Excess Returns

In this episode of Excess Returns, Professor Aswath Damodaran joins Matt Zeigler and Kai Wu for a wide-ranging conversation on valuation, portfolio construction, and how investors should think about risk, discipline, and opportunity in a market shaped by AI, market concentration, and rising uncertainty. Damodaran walks through how he builds and manages his own portfolio, why price matters more than story or quality, and how AI-driven capital spending could reshape margins and returns across the economy. The discussion blends practical investing frameworks with big-picture market insights, offering a clear look at how a valuation-driven investor navigates today’s environment.Main topics covered• How Aswath Damodaran builds a stock portfolio, including diversification, position sizing, and turnover• Why investing is about buying at the right price, not buying great companies• Using valuation frameworks to invest in young, unprofitable, and fast-growing companies• How stories and narratives fit into valuation without replacing financial discipline• Watchlists, patience, and waiting for price rather than chasing popular stocks• Sell discipline, overvaluation triggers, and avoiding emotional attachment to winners• Using probability distributions and simulations instead of single-point estimates• How company lifecycles affect growth, margins, and capital allocation decisions• Why many companies struggle as they age and how management quality shows up late in the lifecycle• AI as a capital cycle and why massive AI investment may lower margins overall• Why AI is likely to create a bubble, even if it delivers long-term economic value• Winners and losers in the AI value chain, from infrastructure to applications• Risks from AI infrastructure spending, debt, and cross-ownership structures• Why private markets may not deliver better outcomes for individual investors• How Damodaran thinks about cash, diversification, and assets uncorrelated with equities• Reentering markets after selling and avoiding the trap of staying in cash too long• Time horizon, legacy investing, and managing wealth across generationsTimestamps00:00 Investing is about price, valuation, and early thoughts on AI and market risk01:54 Personal investing philosophy and why portfolios must be investor-specific03:00 Diversification, number of holdings, and managing downside risk05:00 Valuation frameworks and buying companies at the right price06:00 Stories versus numbers and avoiding the circle of competence trap08:20 Political risk and why some sectors are hard to value08:47 Watchlists, patience, and waiting for price to meet value11:43 When and why to sell stocks as a value investor12:00 Using probability distributions and simulations in valuation15:48 Sell discipline, fund flows, and separating skill from luck18:00 Company lifecycles, aging businesses, and management discipline23:18 Apple, Meta, and contrasting approaches to AI investment24:08 AI bubbles, winner-take-all dynamics, and capital cycles27:48 Infrastructure investing, debt risk, and societal spillovers32:20 Cross-ownership risks and AI ecosystem fragility35:00 AI’s impact on profit margins and competition39:41 Where AI value may accrue over time44:38 AI tools, valuation bots, and the rise of investment scams49:17 Private markets, alternatives, and cost structures53:05 Cash, collectibles, and diversification beyond equities56:33 Reentering markets after selling and avoiding market timing traps58:35 Time horizon, legacy investing, and generational wealth

In this episode of Excess Returns, we welcome back Liz Ann Sonders to discuss the evolving market and economic landscape heading into 2026. The conversation focuses on why this cycle feels fundamentally different, how instability rather than uncertainty is shaping investor behavior, and what that means for inflation, the labor market, Federal Reserve policy, and equity markets. Liz Ann breaks down the growing bifurcation across the economy and markets, the shift away from the Great Moderation era, and how investors should think about diversification, earnings, valuations, and AI-driven capital spending in a more volatile and fragmented environment.Main topics covered• Why today’s environment is better described as unstable rather than uncertain• The K-shaped economy and growing bifurcation across consumers, sectors, and markets• Inflation dynamics and why 2 percent may now be a floor rather than a ceiling• How deglobalization, supply chains, and tariffs are changing the inflation regime• The shifting relationship between stocks and bonds• Hard