Excess Returns

Excess Returns
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Excess Returns is dedicated to making you a better long-term investor and making complex investing topics understandable. Join Jack Forehand, Justin Carbonneau and Matt Zeigler as they sit down with some of the most interesting names in finance to discuss topics like macroeconomics, value investing, factor investing, and more. Subscribe to learn along with us.

  1. 7 HR AGO

    The Funding Test Ahead | Andy Constan on AI, War Risk and the Shift Abroad

    In this episode of Excess Returns, we welcome back Andy Constan of Damped Spring Advisors for a wide-ranging discussion on geopolitical risk, AI and productivity, capital flows, credit markets, fiscal policy, and the shift from US to international equities. Andy walks through the framework he uses to evaluate uncertainty, from wars and geopolitical shocks to the long-term implications of artificial intelligence, and explains why capital markets and funding conditions may matter more than bold narratives. We also explore growth, inflation, Fed policy, and the structural case for global diversification in today’s macro environment. Main topics covered A practical framework for analyzing geopolitical shocks, including red flags, green flags, and how to evaluate information quality during times of uncertainty How markets are pricing the current conflict with Iran across oil, equities, bonds, gold, and volatility Why historical market performance after wars may offer limited predictive value due to small sample sizes How to think about AI from a macro perspective, including GDP growth versus GDP share and who ultimately captures the gains The capital markets implications of massive AI-related capex and whether equity and credit markets can fund current spending plans Growth, inflation, and the Fed: how fiscal stimulus, wealth effects, QT, and labor market trends are shaping the current macro backdrop Why Andy has shifted away from US assets toward international markets, including the role of bond yields and global risk parity A critical look at the Trump accounts proposal and the broader issue of fiscal deficits and capital allocation The key risks Andy is watching over the next three to six months, especially around credit markets and funding conditions Timestamps 00:00 Introduction and overview of discussion topics01:01 Framework for evaluating geopolitical shocks and information quality11:46 Market reaction to the Iran conflict and asset pricing implications23:00 Why historical war data may not be reliable for market forecasting27:03 How to analyze AI’s impact on productivity and economic growth37:00 AI capex, credit markets, and funding risks42:24 Growth, inflation, and Fed policy in the current cycle49:20 The case for international equities over US markets56:20 Trump accounts, fiscal policy, and capital allocation01:02:23 What Andy is watching most closely in the months ahead

    1h 4m

  2. 2 DAYS AGO

    Is AI Replacing Workers Faster Than We Think? | We Break Down the Viral AI Doom Loop Article

    In this episode, Jack Forehand and Kai Wu break down the viral “AI doom loop” article that sparked debate across Wall Street, Silicon Valley, and even the Federal Reserve. They walk through the core thesis that artificial intelligence could trigger a non-cyclical economic disruption, separating signal from noise and exploring what it could mean for software stocks, labor markets, productivity, wealth inequality, and long-term investing. Rather than reacting emotionally, they analyze the mechanics step by step, asking whether AI is more likely to replace workers or amplify them, how fast adoption can realistically happen, and what investors should be watching right now. Main topics covered: The core thesis behind the AI doom loop scenario and why it went viral Is AI a substitute for human labor or a productivity multiplier People times productivity as a framework for understanding economic growth Why we are not yet seeing major AI disruption in labor or productivity data Software stocks, margin compression, and the risk to SaaS business models The Jevons Paradox and whether lower costs could expand demand instead of destroy it Why incumbents with strong intangible moats may survive AI disruption The difference between technological capability and real world adoption speed Compute, energy, and token costs as natural limits on AI expansion The feedback loop argument and whether AI could cause a demand shock Creative destruction and the difficulty of forecasting new job creation AI, high income knowledge workers, and the risk to consumer spending Wealth inequality, capital versus labor, and policy responses like UBI Why investors can be bullish on AI technology but cautious on markets How to think about short term disruption versus long term abundance Timestamps: 00:00 Introduction and the AI doom loop thesis 02:15 Why the article triggered a market reaction 06:00 People times productivity and economic growth 09:00 AI and disruption in software stocks 15:00 Jevons Paradox and expanding total demand 19:00 AI agents, frictionless commerce, and price competition 26:00 Adoption speed versus technology speed 28:00 Compute constraints and natural governors on AI growth 31:00 The non cyclical disruption feedback loop 33:00 Creative destruction and new job formation 38:00 General purpose technology and broad economic exposure 44:00 Replacement versus augmentation of workers 48:00 Token costs, enterprise AI spending, and labor tradeoffs 51:00 High income job risk and inequality concerns

