Excess Returns

In this episode of Excess Returns, Jack Forehand and Matt Zeigler dig into forecast season by reviewing and synthesizing insights from 22 major Wall Street and institutional market outlooks. Rather than treating year-end forecasts as precise predictions, the conversation uses them as a framework for understanding consensus views, hidden assumptions, and where the real risks and surprises for 2026 may lie. The discussion spans macroeconomic conditions, AI-driven growth, earnings expectations, valuation risks, and the growing divergence beneath headline market performance, helping investors think more clearly about the range of outcomes ahead.

Main topics covered
• Why year-end market forecasts are still useful despite being consistently wrong on exact targets
• What consensus forecasts reveal about expectations for economic growth in 2026
• The role of artificial intelligence in driving earnings, productivity, and capital spending
• Reacceleration versus late-cycle slowdown and how forecasters are split on the outlook
• Inflation expectations, interest rates, and the likelihood of fewer Fed cuts than expected
• Fiscal policy, deficits, and the growing role of government stimulus
• Energy constraints, data centers, and the physical limits of the AI buildout
• Profit margin expansion versus revenue growth and why this matters for valuations
• S&P 500 price targets, earnings assumptions, and where optimism and caution diverge
• The dominance of the Magnificent Seven and the debate over market and earnings broadening
• Risks beneath the surface, including margin compression, valuation resets, and sector rotation
• What investors can learn by comparing the most bullish and most bearish forecasts

Timestamps
00:00 Forecast season and why reading outlooks still matters
03:00 Why precise market targets are misleading but informative
05:30 Using consensus forecasts to identify risks and surprises
08:30 AI, economic reacceleration, and productivity expectations
13:00 Recession risks, stagflation fears, and late-cycle dynamics
17:00 Inflation outlook and why it may reemerge later in the year
22:00 Fed policy, rate cuts, and rising internal dissent
26:00 Fiscal stimulus, deficits, and long-term consequences
28:00 AI infrastructure, energy constraints, and data centers
35:00 AI diffusion and real-world productivity gains
39:00 S&P 500 targets, earnings growth, and valuation assumptions
43:00 Profit margins, mean reversion, and long-term risks
47:00 Magnificent Seven earnings versus the rest of the market
52:00 Market broadening, international stocks, and diversification
56:00 Key takeaways for investors heading into 2026

In this special compilation episode of Excess Returns, we ask one revealing question to some of the most respected investors, strategists, and market thinkers in the industry:

What is one belief you hold about investing that most of your peers would disagree with?

The answers challenge conventional wisdom across macro, valuation, diversification, options, forecasting, AI, and investor behavior.

Rather than consensus, this episode highlights how great investors think differently about risk, uncertainty, and long-term outcomes.

00:06 Jim Grant – Why gold has been, is, and will remain money
02:14 Andy Constan – Why quantitative easing is always pro-growth and inflationary
03:36 Liz Ann Sonders – Why year-end market price targets are a useless exercise
04:56 Richard Bernstein – Why the stock market is ownership, not a horse race
06:33 David Giroux – Why macro investing does not create long-term alpha
08:00 Meb Faber – Why dividend investing narratives are often misunderstood
11:44 Sam Ro – When valuations actually matter and when they don’t
13:27 Jason Buck – Why belief systems in investing are often built on insecurity
15:16 Mike Green – Why markets change when metrics become targets
17:16 Jerry Parker – Why the Sharpe ratio fails for asymmetric return strategies
19:15 Chris Mayer – Why trimming great businesses often hurts long-term returns
21:14 Joseph Shaposhnik – Why a stock that has doubled may still be early
24:27 Warren Pies – Why price and technicals are essential for managing risk
25:33 Katie Stockton – Why technical analysis can stand on its own
27:17 Jim Paulsen – Why policy makers matter less than cultural and economic forces
28:41 Adam Parker – Why differentiated thinking is the only real edge versus the index
30:29 Rupert Mitchell – Why copying great investors is a mistake
31:18 Victor Haghani – Why asset allocation should be dynamic, not static
33:09 Dan Rasmussen – Why historical growth tells you almost nothing about future growth
33:45 Graeme Forster – Why you don't just need to be right 60% of the time
35:40 Shannon Saccocia – Why investors should think more like futurists than historians
36:21 Cem Karsan – Why options are not derivatives, but the true underlying
40:31 Aahan Menon – Why tariffs and macro news matter less than investors think
41:49 Andrew Beer – Why simple bets often outperform complex strategies
44:09 Bogumil Baranowski – Why successful investing requires far less work than people believe
45:55 Rick Ferri – Why advice fees and asset management fees should be separated
46:57 Cameron Dawson – Why multidisciplinary thinking is essential for investors
48:24 Mary Ann Bartels – Why blue chip dividend investing still has a place
49:40 Travis Prentice – Why turnover depends entirely on the strategy
50:24 Scott McBride – Why catalysts are overrated in value investing
50:58 Jared Dillian – Why tariffs and protectionism make economies poorer
53:35 Peter Atwater – Why shareholders are no longer the top corporate priority
54:34 Ian Cassel – Why turnover myths persist in microcap investing
55:31 Kris Sidial – Why trading psychology matters more than models
56:17 Noel Smith – Why top hedge fund returns are not the upper limit
57:09 Kai Wu – How AI will reshape investing jobs without replacing humans
01:00:49 Tim Hayes – Why markets cannot be forecast reliably
01:02:12 Doug Clinton – Why AI-powered asset management could be a multi-trillion-dollar industry