data versus soft data and what sentiment is really telling us• The labor market’s headwinds and tailwinds, including immigration and hiring trends• AI’s impact on productivity, jobs, and capital spending• The AI capex boom and how it differs from the late 1990s tech cycle• Earnings growth, valuation compression, and market broadening• Rolling recessions versus traditional economic downturns• Federal Reserve challenges under a conflicted dual mandate• Why factor-based investing matters more than sector or style callsTimestamps00:00 Introduction and why this cycle feels different02:00 Uncertainty versus instability in markets03:30 The K-shaped economy and market bifurcation07:00 Market broadening, small caps, and diversification09:00 Inflation measurement challenges and data reliability12:00 Why inflation may stay above 2 percent15:00 Stock and bond correlations across cycles17:30 Labor market crosscurrents and immigration effects20:45 AI, productivity, and entry-level job pressures24:30 Sentiment versus fundamentals in markets27:30 Retail trading, behavior, and market psychology31:00 Rolling recessions and post-pandemic distortions38:00 Technology, cyclicality, and sector rotation40:30 The Fed’s policy dilemma and internal disagreements45:00 AI capital spending and comparisons to the dot-com era51:00 Earnings growth versus valuation expansion55:00 Factors, GARP, and portfolio positioning for 2026

This episode of Excess Returns features a wide ranging conversation with Grant Williams on what he calls the hundred year pivot. Grant explains why today’s environment feels fundamentally different from the last several decades, why long held investing assumptions may no longer apply, and how declining trust in institutions, money, and markets is reshaping the global financial system. Drawing on history, macroeconomics, and decades of market experience, the discussion explores what this transition means for investors trying to navigate a world defined by uncertainty, volatility, and structural change.Main topics covered• What the hundred year pivot means and why it represents a once in a generation shift• The Fourth Turning framework and how it connects financial crises, politics, and social change• Why buy the dip worked for decades and why it may fail in the years ahead• The erosion of trust in institutions and its impact on markets and money• The financial crisis, sanctions, and the freezing of sovereign assets as turning points• The role of the dollar, gold, and central banks in a changing monetary system• Lessons from history including Bretton Woods and the Suez crisis• Why commodities and real assets matter in a world of deglobalization and reshoring• How artificial intelligence fits into the current investment cycle and capital allocation boom• Portfolio construction and behavioral challenges in a higher volatility environmentTimestamps00:00 The hundred year pivot and why this cycle is different01:30 Defining the Fourth Turning and historical cycles07:40 The financial crisis as the start of institutional breakdown11:00 Sanctions, sovereign assets, and the end of unquestioned trust in the dollar18:20 Historical parallels from Bretton Woods and the Suez crisis24:50 What could trigger a broader monetary reset28:50 Energy, geopolitics, and shifting global alliances35:00 Commodities, real assets, and rebuilding supply chains42:40 Artificial intelligence, capital cycles, and uncertainty52:30 Portfolio construction, behavior, and risk tolerance59:50 Where to follow Grant Williams and his work

In this episode of Excess Returns, we dive deep into one of the most pressing investing debates today: how to think about valuations, profit margins, and artificial intelligence in a market that feels both expensive and transformative. Sam Ro joins Matt Zeigler and Kai Wu for a wide-ranging conversation that explores whether traditional valuation tools still matter, how AI is reshaping corporate economics, and why history suggests investors should be cautious about bubble narratives even when enthusiasm runs high. From profit margins and capital intensity to the future of the Magnificent Seven, this episode focuses on how long-term investors can frame uncertainty without relying on false precision or short-term market calls.Timestamps00:00 Valuations, bubbles, and why timing markets is so hard01:41 Do valuations still matter for investors05:58 S&P 500 valuation levels versus history09:30 Profit margins and why mean reversion has not shown up yet14:39 Household finances, pricing power, and consumer resilience15:47 AI, productivity, and the