Laurence B. Siegel hosts a conversation with Seda Peksevim, PhD, Founder & Managing Director of Pensión Research & Consulting and Lecturer at Sabancı University, on how pension systems must be designed differently in emerging market economies.

Dr. Peksevim explains why retirement outcomes are shaped by three interconnected challenges: behavioral biases that limit saving, unstable and risky labor income, and heightened financial market volatility. She discusses the critical role of automatic enrollment, default options, and lifecycle fund design—and why importing pension models from developed markets without adjustment can produce poor results.

The discussion also explores multipillar pension systems, behavioral and technological tools to improve participation, micro‑pension innovations, and the complexities of both accumulation and decumulation under uncertainty. The central takeaway: effective pension systems must be context‑specific, behaviorally informed, and aligned with local economic realities.

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.