In this episode of the Financial Thought Exchange, Lotta Moberg, CFA, PhD, speaks with Stephen J. Brown, PhD, Emeritus Professor of Finance at Monash University in Australia and at the Stern School of Business at New York University, and winner of the CFA Institute Research Foundation 2025 James R. Vertin Research Award.

Brown discusses the origins of hedge funds, their role as liquidity providers, and why their performance often disappoints relative to public markets. He explains how hedge fund risk differs from traditional market risk, the limits of diversification, and why rigorous due diligence is essential. The conversation also explores his research on sensation‑seeking behavior among hedge fund managers and its implications for risk‑adjusted returns.

Related research by Stephen J. Brown:

• Why Hedge Funds

https://www.tandfonline.com/doi/full/10.2469/faj.v72.n6.6

• Sensation Seeking and Hedge Funds

https://www.jstor.org/stable/26656034

In Part 2, Francesco Fabozzi, PhD—Managing Editor of the Journal of Financial Data Science—joins host Lotta Moberg, CFA, PhD, to explore how modern NLP and large language models are reshaping investment management. Building on the technical foundations from Part 1, this episode turns to real-world applications: when to fine‑tune models versus rely on prompt engineering, how retrieval‑augmented generation (RAG) keeps models current with fast‑changing financial information, and why agentic systems are emerging as powerful tools for research automation. Fabozzi explains practical use cases ranging from sentiment‑driven return prediction to efficient knowledge‑distillation workflows, research assistants that read earnings reports, and coding agents that help back‑test investment ideas. The discussion closes with a look at where innovation is headed, including the potential of "general price transformers" for market forecasting.

This episode is essential for anyone applying AI within investment processes.

Francesco Fabozzi, PhD, Managing Editor of the Journal of Financial Data Science, joins Lotta Moberg, CFA, PhD to unpack how natural language processing matured into the powerful tool it is today. The discussion traces early finance‑focused techniques—dictionary counts, sentiment word lists, and sparse document‑term matrices, along with their limits around context and negation. Fabozzi then explains how neural networks introduced embeddings and contextual meaning, paving the way for recurrent models and eventually transformer architectures. He breaks down how self‑attention, encoder–decoder designs, and decoder‑only LLMs transformed language understanding and made large‑scale modeling feasible.

This episode lays the groundwork for understanding how modern NLP models interpret financial text. Look for Part 2, where the conversation turns to practical applications in investment management.

In the concluding episode, Lotta Moberg, CFA, PhD and Oswaldo Zapata, PhD look toward the future of quantum computing in finance. They discuss potential high‑value applications such as optimization, option pricing, machine learning, and large‑scale simulations. Zapata also highlights the cyber‑security implications of quantum technologies, including the threat of breaking RSA encryption and the urgency of adopting quantum‑safe protocols. The conversation covers industry readiness, from hedge fund research to major institutions investing in quantum capabilities, as well as the emerging need for "quantum‑quants." This episode provides a forward‑looking view of how quantum computing may reshape financial services, risk management, and professional skill sets in the decade ahead.

To read Oswaldo Zapata, PhD's chapter in AI in Asset Management, follow this link: https://rpc.cfainstitute.org/research/foundation/2025/chapter-9-quantum-computing-for-finance

Part 2 explores the technical hurdles shaping quantum computing's readiness for financial applications. Oswaldo Zapata, PhD and host Lotta Moberg, CFA, PhD discuss qubit quality, error rates, and why today's devices remain in the "noisy intermediate‑scale quantum" (NISQ) era. The episode breaks down hybrid classical‑quantum approaches, quantum‑inspired algorithms, and the complex process of encoding classical data into quantum states. Zapata explains why portfolio optimization is a promising—but still aspirational—area for quantum speedups, and how current hardware limitations constrain real‑world deployment. This conversation offers an honest assessment of where the technology stands today and what breakthroughs are still needed before quantum tools can meaningfully impact finance.

To read Oswaldo Zapata, PhD's chapter in AI in Asset Management, follow this link: https://rpc.cfainstitute.org/research/foundation/2025/chapter-9-quantum-computing-for-finance

In Part 1 of this three-part interview, host Lotta Moberg, CFA, PhD, speaks with Oswaldo Zapata, PhD, co‑founder of the Quantum Finance Boardroom and contributor to the CFA Institute Research Foundation monograph AI in Asset Management. This episode introduces the fundamentals of quantum computation, outlining how qubits, superposition, and quantum gates differ from classical computing. Zapata explains why quantum systems can process information in exponentially richer ways and discusses the challenges of building reliable qubits in real‑world laboratory environments. The conversation sets the groundwork for understanding how quantum computers may eventually tackle computational problems that classical machines cannot. This episode is ideal for finance professionals seeking a clear, accessible foundation in quantum technologies before diving into applications.

To read Oswaldo Zapata, PhD's chapter in AI in Asset Management, follow this link: https://rpc.cfainstitute.org/research/foundation/2025/chapter-9-quantum-computing-for-finance

Marcos López de Prado, PhD and Vincent Zoonekynd, PhD, of Abu Dhabi Investment Authority discuss their Research Foundation brief, Causality and Factor Investing: A Primer. They explore why many factor models fail, the risks of confounder and collider bias, and why factor investing requires a causal—not purely statistical—approach. Learn how causal graphs and theory-driven methods can improve attribution and model design. A must-watch for quantitative researchers and finance professionals seeking deeper insights into risk premia and robust factor modeling.

Larry Siegel speaks with Dr. William J. Bernstein—author, neurologist, and investment thinker—about the pillars of prosperity: property rights, scientific rationalism, capital markets, and infrastructure. They examine cultural influences on economic growth, the Henrich hypothesis on trust, and the future of globalization. Bernstein shares his philosophy on passive investing, liability-matching portfolios, and why TIPS matter for retirees. He also previews a new book with Ed McQuarrie that challenges assumptions about long-term stock returns. A deep dive into history, markets, and strategies for financial security.