In this episode of Excess Returns, we sit down with Adrian Helfert of Westwood to discuss how investors should be thinking about portfolio construction in a market shaped by artificial intelligence, high levels of concentration, shifting interest rate dynamics, and evolving economic signals. The conversation covers how AI-driven capital spending is changing return profiles across markets, why traditional investing rules are breaking down, and how investors can balance growth, income, and risk in an uncertain environment. Adrian shares his framework for understanding return drivers, his views on market concentration and valuation, and how to think about diversification, macro risk, and income generation going forward.

Main topics covered
• How Westwood frames portfolio construction around capital appreciation, income, and event-driven returns
• Why AI spending is both a major opportunity and a growing existential risk for large companies
• The sustainability of market concentration and what it means for future returns
• Whether higher interest rates really hurt growth stocks the way investors expect
• How massive data center and AI capital expenditures could translate into productivity gains
• The case for market broadening beyond the Magnificent Seven
• Why traditional recession indicators have failed in recent cycles
• How inflation, labor markets, and Federal Reserve policy interact today
• Rethinking the classic 60/40 portfolio and the role of private markets
• Using covered calls and active income strategies to manage risk and generate yield

Timestamps
00:00 Introduction and near-term opportunities versus long-term risk
02:40 Capital appreciation, income, and event-driven investing framework
06:30 Have markets structurally changed to support higher returns
09:30 Intangible assets, AI, and margin expansion
10:20 The scale of AI and data center capital spending
13:00 Productivity gains and return on investment from AI
16:00 AI as both opportunity and risk for companies
19:30 Market concentration and diversification concerns
23:30 Will market leadership eventually broaden
25:30 Growth stocks, duration, and interest rates
29:30 International diversification and global investing
33:30 Why recession indicators have failed
39:00 Inflation outlook and Federal Reserve policy
46:00 Rethinking the 60/40 portfolio
53:00 Enhanced income strategies and covered calls
59:00 One investing belief most peers disagree with

In this special episode, Adam Butler and Ben Hunt join Matt Zeigler to unpack one of the most charged debates in markets and economics today: whether our official statistics still reflect lived reality. Building on Mike Green’s work and Adam Butler’s essay The Bureau of Missing Children, the conversation moves beyond the technical definition of poverty to a deeper idea of economic precarity, the growing gap between what we measure and what people actually experience. Together, they explore debt, housing, childcare, labor mobility, AI, and the erosion of meaning in economic language, while wrestling with what policy, community, and human-centered solutions might look like in a world that increasingly feels unstable.

Main topics covered

• Why the debate should focus on precarity rather than poverty

• The disconnect between inflation statistics and lived experience

• How debt, housing, childcare, and education drive economic insecurity

• The idea of a participation budget for modern family formation

• Why labor mobility has broken down since the financial crisis

• How asset prices and credit intensify risk for households

• The role of grandparents and off-balance-sheet support in the economy

• Darwin’s wedge, positional goods, and rising costs of everyday life

• The impact of AI, technocracy, and anti-human incentives

• Centralized versus decentralized solutions to today’s economic challenges

• What it means to carry the fire and preserve human-centered values

Timestamps
00:00 Introduction and the emotional roots of the precarity debate
02:00 Poverty versus precarity and what we are really measuring
06:30 Technocrats, narratives, and the limits of economic statistics
09:00 Personal experiences with precarity and debt
15:00 The Bureau of Missing Children and family formation economics
21:00 Modeling household income and participation budgets
25:50 Rising costs of childcare, housing, and everyday life
33:00 Darwin’s wedge and positional competition
36:45 Debt, housing, and labor immobility
40:00 Grandparents, unpaid care, and off-balance-sheet subsidies
46:30 How today differs from 40 or 50 years ago
49:40 Labor mobility as a lost engine of opportunity
55:00 Policy paths, mission-driven economics, and decentralization
01:11:00 Visionary leadership versus bottom-up solutions
01:15:50 Carrying the fire and preserving meaning
01:17:30 Where to follow Adam Butler and Ben Hunt

In this episode of Excess Returns, we sit down with David Wright, Head of Quantitative Investing at Pictet Asset Management, for a deep and practical conversation about how artificial intelligence and machine learning are actually being used in real-world investment strategies. Rather than focusing on hype or black-box promises, David walks through how systematic investors combine human judgment, economic intuition, and machine learning models to forecast stock returns, construct portfolios, and manage risk. The discussion covers what AI can and cannot do in investing today, how machine learning differs from traditional factor models and large language models like ChatGPT, and why interpretability and robustness still matter. This episode is a must-watch for investors interested in quantitative investing, AI-driven ETFs, and the future of systematic portfolio construction.