limits of forecasting economic impact19:15 Valuations adjusted for structurally higher profit margins21:15 Tech multiples, growth expectations, and PEG ratios24:07 Are we in an AI bubble and why that question may not help29:14 Lessons from past bubbles and irrational exuberance30:14 How transformative AI could be compared to past innovations35:20 Massive AI capital spending and the risk of overbuild39:42 Who captures value in AI: builders versus users46:39 Revenue per worker and productivity trends48:00 Dispersion inside the Magnificent Seven51:34 Big tech shifting from asset-light to asset-heavy models59:53 Turnover among top companies over time01:01:10 Why Wall Street price targets miss the point01:04:30 Presidential cycles and market returns01:06:28 Fund manager surveys and why popular risks are often lagging indicatorsTopics coveredHow investors should think about valuations over long time horizonsWhy elevated profit margins may be more structural than cyclicalThe role of AI in productivity, earnings, and competitive dynamicsBubble psychology and lessons from the dot-com eraCapital intensity, overinvestment, and the risk of write-downsWhy AI infrastructure builders may not capture most of the valueWhat dispersion within the Magnificent Seven signals for marketsWhy broad diversification still matters in a rapidly changing market

In this episode of Excess Returns, Katie Stockton of Fairlead Strategies joins Matt Zeigler and Justin Carbonneau to walk through her technical outlook for markets as we head into 2026. The conversation focuses on trend analysis, momentum, volatility, and risk management across U.S. equities, sectors, international markets, and alternative assets. Rather than making predictions, Katie explains how she reacts to price, confirms signals, and uses a disciplined technical process to identify opportunities and manage downside risk in changing market environments.Main topics coveredMarket trend outlook for U.S. equities heading into 2026Why long-term trends remain constructive despite rising short-term risksHow to think about volatility, consolidation, and corrective phasesWhat loss of momentum in late 2025 signals for near-term positioningHow to use triangle formations, support, and resistance levelsUnderstanding DeMark indicators, MACD, and stochastic signalsLeadership shifts within large-cap technology and the Mag 7Growth versus value dynamics across market capsSmall caps, market breadth, and participation signalsSector rotation insights including technology, healthcare, financials, energy, utilities, and real estateHow sentiment indicators like fear and greed fit into a broader processGold, silver, and precious metals trends and volatilityBitcoin and crypto from a technical perspectiveThe U.S. dollar, yields, and global market implicationsInternational and emerging market opportunitiesHow the Fairlead Tactical Sector ETF is constructed and used in portfoliosWhere a tactical, risk-managed strategy can fit within asset allocationTimestamps00:00 Market setup and trend perspective for 202601:25 Long-term uptrend versus short-term risk04:16 Momentum loss and near-term caution06:00 Nasdaq 100 triangle and volatility setup07:45 Ichimoku clouds and trend confirmation11:01 Using consolidation and support levels13:05 Tech leadership and relative strength shifts18:30 Small caps, breadth, and market participation21:01 Growth versus value across market caps23:00 Market breadth and advance-decline signals24:13 Sentiment, fear and greed, and retests30:00 Breakouts, catalysts, and confirmation32:00 Sector rotation overview35:00 Energy, real estate, and rate-sensitive sectors39:10 Fairlead Tactical Sector ETF strategy45:00 International and emerging markets47:36 Gold, silver, and precious metals51:04 U.S. dollar and currency trends54:00 Bitcoin and crypto technical outlook57:12 Key indicators to watch going forward59:07 Long-term takeaways for investors

