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. 21 HR AGO

    What War Charts and AI Bubbles Miss | The Weekly Market Insight – March 8, 2026

    Follow Two Quants and a Financial Planner on Spotify Follow Two Quants and a Financial Planner on Apple In this new weekly Excess Returns recap, Jack Forehand and Matt Zeigler highlight the most important investing insights from recent conversations across the Excess Returns podcast network. Drawing on discussions with Andy Constan, Rob Arnott, Kai Wu, Ben Hunt, Rupert Mitchell, Meb Faber and others, the episode connects ideas across macro, markets, AI, credit cycles and valuation. The conversation focuses on timeless investing principles investors can apply today, including how to evaluate expert opinions, how AI may reshape markets and jobs, what defines a true market bubble, why international stocks may be benefiting from global fiscal spending, and why the best opportunities in markets often come after long periods of underperformance. Topics covered in this episode How to evaluate expert opinions during major market events and filter signal from noise Andy Constan’s framework for judging credibility based on experience and confidence Why charts showing markets rising after wars are often misleading data mining The difference between believing in AI technology and believing AI stocks are good investments How AI could both replace and augment human work through the task based structure of jobs Rob Arnott’s definition of a market bubble using implausible growth assumptions Why many technology leaders ultimately fail to justify the expectations priced into their stocks The difference between software companies whose moat is code and those with durable intangible advantages How brand, switching costs, distribution and network effects protect enterprise software companies Why AI may be one of the most disruptive technologies in history and what that means for markets Meb Faber on the myth that the easy money has already been made in international and value stocks The behavioral challenge of holding unpopular strategies through long periods of underperformance Rob Arnott on why small cap value could outperform large cap growth over the next decade Ben Hunt on the point in every credit cycle when lenders say no more How rising costs of capital can trigger boom bust credit cycles Rupert Mitchell on why global equity markets often follow government fiscal spending The growing role of international fiscal policy and capital flows in global market leadership Timestamps 00:00 Introduction and the idea behind the weekly Excess Returns recap show03:00 Andy Constan on how to evaluate experts and filter market commentary11:40 Why charts showing markets rising after wars can be misleading17:00 Kai Wu on AI technology versus AI investments and the future of work25:37 Rob Arnott on how to define a market bubble using valuation assumptions29:35 Kai Wu on software moats, intangible assets and enterprise software durability35:31 Rob Arnott on how disruptive AI could be for the global economy39:54 Meb Faber on why the easy money has never been made in markets43:57 Rob Arnott on small cap value versus large cap growth opportunities48:39 Ben Hunt on credit cycles and the moment lenders pull back55:56 Rupert Mitchell on fiscal spending and global equity market performance

    1h 2m

  2. 3 DAYS AGO

    1% Growth. Zero Jobs | Jim Paulsen on the Recession Hiding in Plain Sight

    Subscribe to the Jim Paulsen Show on Spotify Subscribe to the Jim Paulsen Show on Apple Podcasts In this episode of the Jim Paulsen Show, Jim joins Jack Forehand and Justin Carbonneau to break down the macro forces shaping today’s markets and economy. Jim explains why the economy may be far weaker than headline GDP numbers suggest, how technology and AI investment are masking weakness in the broader economy, and why leadership in the stock market may be shifting. The conversation also explores the market implications of geopolitical conflict, the relationship between policy and market leadership, and how investors should think about AI’s long-term economic impact. Topics covered in this episode How geopolitical events like the Iran conflict affect markets, volatility, oil prices, and investor sentiment Why market reactions to geopolitical shocks often fade once the situation is “vetted” by investors The relationship between oil prices, the US dollar, and global financial markets Why Paulsen remains constructive on international stocks and emerging markets despite recent volatility Why energy and food now represent a much smaller share of consumer spending than in past inflation cycles The argument that inflation fears may be overstated given structural disinflationary forces in the economy How AI and technological innovation can destroy some jobs while simultaneously creating new economic demand Why technological progress often lowers costs and expands markets rather than simply eliminating work The concept that the “new economy” driven by technology investment is now large enough to influence overall GDP growth Paulsen’s analysis showing that roughly 11 percent of the economy tied to new-era investment is growing rapidly while the remaining 89 percent is barely growing Why the broader economy may resemble a recession even while headline GDP remains positive How the dominance of large technology companies in indexes like the S&P 500 may be masking weakness in the broader market The historical “toggle” between technology leadership and broader market leadership in equity markets Why policy conditions like the yield curve and monetary easing often drive leadership shifts toward value, small caps, and cyclical stocks Whether the Federal Reserve could begin easing policy without a traditional recession Why policy support may eventually broaden the bull market beyond technology stocks Timestamps 0:00 Jim Paulsen on geopolitical volatility, oil prices, and market reactions2:50 How investors should think about the Iran conflict and market implications10:50 The relationship between oil prices, the US dollar, and safe-haven flows12:20 Why Paulsen likes international and emerging market stocks14:30 Why higher oil prices may not lead to sustained inflation18:40 AI disruption and the economic debate around jobs and productivity23:00 How innovation historically creates new demand and economic growth29:40 Technology is the tail wagging the economic dog33:30 Why the “new economy” is growing far faster than the rest of the economy37:00 Evidence that most of the economy may already resemble a recession41:00 Profit growth disparity between technology and the rest of the economy45:40 Why the stock market can mask weakness in the broader economy46:30 The historical leadership toggle between tech and the broader market49:00 Valuation differences between technology and other sectors50:30 How policy conditions influence market leadership55:00 Signs that leadership may already be shifting beyond tech57:00 Could the Fed ease without a traditional recession59:00 What a policy shift could mean for the next phase of the bull market