    1h 1m

  3. 3 DAYS AGO

    The AI Panic Trade | What the Viral Doomsday AI Article Means for Markets

    Follow Last Call on Spotify⁠ ⁠Follow Last Call on Apple Podcasts In this episode of Last Call, Jack Forehand and Matt Zeigler look past the headlines to unpack what really moved markets this month. From the viral AI end of times scenario that sparked responses from Citadel, Fed Governor Waller, and Jeremy Siegel, to the growing stress in private credit and the rotation out of US mega cap stocks, this is a different kind of market wrap. Instead of recapping what the S and P 500 did, we explore what investors are actually doing with their money, how narratives shape positioning, and what the data says about whether this time is different. Featuring Brent Kochuba of SpotGamma, Ben Hunt of Epsilon Theory, Rupert Mitchell of Blind Squirrel Macro, and Meb Faber of The Idea Farm, this episode dives into AI, software stocks, options flows, credit cycles, global equity markets, gold, and the power of base rates in investing. Main topics covered: The viral AI bear case scenario and why a fictional narrative moved real markets How investors should think in probabilities, bull cases, base cases, and bear cases What options pricing and put call ratios reveal about real fear versus social media fear The state of software stocks and whether extreme bearishness may have marked a short term bottom Private credit stress, rising default risks, and why every credit cycle ends when lenders say no more An on the ground anecdote from San Francisco illustrating how refinancing risk is playing out in real time The rotation from US mega caps into international stocks and why fiscal spending matters for equity markets Gold and gold miners as potential beneficiaries of global liquidity and currency shifts Why base rates matter when evaluating explosive AI revenue forecasts Historical lessons from the Nifty Fifty, Japan’s bubble, the dot com era, and other periods when investors believed this time is different Portfolio construction tools including diversification, rebalancing, and trend following in bubble environments Timestamps: 00:00 Introduction and the AI end of times narrative02:16 Why investors are responding to fiction and what we can learn from it08:00 Brent Kochuba on options flows and software stock positioning13:00 Has extreme bearishness in software marked a bottom19:55 Ben Hunt on private credit and the boom bust cycle27:00 A San Francisco refinancing story and when lenders say no33:08 Rupert Mitchell on global markets, fiscal spending, and gold44:22 Meb Faber on base rates, bubbles, and this time is different01:00:16 How to track AI’s real world impact in corporate data If you enjoy deep dives into investing, AI, market structure, credit cycles, global equities, and evidence based portfolio construction, be sure to subscribe to Excess Returns for more conversations like this.

    1h 10m

  4. 4 DAYS AGO

    Most Portfolios Are Built Backwards | Cullen Roche on Building Your Perfect Portfolio