In this episode of Excess Returns, we sit down with Paul Eitelman, Global Chief Investment Strategist at Russell Investments, to unpack their 2026 outlook and the idea of a “Great Inflection Point” for markets and the economy. Paul explains why the U.S. economy may be shifting from resilience to reacceleration, how artificial intelligence is moving from hype to measurable returns, and why market leadership could finally broaden beyond the Magnificent Seven. The conversation blends macroeconomic analysis, behavioral finance, and real-world portfolio implications, offering investors a framework for thinking about growth, risk, and diversification as we head into 2026.

Main topics covered
• The cycle, valuation, and sentiment framework and how it shapes investment decisions
• Why economic growth may reaccelerate in 2026 after navigating policy headwinds
• Accelerating AI adoption and what early signs of ROI mean for productivity and profits
• The J-curve of new technologies and where AI may sit today
• Capital spending, leverage, and profitability risks among hyperscalers and large tech firms
• Energy demand, labor market impacts, and other societal risks tied to AI
• Tariffs, immigration, and uncertainty as fading or manageable economic headwinds
• Financial conditions, fiscal stimulus, and deregulation as emerging tailwinds
• The gap between hard economic data and weak consumer sentiment
• Why recession forecasts have been wrong and how to think about recession risk going forward
• Inflation dynamics, the Federal Reserve’s priorities, and the outlook for rates
• The case for market broadening beyond the Magnificent Seven
• Global diversification, small caps, international equities, and emerging markets
• Behavioral finance, investor sentiment, and staying invested through volatility
• Portfolio construction implications, including real assets and alternatives

Timestamps
00:00 Introduction and the Great Inflection Point outlook
03:00 Cycle, valuation, and sentiment investing framework
05:50 From economic resilience to potential reacceleration
07:00 AI as a transformational technology and historical parallels
09:20 Measuring returns on AI investment and productivity gains
11:00 The AI J-curve and timing of benefits
13:00 Capital intensity, leverage, and risks for big tech
15:00 Energy demand, labor markets, and AI risks
19:00 How Paul uses AI in his own research workflow
20:30 The case for economic reacceleration into 2026
21:40 Tariffs and their real economic impact
23:20 Immigration and labor supply effects
24:10 Uncertainty, confidence, and business decision-making
26:10 Financial conditions and household wealth
28:00 Fiscal stimulus and the One Big Beautiful Bill Act
29:20 Deregulation as a potential growth tailwind
30:40 Hard data versus soft data in the economy
34:10 Why recession forecasts failed
37:10 Recession risk outlook for 2026
40:30 Inflation dynamics and the Fed’s focus
43:50 Broadening market leadership beyond the Magnificent Seven
46:10 Investor sentiment, panic, and opportunity
49:00 Translating macro views into portfolio strategy
51:30 Real assets, alternatives, and diversification
54:30 Investing lessons, compounding, and staying invested

In the latest episode of Click Beta, Matt Zeigler, Dave Nadig and Cameron Dawson take a look back at 2025 and a look forward to 2026. Subscribe to Click Beta via the links below.

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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