Subscribe to the Jim Paulsen Show on Apple Podcasts⁠⁠https://podcasts.apple.com/us/podcast/the-jim-paulsen-show/id1828054999⁠⁠Subscribe on Spotify⁠⁠https://open.spotify.com/show/3QaBDVGuBZ3cZfFZ4mqPFc⁠⁠In this episode of the Jim Paulsen Show, Jim Paulsen joins Jack Forehand and Justin Carbonneau to break down what the economy and markets may really be signaling beneath the headline numbers. Drawing from his recent outlook and long history studying market cycles, Jim explains why growth may be weaker than it appears, how policy lags are shaping the outlook, and why today’s market looks very different from past late-cycle environments. The conversation explores the divide between the “new era” economy and the rest of the market, what that means for investors in 2026, and where opportunities may be emerging as monetary and fiscal policy begin to shift.Topics covered in this episode• Why headline GDP growth may be overstating the true strength of the economy• How trade distortions are affecting recent GDP data• The concept of a “no-shaped economy” and the divide between new era and old era businesses• Labor market signals that suggest economic sluggishness beneath the surface• Why this may be one of the most disliked bull markets in history• The role of policy lags and why easing could matter more than investors expect• How market concentration has shaped returns over the last several years• Warning signs emerging within the technology sector• The relationship between corporate cash levels, R&D spending, and tech leadership• Why market breadth and old era sectors may become more important going forward• Thoughts on bonds, stocks, commodities, gold, and portfolio positioning• Why international and emerging markets could benefit from a weaker dollar• How investors might think about diversification in an unusual market cycleTimestamps00:00 Introduction and key themes from Jim’s outlook03:00 Why the economy may be weaker than GDP headlines suggest06:00 Labor market signals and recession-like dynamics12:00 Policy lags, the Fed, and why growth could soften further15:00 Market performance after multiple strong years18:00 The no-shaped economy and the split between new era and old era24:00 Strange market signals at all-time highs27:00 Valuations, sentiment, and why pessimism matters29:00 Fed easing expectations and consensus forecasts35:00 Warning signs for technology stocks42:00 Corporate cash, R&D spending, and tech leadership risks47:00 Portfolio construction and asset allocation thinking55:00 Final thoughts on opportunities and risks ahead

In this wide-ranging conversation, Gautam Baid joins Excess Returns to discuss the principles that shaped his investing philosophy, the lessons learned through bear markets, and why compounding, patience, and quality matter far more than forecasts or short-term performance. Drawing from his books The Joys of Compounding and The Making of a Value Investor, Baid shares a deeply reflective framework for long-term investing, portfolio construction, behavioral discipline, and global diversification, with insights spanning Indian and US markets, liquidity cycles, AI, and investor psychology.Main topics covered• The asymmetric power of compounding and why being wrong half the time can still lead to exceptional long-term returns• Why patience, temperament, and behavior matter more than analytical precision in investing• The role of journaling in improving decision-making and avoiding repeated behavioral mistakes• How investor sentiment reveals itself through IPO markets and portfolio quality late in bull cycles• Why long-term investing requires continuous monitoring rather than buy-and-forget complacency• Letting winners run, cutting losers, and understanding power-law outcomes in stock markets• Liquidity cycles and how they drive market returns in both India and the United States• How bear markets reshape investing philosophy toward resilience, quality, and diversification• When averaging down makes sense and when it is dangerous• The differences between Indian and US equity markets, valuations, and governance• Why home country bias can be a major risk for US-based investors• AI, productivity, profitability, and where future market winners may emerge beyond mega-cap tech• Why passion for investing matters more than money in sustaining long-term successTimestamps00:00 Introduction and the asymmetric nature of compounding01:00 Gautam Baid’s investing background and books03:00 The importance of journaling and learning through bear markets06:00 Investor sentiment, IPOs, and late-cycle market behavior10:20 Long-term investing versus complacency and monitoring risk14:15 Convex upside, concave downside, and letting winners run18:30 Liquidity cycles and lessons from Stan Druckenmiller22:45 Identifying market bottoms and the anatomy of bull and bear markets28:00 Averaging down, quality, and risk management30:30 How bear markets change investor psychology and strategy33:00 Patience, management quality, and long-term optionality36:15 Mr. Market, price signals, and market intelligence39:00 The Federal Reserve, inflation, and asset price dynamics44:00 Understanding the Indian equity market and valuation structure46:45 Why global diversification matters for US investors50:30 AI, margins, and the future of value investing53:00 Passion, purpose, and the psychology of long-term investing54:30 The single most note investors should learn