    In this episode of Excess Returns, we sit down with Cullen Roche to discuss his new book Your Perfect Portfolio and the deeper principles behind building a portfolio that actually fits your life. Rather than starting with asset allocation models or return forecasts, Cullen reframes investing around risk, time horizons, and lifetime consumption. We explore how to think about stocks, bonds, factor investing, international diversification, private assets, inflation hedges, and more through the lens of financial planning and asset liability matching. This is a practical, wide ranging conversation about portfolio construction, behavioral risk, and how investors can align their investments with real world goals. Main topics covered: Why you are a saver, not an investor, and why that distinction matters Defining risk as uncertainty of lifetime consumption The temporal conundrum and matching investments to time horizons Human capital as your most important asset and how it impacts portfolio risk The pros and cons of a 100 percent stock allocation Rethinking the 60 40 portfolio after inflation and rising rates International diversification and valuation differences between US and global markets Factor investing as a time horizon tool rather than an alpha strategy The forward cap portfolio and skating to where the market cap puck is going Inflation protection strategies including stocks, TIPS, gold, and the permanent portfolio Risk parity and the tradeoff between diversification and return Countercyclical rebalancing and managing behavioral risk Private equity, venture capital, and the illiquidity premium Defined duration investing and asset liability matching for individual investors The real impact of inflation, taxes, and fees on long term returns Timestamps: 00:00 Risk as lifetime consumption and asset liability matching 01:03 Introduction to Your Perfect Portfolio 05:25 You are a saver, not an investor 08:24 Defining risk and uncertainty of lifetime consumption 10:15 The temporal conundrum and time horizons 12:38 Using past performance and forecasting responsibly 15:00 Human capital and portfolio construction 17:12 The case for a 100 percent stock allocation 19:50 Rethinking the 60 40 portfolio 24:00 Adding international diversification 29:43 Factor investing across time horizons 35:00 The forward cap portfolio concept 38:27 Inflation hedges and the permanent portfolio 42:27 Risk parity explained 44:49 Countercyclical rebalancing 47:17 Private assets and illiquidity 51:25 Defined duration strategy and Discipline Funds ETFs 56:00 Real returns after inflation, taxes, and fees If you are interested in portfolio construction, asset allocation, financial planning, factor investing, inflation protection, or building a long term investment strategy that matches your goals, this conversation offers a thoughtful framework for thinking differently about risk and returns.

    1 hr

  5. 6 DAYS AGO

    The Edge Has Shifted | Matt Reustle on How the Best Investors Use AI

    In this episode of Excess Returns, we sit down with Matt Russell of Business Breakdowns to explore how AI is actually being used in investing today. We go beyond the hype and break down practical use cases for AI in portfolio management, stock research, due diligence, monitoring, and idea generation. From deep research models and agentic AI to prompt engineering and workflow design, this conversation walks through how professional investors can use AI tools to increase productivity, improve decision-making, and reduce blind spots without losing their edge. If you are an asset manager, analyst, allocator, or DIY investor wondering how AI will impact investing and stock picking, this episode offers a clear, practical roadmap. Main topics covered: The evolution from early large language models to deep research and agentic AI for investors LLMs vs agent-based AI and why the distinction matters for investment research How AI fits into an investor’s workflow, from due diligence to portfolio monitoring Using AI to monitor KPIs, earnings calls, and cross-industry signals in real time How AI can help kill bad ideas faster and surface deal breakers early Prompt engineering for investors, including mindset framing, audience targeting, and output design Building mental models into AI systems to reflect your investment philosophy AI tech stacks for investors, including writing tools, deep research models, and browser-based AI Iteration, experimentation, and standardized testing of prompts across model upgrades The impact of AI on alpha generation, active management, and generalist vs specialist investors Organizational adoption strategies for investment firms considering AI Customization, agentic workflows, and what AI in investing could look like five years from now Timestamps: 00:00 How AI tools increase investor productivity 01:16 Why early ChatGPT was a head fake for investors 03:07 The inflection point with deep research and agentic AI 05:00 LLMs vs agents explained in plain English 07:01 Where AI fits inside an investment workflow 09:28 Replacing manual earnings transcript work 11:40 Real-time monitoring and AI alerts 19:24 Using AI to kill bad investment ideas faster 22:01 Trust but verify, hallucinations and safeguards 25:29 Matt’s AI tech stack for investing 30:00 Prompt engineering breakthroughs 33:00 Standardized experimentation across new AI models 36:07 Building idea generation prompts step by step 40:15 Using AI as an editor and critical reviewer 43:50 Does AI compress investor skill differences 46:10 How funds should adopt AI internally 50:40 Fear of falling behind in asset management 53:05 Generalists vs specialists in an AI world 55:18 AI and the pursuit of alpha 57:00 Customization, agents and the future of investing 01:01:10 Coding agents and building tools with AI

    1h 4m

  6. 23 FEB

    When You've Won the Game, Stop Playing | What Great Investors Taught Us About Portfolio and Purpose