Main topics covered:

• What artificial intelligence and machine learning really mean in an investing context

• How machine learning models are trained to forecast relative stock returns

• The role of features, signals, and decision trees in quantitative investing

• Key differences between machine learning models and large language models like ChatGPT

• Why interpretability and stability matter more than hype in AI investing

• How human judgment and machine learning complement each other in portfolio management

• Data selection, feature engineering, and the trade-offs between traditional and alternative data

• Overfitting, data mining concerns, and how professional investors build guardrails

• Time horizons, rebalancing frequency, and transaction cost considerations

• How AI-driven strategies are implemented in diversified portfolios and ETFs

• The future of AI in investing and what it means for investors

Timestamps:
00:00 Introduction and overview of AI and machine learning in investing
03:00 Defining artificial intelligence vs machine learning in finance
05:00 How machine learning models are trained using financial data
07:00 Machine learning vs ChatGPT and large language models for stock selection
09:45 Decision trees and how machine learning makes forecasts
12:00 Choosing data inputs: traditional data vs alternative data
14:40 The role of economic intuition and explainability in quant models
18:00 Time horizons and why machine learning works better at shorter horizons
22:00 Can machine learning improve traditional factor investing
24:00 Data mining, overfitting, and model robustness
26:00 What humans do better than AI and where machines excel
30:00 Feature importance, conditioning effects, and model structure
32:00 Model retraining, stability, and long-term persistence
36:00 The future of automation and human oversight in investing
40:00 Why ChatGPT-style models struggle with portfolio construction
45:00 Portfolio construction, diversification, and ETF implementation
51:00 Rebalancing, transaction costs, and practical execution
56:00 Surprising insights from machine learning models
59:00 Closing lessons on investing and avoiding overtrading

In this episode of our new show The 100 Year Thinkers, Robert Hagstrom, Chris Mayer, Bogumil Baranowki and Matt Zeigler explain how investors get trapped by labels, abstractions, and simplistic models, and why breaking free with better mental models, language, and long-term thinking is a real edge in markets.

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Brent Kochuba takes a look behind the scenes at the options flows driving the market heading into the December options expiration and the end of 2025.

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Ed Yardeni returns to Excess Returns to break down the evolving market landscape, why he moved the Magnificent 7 to underweight, and how AI, productivity, interest rates, global markets, and sector leadership will shape the next stage of the Roaring 2020s. Ed explains why the economy has remained so resilient, what could finally trigger a true market broadening, and how investors should think about everything from tech competition to inflation, private credit risks, and Fed policy heading into 2026.

Main topics covered
• Why Ed reduced the Magnificent 7 and tech from overweight to market weight
• How extreme sector concentration affects portfolio construction
• The escalating competition inside AI and large-cap tech
• The AI CapEx boom and how it changes earnings, margins, and valuation
• Valuation considerations for tech leaders at this stage of the cycle
• Whether the Mag 7 should be compared to past tech bubbles
• How AI adoption may spread to the broader economy and boost productivity
• Economic impact of AI on jobs, wages, and long-term inflation
• Why the US economy avoided recession despite persistent warnings
• Rolling recessions vs traditional recessions and how they shape markets
• Private credit risks and whether they pose a systemic threat
• Prospects for small caps, mid caps, financials, industrials, and healthcare
• Why 2026 may finally bring true market broadening
• The outlook for international investing and emerging markets
• Ed’s S&P 500 roadmap to 7,700 next year and 10,000 by 2029
• Fed policy, rate cuts, inflation, bond vigilantes, and political pressure
• Key risks investors should monitor heading into 2026