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 forecastsTimestamps00:00 Forecast season and why reading outlooks still matters03:00 Why precise market targets are misleading but informative05:30 Using consensus forecasts to identify risks and surprises08:30 AI, economic reacceleration, and productivity expectations13:00 Recession risks, stagflation fears, and late-cycle dynamics17:00 Inflation outlook and why it may reemerge later in the year22:00 Fed policy, rate cuts, and rising internal dissent26:00 Fiscal stimulus, deficits, and long-term consequences28:00 AI infrastructure, energy constraints, and data centers35:00 AI diffusion and real-world productivity gains39:00 S&P 500 targets, earnings growth, and valuation assumptions43:00 Profit margins, mean reversion, and long-term risks47:00 Magnificent Seven earnings versus the rest of the market52:00 Market broadening, international stocks, and diversification56:00 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 money02:14 Andy Constan – Why quantitative easing is always pro-growth and inflationary03:36 Liz Ann Sonders – Why year-end market price targets are a useless exercise04:56 Richard Bernstein – Why the stock market is ownership, not a horse race06:33 David Giroux – Why macro investing does not create long-term alpha08:00 Meb Faber – Why dividend investing narratives are often misunderstood11:44 Sam Ro – When valuations actually matter and when they don’t13:27 Jason Buck – Why belief systems in investing are often built on insecurity15:16 Mike Green – Why markets change when metrics become targets17:16 Jerry Parker – Why the Sharpe ratio fails for asymmetric return strategies19:15 Chris Mayer – Why trimming great businesses often hurts long-term returns21:14 Joseph Shaposhnik – Why a stock that has doubled may still be early24:27 Warren Pies – Why price and technicals are essential for managing risk25:33 Katie Stockton – Why technical analysis can stand on its own27:17 Jim Paulsen – Why policy makers matter less than cultural and economic forces28:41 Adam Parker – Why differentiated thinking is the only real edge versus the index30:29 Rupert Mitchell – Why copying great investors is a mistake31:18 Victor Haghani – Why asset allocation should be dynamic, not static33:09 Dan Rasmussen – Why historical growth tells you almost nothing about future growth33:45 Graeme Forster – Why you don't just need to be right 60% of the time35:40 Shannon Saccocia – Why investors should think more like futurists than historians36:21 Cem Karsan – Why options are not derivatives, but the true underlying40:31 Aahan Menon – Why tariffs and macro news matter less than investors think41:49 Andrew Beer – Why simple bets often outperform complex strategies44:09 Bogumil Baranowski – Why successful investing requires far less work than people believe45:55 Rick Ferri – Why advice fees and asset management fees should be separated46:57 Cameron Dawson – Why multidisciplinary thinking is essential for investors48:24 Mary Ann Bartels – Why blue chip dividend investing still has a place49:40 Travis Prentice – Why turnover depends entirely on the strategy50:24 Scott McBride – Why catalysts are overrated in value investing50:58 Jared Dillian – Why tariffs and protectionism make economies poorer53:35 Peter Atwater – Why shareholders are no longer the top corporate priority54:34 Ian Cassel – Why turnover myths persist in microcap investing55:31 Kris Sidial – Why trading psychology matters more than models56:17 Noel Smith – Why top hedge fund returns are not the upper limit57:09 Kai Wu – How AI will reshape investing jobs without replacing humans01:00:49 Tim Hayes – Why markets cannot be forecast reliably01:02:12 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 alternativesTimestamps00:00 Introduction and the Great Inflection Point outlook03:00 Cycle, valuation, and sentiment investing framework05:50 From economic resilience to potential reacceleration07:00 AI as a transformational technology and historical parallels09:20 Measuring returns on AI investment and productivity gains11:00 The AI J-curve and timing of benefits13:00 Capital intensity, leverage, and risks for big tech15:00 Energy demand, labor markets, and AI risks19:00 How Paul uses AI in his own research workflow20:30 The case for economic reacceleration into 202621:40 Tariffs and their real economic impact23:20 Immigration and labor supply effects24:10 Uncertainty, confidence, and