    Subscribe to Two Quants and a Financial Planner on Spotify Subscribe to Two Quants and a Financial Planner on Apple In this episode, we explore one of the most important but overlooked questions in investing: what is the purpose of your portfolio? Through a series of powerful clips and reflections from Aswath Damodaran, Meb Faber, Ben Hunt, Cullen Roche, Corey Hoffstein, Daniel Crosby, Larry Swedroe, and Wes Gray, we examine how goals like financial freedom, funded contentment, liability driven investing, retirement planning, and multi generational wealth shape the way we invest. This conversation goes beyond beating the market and focuses on preserving and growing wealth, reducing financial stress, aligning money with meaning, and defining what a life well lived truly looks like. Topics covered include: Why the end game of investing matters more than beating the market Preserving and growing wealth vs trying to get rich Freedom as the ultimate goal of financial independence Funded contentment and what it means to live a life well lived Liability driven investing and matching assets to future needs The difference between getting rich and staying rich Needs vs desires and understanding marginal utility of wealth Retirement planning and redefining success beyond a number Multi generational wealth and thinking beyond your own lifetime The psychological impact of growing up with or without money Financial freedom, stress reduction, and peace of mind Tactical financial goals vs long term purpose driven investing Education, legacy, and investing in the next generation Why once you win the game you may not need to keep playing Timestamps: 00:00 Aswath Damodaran on preserving and growing wealth10:04 Meb Faber on freedom, contentment, and the hedonic treadmill22:36 Ben Hunt on funded contentment and finding your pack28:23 Cullen Roche on risk as uncertainty of consumption33:25 Corey Hoffstein on liability driven investing and not worrying about money41:50 Daniel Crosby on financial freedom and living life on your own terms47:33 Larry Swedroe on needs vs desires and staying rich55:54 Wes Gray on big blue arrows, tactical goals, and peace of mind

    1h 9m

  7. 21 FEB

    When Safe Becomes the Most Dangerous | The 100-Year Thinkers on AI, Staples and How Words Mislead

    Subscribe to the 100 Year Thinkers of Spotify Subscribe to the 100 Year Thinkers of Apple In this episode of the 100 Year Thinkers, Matt Zeigler and Bogumil Baranowski continue their conversation with Robert Hagstrom and Chris Mayer, diving deeper into general semantics and what it means for investors navigating AI enthusiasm, market volatility, benchmark obsession, and the gamification of markets. From Warren Buffett’s cathedral versus casino metaphor to the risks hiding in so-called “safe” consumer staples stocks, this discussion explores how language, expectations, and mistaken certainty shape investment decisions. If you want to think more clearly about markets, technology, valuation, and your own reactions as an investor, this episode offers a powerful mental framework. Topics Covered What general semantics is and how language influences how investors think IFD disease idealism frustration demoralization and how unrealistic expectations impact markets AI hype, capital spending, and the prisoner’s dilemma facing major tech companies Warren Buffett’s cathedral versus casino metaphor and what it means for investors today Why beating the S and P 500 may not be the right benchmark for success The gamification of markets, retail trading growth, and the shift from long-term investing to speculation Terminal value risk in software stocks amid AI disruption Why low volatility “warm fuzzy” stocks like consumer staples may be more dangerous than they appear Expectations investing, confidence versus overconfidence, and avoiding mistaken certainty The map is not the territory and how to avoid confusing models with reality Everything is connected to everything else markets as biological systems rather than mechanical systems Delayed gratification, compounding, and why wealth is built later in the investment journey Timestamps 00:00 Cathedral versus casino capitalism and the market metaphor02:00 What is general semantics and why it matters for investors03:00 IFD disease unrealistic expectations and AI hype06:40 Outperformance, Bill Miller, and unrealistic return expectations09:00 Are market benchmarks the right way to measure success12:00 What if stock market indexes did not exist14:00 Public versus private markets and myopic loss aversion18:40 Compounding, volatility, and delayed gratification21:00 AI valuations, strategic capital spending, and economic returns24:20 The AI adoption cycle frustration and demoralization30:40 The man in overalls story and delaying reactions33:30 Warren Buffett cathedral versus casino metaphor revisited35:00 Gamification of markets passive flows and species shift in investing39:00 When to sit still versus when to act in volatile markets43:00 Mistaken certainty and the biggest risks in today’s market45:00 The hidden risk in consumer staples and low volatility stocks47:20 Expectations investing confidence versus overconfidence49:40 Everything is connected markets as living systems53:00 What success really means beyond beating an index56:20 The map is not the territory final lessons for investors

