E347: The $26B CIO Who Turned Superforecasting Into Alpha
How do you manage a $26 billion public fund while keeping every investment decision disciplined, every team member calibrated, and every partner accountable? In this episode, I sit down with Mark Steed, Chief Investment Officer of AZ Public Safety Personnel Retirement System, to explore how super forecasting and probabilistic thinking shape portfolio management. Mark shares how lessons from Dr. Phil Tetlock's the Good Judgment Project inform every investment decision, why intellectual humility and calibrated confidence drive better outcomes, and how simplifying portfolios into broad buckets creates flexibility and competition for capital. He also unpacks the role of co-investments, structural alpha, and first principles thinking in public markets.
Highlights:
- How PSPRS uses probabilistic forecasts and Briar Scores to track accuracy and improve decision-making
- Why intellectual humility and calibration are as important as market knowledge
- Simplifying complex portfolios into three broad buckets: capital appreciation, contractual income, and diversifying strategies
- The growing role of co-investments and capturing structural alpha with trusted partners
- Benchmarking against the S&P 500 while managing expectations for thousands of police and fire pensioners
- Distinguishing between “investing,” “allocating,” and truly “owning” assets
- Lessons from super forecasting on evaluating GPs and reducing overconfidence in a complex market environment
Guest bio:
Mark Steed is Chief Investment Officer of AZ Public Safety Personnel Retirement System, overseeing approximately $26 billion for police and fire pensions. He has implemented super forecasting and probabilistic investment methodologies at scale, transforming decision-making, portfolio construction, and team dynamics. Known for his focus on calibration, intellectual humility, and first principles thinking, Mark combines behavioral science with traditional investment rigor to deliver disciplined, long-term results.
Are you interested in sponsoring the How I Invest Podcast? Please email David Weisburd at [email protected].
We’d like to thank AlphaSense for sponsoring this episode!
Sponsor:
AlphaSense is the AI-powered market intelligence platform trusted by 85% of the S&P 100, helping investment professionals make faster, more confident, data-driven decisions. Built for hedge funds, asset allocators, private venture capital firms, and investment bankers, AlphaSense uses advanced AI and powerful search across premium proprietary content to surface the insights that matter most—before the market moves. Elevate your research and stay ahead of the competition. Visit https://www.alpha-sense.com/howiinvest/ to learn more.
Stay Connected with David Weisburd:
X/Twitter: @dweisburd LinkedIn: https://www.linkedin.com/in/dweisburd/ Weisburd Capital: https://www.weisburdcapital.com/
Stay Connected with Mark Steed:
LinkedIn:https://www.linkedin.com/in/mark-steed/
Questions or topics you want us to discuss on How I Invest? Email us at [email protected].
Disclaimer:
This podcast is for informational purposes only and does not constitute investment, financial, legal, or tax advice. Nothing in this episode should be interpreted as an offer to buy or sell any securities or to participate in any investment strategy. All opinions expressed by the host and guests are their own and do not represent the views of Weisburd Capital. Participants may hold positions or have financial interests in the companies, funds, or investments discussed. Any references to specific investments are for illustrative purposes only. Investing involves risk, including the potential loss of capital. Past performance is not indicative of future results, and any forward-looking statements are subject to risks and uncertainties. Any third-party data or opinions have not been independently verified. Listeners should conduct their own research and consult their own advisors before making any investment decisions.
Chapters
| 0:00 | |
| 6:14 | |
| 7:14 | |
| 9:20 | |
| 13:13 | |
| 14:29 | |
| 16:02 | |
| 17:27 | |
| 19:08 | |
| 22:16 | |
| 24:02 | |
| 25:58 | |
| 27:24 | |
| 29:38 | |
| 33:06 | |
| 34:54 | |
| 36:16 | |
| 38:17 |
Transcript
Loading transcript...