business decision-making26:10 Financial conditions and household wealth28:00 Fiscal stimulus and the One Big Beautiful Bill Act29:20 Deregulation as a potential growth tailwind30:40 Hard data versus soft data in the economy34:10 Why recession forecasts failed37:10 Recession risk outlook for 202640:30 Inflation dynamics and the Fed’s focus43:50 Broadening market leadership beyond the Magnificent Seven46:10 Investor sentiment, panic, and opportunity49:00 Translating macro views into portfolio strategy51:30 Real assets, alternatives, and diversification54:30 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. Follow Click Beta:Spotify⁠https://open.spotify.com/show/0u1fxie4C4vHXIJPUMhvUs⁠Apple Podcasts⁠https://podcasts.apple.com/ky/podcast/click-beta/id1793929457⁠YouTube:⁠https://www.youtube.com/excessreturns

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 yieldTimestamps00:00 Introduction and near-term opportunities versus long-term risk02:40 Capital appreciation, income, and event-driven investing framework06:30 Have markets structurally changed to support higher returns09:30 Intangible assets, AI, and margin expansion10:20 The scale of AI and data center capital spending13:00 Productivity gains and return on investment from AI16:00 AI as both opportunity and risk for companies19:30 Market concentration and diversification concerns23:30 Will market leadership eventually broaden25:30 Growth stocks, duration, and interest rates29:30 International diversification and global investing33:30 Why recession indicators have failed39:00 Inflation outlook and Federal Reserve policy46:00 Rethinking the 60/40 portfolio53:00 Enhanced income strategies and covered calls59:00 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 coveredWhy the debate should focus on precarity rather than povertyThe disconnect between inflation statistics and lived experienceHow debt, housing, childcare, and education drive economic insecurityThe idea of a participation budget for modern family formationWhy labor mobility has broken down since the financial crisisHow asset prices and credit intensify risk for householdsThe role of grandparents and off-balance-sheet support in the economyDarwin’s wedge, positional goods, and rising costs of everyday lifeThe impact of AI, technocracy, and anti-human incentivesCentralized versus decentralized solutions to today’s economic challengesWhat it means to carry the fire and preserve human-centered valuesTimestamps00:00 Introduction and the emotional roots of the precarity debate02:00 Poverty versus precarity and what we are really measuring06:30 Technocrats, narratives, and the limits of economic statistics09:00 Personal experiences with precarity and debt15:00 The Bureau of Missing Children and family formation economics21:00 Modeling household income and participation budgets25:50 Rising costs of childcare, housing, and everyday life33:00 Darwin’s wedge and positional competition36:45 Debt, housing, and labor immobility40:00 Grandparents, unpaid care, and off-balance-sheet subsidies46:30 How today differs from 40 or 50 years ago49:40 Labor mobility as a lost engine of opportunity55:00 Policy paths, mission-driven economics, and decentralization01:11:00 Visionary leadership versus bottom-up solutions01:15:50 Carrying the fire and preserving meaning01:17:30 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 contextHow machine learning models are trained to forecast relative stock returnsThe role of features, signals, and decision trees in quantitative investingKey differences between machine learning models and large language models like ChatGPTWhy interpretability and stability matter more than hype in AI investingHow human judgment and machine learning complement each other in portfolio managementData selection, feature engineering, and the trade-offs between traditional and alternative dataOverfitting, data mining concerns, and how professional investors build guardrailsTime horizons, rebalancing frequency, and transaction cost considerationsHow AI-driven strategies are implemented in diversified portfolios and ETFsThe future of AI in investing and what it means for investorsTimestamps:00:00 Introduction and overview of AI and machine learning in investing03:00 Defining artificial intelligence vs machine learning in finance05:00 How machine learning models are trained using financial data07:00 Machine learning vs ChatGPT and large language models for stock selection09:45 Decision trees and how machine learning makes forecasts12:00 Choosing data inputs: traditional data vs alternative data14:40 