SI419: What Happens When AI Starts Building the Portfolio ft. Nick Baltas

Alan Dunne is joined by Nick Baltas to discuss a strong period for trend following and what the latest research says about adapting systematic strategies to changing markets. They examine whether volatility can help determine how quickly trend models should react, and why Nick remains skeptical of relying on regime triggers with limited statistical evidence. The conversation then turns to agentic AI and a new framework for using specialized AI agents in asset allocation. Nick explores how agents could analyze macro regimes, challenge competing portfolio approaches and help investment committees make decisions faster, while also confronting problems around look-ahead bias, reproducibility and human oversight.

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50 YEARS OF TREND FOLLOWING BOOK AND BEHIND-THE-SCENES VIDEO FOR ACCREDITED INVESTORS - CLICK HERE

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Episode TimeStamps:

00:00 - Introduction and what’s caught Nick’s attention

01:31 - The extraordinary concentration inside the S&P 500

04:12 - Why bond yields are rising around the world

08:28 - A strong month and year for trend following

12:08 - Why the bond move has happened without a volatility spike

15:12 - Can volatility make trend following more adaptive?

20:43 - What the latest research actually tells us

23:21 - How Nick uses volatility in portfolio construction

25:50 - Can investors identify trend regimes in advance?

27:04 - Why making trend models more complicated may not help

29:30 - V-shaped markets and the problem with regime triggers

32:01 - How agentic AI could reshape asset allocation

44:14 - What actually drives an AI-built portfolio?

50:03 - Why backtesting AI investment models is so difficult

55:05 - Could the same AI framework work for trend following?

58:33 - Using AI to manage competing investment objectives

01:01:22 - When will we see portfolios run by AI agents?

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