is changing the rules of the game. Alix argues that the rise of LLMs and financial markets automation is amplifying investor laziness and creating “fantasy stocks,” where hype replaces deep work. He reveals how algorithmic trading and AI are reshaping competition, why quant fund blowups from 2007 still hold lessons today, and how complexity and systems thinking in markets help investors avoid repeating those same errors. From overreliance on automation to cognitive bias in quant funds and artificial intelligence, Alix explains how to adapt your process—combining analog judgment, data discipline, and humility—to truly understand how hedge funds make money and how smart people keep losing it.- Why smart investors lose money and how behavioral finance explains repeated hedge-fund blow-ups- Cognitive biases in investing and how even seasoned managers misread probability and risk- Investor temperament and success: why emotional discipline matters more than IQ or pedigree- Risk management lessons from hedge funds drawn from two decades of allocation experience- Quant finance insights from studying how data access, cleaning, and market impact shape alpha- Quantitative trading psychology and what separates disciplined quants from over-fit models- Why quants lose money: the hidden behavioral alpha that algorithms can’t replicate- Market microstructure investing and how execution, liquidity, and leverage drive performance- Complexity and systems thinking in markets—how to simplify chaotic systems into tradable edges- Behavioral alpha in quant strategies: exploiting human errors embedded in data- Intelligence vs wisdom investing: when deep knowledge clouds judgment and kills returns- IQ traps in decision making that cause overconfidence and portfolio blow-ups- Intellectual arrogance in hedge funds and how meta-rationality builds long-term humility- Generative AI in markets and how narrative feedback loops distort valuations- AI amplifying investor mistakes: when automation removes human judgment- Machine learning investing: where predictive models add value—and where they fail- Data-driven investing strategies and the limits of backtesting without context- Automation in portfolio management and the danger of delegating conviction to code- Network theory in investing: building multiple networks to uncover leading indicators- Analog training vs digital distraction: why reading, reflection, and deep work still create edge- Emotional self-regulation for investors—habits, routines, and recovery to sustain performance- Lessons from poker and backgammon for investing: strategy, variance, and position sizing- Mentorship and triads networking strategy—how to create compounding social capital- How to build diverse networks for success across geography, sector, and generation- Stoicism and finance mindset: developing calm under uncertainty and volatility