The role of economic intuition and explainability in quant models18:00 Time horizons and why machine learning works better at shorter horizons22:00 Can machine learning improve traditional factor investing24:00 Data mining, overfitting, and model robustness26:00 What humans do better than AI and where machines excel30:00 Feature importance, conditioning effects, and model structure32:00 Model retraining, stability, and long-term persistence36:00 The future of automation and human oversight in investing40:00 Why ChatGPT-style models struggle with portfolio construction45:00 Portfolio construction, diversification, and ETF implementation51:00 Rebalancing, transaction costs, and practical execution56:00 Surprising insights from machine learning models59:00 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.Subscribe on Spotify⁠⁠https://open.spotify.com/show/5IsVVM27KWP6SUW6KN2ife⁠⁠Subscribe on Apple Podcasts⁠⁠https://podcasts.apple.com/us/podcast/the-100-year-thinkers-long-term-compounding-in-a-short-term-world/id1845466003⁠⁠Subscribe on YouTube⁠⁠https://youtube.com/@excessreturns⁠

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. Subscribe on Spotify⁠https://open.spotify.com/show/4KR2YVJqk2lnVETMKDavJf⁠Subscribe on Apple Podcasts⁠https://podcasts.apple.com/us/podcast/the-opex-effect/id1711880009⁠Subscribe on YouTube⁠https://www.youtube.com/channel/UCPYvx_y92dvI1PSdiho0ALw

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 2026Timestamps00:00 Mag 7 concentration and the case for rebalancing03:00 How Ed builds probability-based market scenarios04:30 Why the Roaring 2020s thesis still holds06:00 The no-show recession and economic resilience07:00 Why he moved the Mag 7 and tech to market weight09:30 How every company is becoming a technology company12:20 Knowing when a successful thesis has run its course13:30 The dominance of the US market and global diversification15:00 Why market weight, not overweight, for tech and the Mag 716:00 Tech competition, AI leapfrogging, and margin pressure18:30 The CapEx boom and valuation questions21:00 Comparing today’s tech leaders to the 2000 era23:00 How AI could lift productivity across the entire economy25:00 Putting AI in historical context27:00 How new technologies solve constraints like energy and compute29:00 AI’s long-term impact on productivity and growth30:00 Labor market disruption and job transition dynamics31:20 Will AI be deflationary over time?32:30 Technology, China, automation, and global deflation forces33:00 Ed’s forecast for the S&P 500 through 202935:00 Why recession indicators failed this cycle37:00 How liquidity facilities prevent credit crunches39:00 Private credit risks and transparency challenges40:45 The potential for market broadening in 202642:20 Takeaways from the latest Fed meeting44:00 Should the Fed be cutting rates?45:00 Fed independence under political pressure47:00 Why bond vigilantes may return in 202648:00 International investing opportunities and ETFs49:30 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 CoveredWhat managed futures actually are and how they workHow trend strategies capture big macro shiftsWhy diversification is most valuable during market stressWhy investors struggle with complexity and line-item riskThe statistical case for adding managed futures to a 60/40 portfolioBarriers to adoption and how advisors should explain the strategyThe role of model portfolios and why slow rebalancing can hurt in regime shiftsWhy Andrew prefers simplicity over complexity in managed futuresFee sensitivity, ETFs, and how this strategy goes mainstreamIndexing, replication, and building more efficient alternativesWhy manager selection is hard in this spaceThe “rush to complexity” and why it often hurts returnsHow hedge fund replication works and what it capturesWhat smart money is positioned for today across equities, rates, currencies, and commoditiesMacro themes: inflation, rate cycles, the dollar, yen, and global equity opportunitiesWhy international equities may finally be turningHow managed futures complement – not replace – stocks and bondsWhat mainstream adoption might look like over the next decadeTimestamps00:00 Intro and why managed futures matter02:00 Explaining managed futures in simple terms06:18 The four major asset classes trend funds trade10:00 Why trends form and how information reveals itself in prices11:55 Diversification and how managed futures improve portfolios14:00 Why investors haven’t widely adopted the strategy17:01 Communicating the “what,” not the “how,” with clients18:55 How model portfolios behave in regime change21:55 