    1h 2m

  3. 5 DAYS AGO

    The Widest Valuation Gap in History | Rob Arnott on What Investors Are Missing About AI

    Rob Arnott returns to Excess Returns to discuss the biggest questions facing investors today, including the impact of geopolitical conflict, the valuation gap between U.S. and international markets, the long-term investment implications of artificial intelligence, and why extreme spreads between growth and value may present major opportunities. Arnott, founder of Research Affiliates and pioneer of fundamental indexing, explains why AI itself is not necessarily a bubble but many AI stocks may be priced for implausible growth. He also discusses why small cap and value stocks may offer some of the most compelling long-term opportunities in decades, how market narratives drive valuations, and why diversification beyond the U.S. could be critical for investors. Throughout the conversation, Arnott draws on decades of market history to explain how bubbles form, why profit margins tend to mean revert, and how investors should think about positioning portfolios for the next market cycle. Topics covered in this episode: • Why Rob Arnott believes AI is real but many AI stocks may be in a bubble • How market narratives can push valuations far beyond fundamentals • Why U.S. stocks trade at roughly twice the valuation multiples of international markets • The widening valuation gap between growth and value stocks • Why small cap stocks may be one of the most attractive opportunities today • The massive capital spending required to build the AI ecosystem • How technological revolutions historically destroy jobs but create new opportunities • Why investors should learn to use AI tools to remain competitive • The definition of a market bubble based on implausible growth expectations • Lessons from the dot-com bubble and the history of dominant technology companies • Why profit margins tend to mean revert over time • The long-term outlook for international stocks and diversification • How fundamental indexing works and why it can create rebalancing alpha • The concept of the “Trifecta” approach combining value, core indexing, and growth • The risks of conglomerate premiums and the diversification discount • Why the largest companies in the market rarely remain dominant over long periods • How investors should think about balancing growth exposure with cheaper opportunities Timestamps: 00:00 AI vs AI Stocks: Why Arnott Sees a Bubble 00:01 Introduction to Rob Arnott and Research Affiliates 02:13 The Iran Conflict and How War Impacts Markets 06:41 U.S. Valuations vs International Opportunities 08:50 The Extreme Spread Between Growth and Value 10:00 The Small Cap Opportunity and Index Effects 13:08 The Citrini AI Paper and Long-Term Technology Shifts 14:09 How Technological Revolutions Destroy and Create Jobs 16:00 How AI Is Already Changing Investment Research 20:00 Why AI Tools Are Still Losing Money 23:40 How Investors Should Think About AI Exposure 25:21 Arnott’s Definition of a Market Bubble 27:41 Lessons from the Dot-Com Bubble 28:34 Profit Margins and Mean Reversion 30:34 Technology Moats and Competitive Disruption 32:12 Will Mean Reversion Still Work in Markets? 36:02 The Case for International Stocks 41:39 The Trifecta: A New Framework for Indexing 51:15 Why Expensive Slow-Growth Companies Underperform 56:25 Conglomerate Premiums and Mega Cap Tech 57:00 The Long-Term Case for Value and Small Caps 01:00:00 Why Market Leaders Rarely Stay on Top

    1h 3m

  4. 3 MAR

    100% Out of US Stocks | 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 topics 01:01 Framework for evaluating geopolitical shocks and information quality 11:46 Market reaction to the Iran conflict and asset pricing implications 23:00 Why historical war data may not be reliable for market forecasting 27:03 How to analyze AI’s impact on productivity and economic growth 37:00 AI capex, credit markets, and funding risks 42:24 Growth, inflation, and Fed policy in the current cycle 49:20 The case for international equities over US markets 56:20 Trump accounts, fiscal policy, and capital allocation 01:02:23 What Andy is watching most closely in the months ahead

    1h 4m

  5. 1 MAR

    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

  6. 28 FEB

    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

  7. 27 FEB

    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

  8. 25 FEB

    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
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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.

Information

  • Creator
    Excess Returns
  • Years Active
    2019 - 2026
  • Episodes
    472
  • Rating
    Clean
  • Copyright
    © 905628
  • Show Website
    Excess Returns

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