Timestamps
00:00 Mag 7 concentration and the case for rebalancing
03:00 How Ed builds probability-based market scenarios
04:30 Why the Roaring 2020s thesis still holds
06:00 The no-show recession and economic resilience
07:00 Why he moved the Mag 7 and tech to market weight
09:30 How every company is becoming a technology company
12:20 Knowing when a successful thesis has run its course
13:30 The dominance of the US market and global diversification
15:00 Why market weight, not overweight, for tech and the Mag 7
16:00 Tech competition, AI leapfrogging, and margin pressure
18:30 The CapEx boom and valuation questions
21:00 Comparing today’s tech leaders to the 2000 era
23:00 How AI could lift productivity across the entire economy
25:00 Putting AI in historical context
27:00 How new technologies solve constraints like energy and compute
29:00 AI’s long-term impact on productivity and growth
30:00 Labor market disruption and job transition dynamics
31:20 Will AI be deflationary over time?
32:30 Technology, China, automation, and global deflation forces
33:00 Ed’s forecast for the S&P 500 through 2029
35:00 Why recession indicators failed this cycle
37:00 How liquidity facilities prevent credit crunches
39:00 Private credit risks and transparency challenges
40:45 The potential for market broadening in 2026
42:20 Takeaways from the latest Fed meeting
44:00 Should the Fed be cutting rates?
45:00 Fed independence under political pressure
47:00 Why bond vigilantes may return in 2026
48:00 International investing opportunities and ETFs
49:30 Closing thoughts and key risks ahead

In this episode of Excess Returns, we sit down with Andrew Beer to break down managed futures, hedge fund replication, diversification, and what investors can realistically expect from these alternative strategies. Andrew explains why managed futures can act like a “cloudy crystal ball,” how trend strategies capture major macro shifts, why complexity isn’t always your friend, and how advisors can communicate these concepts to clients. We also explore fees, model portfolios, allocation decisions, global macro themes, and what smart-money positioning looks like heading into 2025.

Topics Covered
What managed futures actually are and how they work
How trend strategies capture big macro shifts
Why diversification is most valuable during market stress
Why investors struggle with complexity and line-item risk
The statistical case for adding managed futures to a 60/40 portfolio
Barriers to adoption and how advisors should explain the strategy
The role of model portfolios and why slow rebalancing can hurt in regime shifts
Why Andrew prefers simplicity over complexity in managed futures
Fee sensitivity, ETFs, and how this strategy goes mainstream
Indexing, replication, and building more efficient alternatives
Why manager selection is hard in this space
The “rush to complexity” and why it often hurts returns
How hedge fund replication works and what it captures
What smart money is positioned for today across equities, rates, currencies, and commodities
Macro themes: inflation, rate cycles, the dollar, yen, and global equity opportunities
Why international equities may finally be turning
How managed futures complement – not replace – stocks and bonds
What mainstream adoption might look like over the next decade

Timestamps
00:00 Intro and why managed futures matter
02:00 Explaining managed futures in simple terms
06:18 The four major asset classes trend funds trade
10:00 Why trends form and how information reveals itself in prices
11:55 Diversification and how managed futures improve portfolios
14:00 Why investors haven’t widely adopted the strategy
17:01 Communicating the “what,” not the “how,” with clients
18:55 How model portfolios behave in regime change
21:55 How managed futures can move faster than traditional allocations
24:00 Why a simple portfolio of major markets works
26:00 Making alternatives feel less risky
28:00 Performance dispersion across managed futures ETFs
30:00 Why complexity doesn’t equal value
35:20 Fees, ETFs, and what mainstream adoption requires
38:00 The real reason for the industry’s “rush to complexity”
40:35 Should managed futures exclude equities and bonds?
43:00 Why it’s so hard to handicap what will work in advance
46:00 The human side of alternatives and advisor communication
47:00 Hedge fund replication explained
50:00 How replication identifies major themes
52:00 Why replication works only in certain strategies
53:10 What smart money positioning looks like today
55:45 Inflation, rates, the dollar, and global opportunities
58:00 The path to managed futures becoming a standard allocation
59:22 Where to find Andrew Beer online

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In the episode, we sit down with Business Breakdowns host Matt Reustle to discuss how he breaks down businesses and the common characteristics that the best businesses he has looked at share.

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