How managed futures can move faster than traditional allocations24:00 Why a simple portfolio of major markets works26:00 Making alternatives feel less risky28:00 Performance dispersion across managed futures ETFs30:00 Why complexity doesn’t equal value35:20 Fees, ETFs, and what mainstream adoption requires38:00 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 advance46:00 The human side of alternatives and advisor communication47:00 Hedge fund replication explained50:00 How replication identifies major themes52:00 Why replication works only in certain strategies53:10 What smart money positioning looks like today55:45 Inflation, rates, the dollar, and global opportunities58:00 The path to managed futures becoming a standard allocation59:22 Where to find Andrew Beer online

We are including this episode from our separate show Teach Me Like I'm Five in the Excess Returns feed. If you would like to continue receiving new episodes, subscribe using the links below.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. Subscribe on Spotifyhttps://open.spotify.com/show/7zu6lFpPohoPKhcu0Er9kBSubscribe on Apple Podcastshttps://podcasts.apple.com/hr/podcast/teach-me-like-im-five-investing-concepts-made-simple/id1815975642

In this episode of Excess Returns, Graeme Forster of Orbis joins us to discuss two major research papers: Six Courageous Questions for 2026 and Sunrise on Venus. We explore how long-running global trends may be reversing, what that means for U.S. dominance, the future of international and emerging markets, the risks and opportunities created by AI and massive CapEx spending, the dollar’s shifting role, and how investors should think about valuation, humility, and navigating a world where the economic “water” is changing. This conversation is packed with global macro insight, long-term investing lessons, and practical frameworks for building more resilient portfolios. Topics Covered:• Why long-term market “water” becomes invisible to investors• Self-reinforcing global cycles and how China’s WTO entry reshaped the world• Signs the 25-year U.S. outperformance cycle may be breaking• How tariffs, political shifts, and corporate reforms change the global landscape• Why international and emerging markets may now offer better expected returns• Why U.S. large caps are not the entire story of American exceptionalism• How to think about valuation, margins, and discounted cash flow models across markets• The AI boom, bubbles, capital cycles, and asymmetric outcomes• How AI CapEx constraints influence winners and losers• The shifting role of the U.S. dollar and why market shocks may behave differently• Maslow’s hierarchy, needs vs. wants, and the return of state-driven capital investment• Deglobalization, reshoring, and the national-security lens for investing• How to evaluate China and Taiwan inside emerging markets• Why humility is an investor’s greatest edgeTimestamps:00:00 Introduction01:02 Why Orbis wrote Six Courageous Questions for 202603:44 The David Foster Wallace “water” analogy and investing06:12 How a 25-year self-reinforcing cycle powered U.S. outperformance10:12 Signs the cycle may be breaking12:00 Corporate reform and opportunity in Asia13:55 Why active share, benchmarking, and incentives distort investor behavior17:31 Decomposing S&P 500 returns: margins, valuations, fundamentals20:20 Expected returns inside and outside the U.S.22:34 Why international stocks offer richer opportunity sets24:25 Currency implications and weakening dollar dynamics26:18 American exceptionalism beyond the top 10 mega caps28:49 Where Orbis is finding value today30:25 Biotech, healthcare, and post-COVID dislocation31:05 How Orbis thinks about valuation in an intangible-heavy world32:09 Is AI a bubble or the beginning of something bigger?34:30 Game theory of AI CapEx and right-tail outcomes36:00 CapEx cycles, history, and who benefits38:00 Indirect AI beneficiaries and the SK Square example40:35 Maslow’s hierarchy and the shift from wants to needs42:32 Deglobalization, national security, and domestic reinvestment44:00 Capital returning to home markets and strategic industries46:00 Can anything reverse these structural trends?48:00 Balancing bottom-up investing with macro awareness49:45 The deeper risk in emerging markets: owning vs. avoiding51:00 Valuation still matters for long-term returns52:29 Corporate behavior, dividends, and re-rating cycles53:52 How Orbis views China vs. bottom-up opportunity55:34 Why great investors must be right 90–95% of the time in decision quality58:00 One lesson Graeme would teach the average investor