    1h 16m

  8. 19 FEB

    The Global Regime Change | Jason Hsu on AI, Factor Investing and What Investors Miss About China

    In this episode of Excess Returns, Jason Hsu returns for a wide-ranging conversation on China’s economy, the global AI race, emerging markets, factor investing, and what the next phase of globalization could mean for U.S. investors. We explore how China’s fiercely competitive domestic capitalism contrasts with common Western narratives, why AI could reshape professional services the way globalization reshaped manufacturing, and how investors should think about portfolio allocation in a shifting G2 world. This discussion covers China manufacturing dominance, Chinese EV competition, U.S. vs. China AI strategy, emerging markets investing, factor investing in inefficient markets, and how machine learning is changing quantitative portfolio management. Main topics covered Why U.S. investors misunderstand China’s economic system and the role of competition inside its domestic market How China became the world’s manufacturing powerhouse and what that means for tariffs and trade wars The Chinese government’s role as a venture-style capital allocator rather than a central planner The real estate reset in China and the shift toward technology, AI, and advanced manufacturing AI as the next wave of globalization and its impact on professional services and labor markets Whether the U.S. vs. China AI competition is truly winner-take-all Capital expenditure intensity in the U.S. vs. capital efficiency and open-source innovation in China U.S. exceptionalism, G2 geopolitics, and portfolio diversification beyond a U.S.-centric allocation Why emerging markets ex-China may differ from China tech exposure The case for separating China from emerging markets in asset allocation The concept of China as an alpha reservoir due to retail-driven market inefficiencies Why traditional value and factor strategies have struggled in the U.S. but still work in China How machine learning and AI are changing quantitative investing and factor construction The launch of CNQQ and accessing large-cap China technology exposure Timestamps 00:00 China as the world’s factory and the role of fierce internal competition 01:02 Why U.S. investors misunderstand China’s economy 03:48 Is China capitalist despite the Communist Party label 05:33 The government as a VC-style investor rather than central planner 07:45 China EV competition and manufacturing dominance 09:23 Tariffs, trade leverage, and manufacturing monopoly dynamics 12:18 China’s bear market and valuation opportunity 13:59 The real estate reset and shift toward productive capital 16:00 AI as the next wave of globalization 18:01 Labor force participation and economic disruption from AI 19:46 Jobs that may survive in an AI-dominated world 22:00 Is U.S. vs. China AI a winner-take-all battle 24:13 Chip restrictions and long-term innovation incentives 26:54 Capital efficiency in China vs. heavy AI capex in the U.S. 29:27 Rebalancing away from U.S.-centric portfolios 31:18 The end of U.S. exceptionalism and the move toward a G2 world 34:00 How endowments approach U.S., developed, and emerging markets 36:35 CNQQ and accessing China large-cap technology 40:45 China as the great alpha reservoir 45:49 The future of factor investing in efficient vs. inefficient markets 49:06 Machine learning, factor decay, and next-generation quant strategies 55:17 Can AI replace active portfolio managers If you enjoy deep conversations on global markets, AI investing, China technology, emerging markets, and quantitative strategies, make sure to subscribe to Excess Returns for more interviews with leading investors and thinkers.

    1h 3m
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Excess Returns is dedicated to making you a better long-term investor and making complex investing topics understandable. Join Jack Forehand, Justin Carbonneau and Matt Zeigler as they sit down with some of the most interesting names in finance to discuss topics like macroeconomics, value investing, factor investing, and more. Subscribe to learn along with us.

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  • Creator
    Excess Returns
  • Years Active
    2019 - 2026
  • Episodes
    469
  • Rating
    Clean
  • Copyright
    © 905628
  • Show Website
    Excess Returns

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