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. 2天前

    The Existential Spending Battle | Adrian Helfert on What You’re Missing in the AI Arms Race

    In this episode of Excess Returns, we sit down with Adrian Helfert of Westwood to discuss how investors should be thinking about portfolio construction in a market shaped by artificial intelligence, high levels of concentration, shifting interest rate dynamics, and evolving economic signals. The conversation covers how AI-driven capital spending is changing return profiles across markets, why traditional investing rules are breaking down, and how investors can balance growth, income, and risk in an uncertain environment. Adrian shares his framework for understanding return drivers, his views on market concentration and valuation, and how to think about diversification, macro risk, and income generation going forward. Main topics covered • How Westwood frames portfolio construction around capital appreciation, income, and event-driven returns • Why AI spending is both a major opportunity and a growing existential risk for large companies • The sustainability of market concentration and what it means for future returns • Whether higher interest rates really hurt growth stocks the way investors expect • How massive data center and AI capital expenditures could translate into productivity gains • The case for market broadening beyond the Magnificent Seven • Why traditional recession indicators have failed in recent cycles • How inflation, labor markets, and Federal Reserve policy interact today • Rethinking the classic 60/40 portfolio and the role of private markets • Using covered calls and active income strategies to manage risk and generate yield Timestamps 00:00 Introduction and near-term opportunities versus long-term risk 02:40 Capital appreciation, income, and event-driven investing framework 06:30 Have markets structurally changed to support higher returns 09:30 Intangible assets, AI, and margin expansion 10:20 The scale of AI and data center capital spending 13:00 Productivity gains and return on investment from AI 16:00 AI as both opportunity and risk for companies 19:30 Market concentration and diversification concerns 23:30 Will market leadership eventually broaden 25:30 Growth stocks, duration, and interest rates 29:30 International diversification and global investing 33:30 Why recession indicators have failed 39:00 Inflation outlook and Federal Reserve policy 46:00 Rethinking the 60/40 portfolio 53:00 Enhanced income strategies and covered calls 59:00 One investing belief most peers disagree with

    1 小时 1 分钟

  2. 4天前

    The Precarity Line | Ben Hunt and Adam Butler on the Broken Math of the American Dream

    In this special episode, Adam Butler and Ben Hunt join Matt Zeigler to unpack one of the most charged debates in markets and economics today: whether our official statistics still reflect lived reality. Building on Mike Green’s work and Adam Butler’s essay The Bureau of Missing Children, the conversation moves beyond the technical definition of poverty to a deeper idea of economic precarity, the growing gap between what we measure and what people actually experience. Together, they explore debt, housing, childcare, labor mobility, AI, and the erosion of meaning in economic language, while wrestling with what policy, community, and human-centered solutions might look like in a world that increasingly feels unstable. Main topics covered Why the debate should focus on precarity rather than poverty The disconnect between inflation statistics and lived experience How debt, housing, childcare, and education drive economic insecurity The idea of a participation budget for modern family formation Why labor mobility has broken down since the financial crisis How asset prices and credit intensify risk for households The role of grandparents and off-balance-sheet support in the economy Darwin’s wedge, positional goods, and rising costs of everyday life The impact of AI, technocracy, and anti-human incentives Centralized versus decentralized solutions to today’s economic challenges What it means to carry the fire and preserve human-centered values Timestamps 00:00 Introduction and the emotional roots of the precarity debate 02:00 Poverty versus precarity and what we are really measuring 06:30 Technocrats, narratives, and the limits of economic statistics 09:00 Personal experiences with precarity and debt 15:00 The Bureau of Missing Children and family formation economics 21:00 Modeling household income and participation budgets 25:50 Rising costs of childcare, housing, and everyday life 33:00 Darwin’s wedge and positional competition 36:45 Debt, housing, and labor immobility 40:00 Grandparents, unpaid care, and off-balance-sheet subsidies 46:30 How today differs from 40 or 50 years ago 49:40 Labor mobility as a lost engine of opportunity 55:00 Policy paths, mission-driven economics, and decentralization 01:11:00 Visionary leadership versus bottom-up solutions 01:15:50 Carrying the fire and preserving meaning 01:17:30 Where to follow Adam Butler and Ben Hunt

    1 小时 19 分钟

  3. 6天前

    The Alpha No Human Can Find | David Wright on Machine Learning's Hidden Edge

    In this episode of Excess Returns, we sit down with David Wright, Head of Quantitative Investing at Pictet Asset Management, for a deep and practical conversation about how artificial intelligence and machine learning are actually being used in real-world investment strategies. Rather than focusing on hype or black-box promises, David walks through how systematic investors combine human judgment, economic intuition, and machine learning models to forecast stock returns, construct portfolios, and manage risk. The discussion covers what AI can and cannot do in investing today, how machine learning differs from traditional factor models and large language models like ChatGPT, and why interpretability and robustness still matter. This episode is a must-watch for investors interested in quantitative investing, AI-driven ETFs, and the future of systematic portfolio construction. Main topics covered: What artificial intelligence and machine learning really mean in an investing context How machine learning models are trained to forecast relative stock returns The role of features, signals, and decision trees in quantitative investing Key differences between machine learning models and large language models like ChatGPT Why interpretability and stability matter more than hype in AI investing How human judgment and machine learning complement each other in portfolio management Data selection, feature engineering, and the trade-offs between traditional and alternative data Overfitting, data mining concerns, and how professional investors build guardrails Time horizons, rebalancing frequency, and transaction cost considerations How AI-driven strategies are implemented in diversified portfolios and ETFs The future of AI in investing and what it means for investors Timestamps: 00:00 Introduction and overview of AI and machine learning in investing 03:00 Defining artificial intelligence vs machine learning in finance 05:00 How machine learning models are trained using financial data 07:00 Machine learning vs ChatGPT and large language models for stock selection 09:45 Decision trees and how machine learning makes forecasts 12:00 Choosing data inputs: traditional data vs alternative data 14:40 The role of economic intuition and explainability in quant models 18:00 Time horizons and why machine learning works better at shorter horizons 22:00 Can machine learning improve traditional factor investing 24:00 Data mining, overfitting, and model robustness 26:00 What humans do better than AI and where machines excel 30:00 Feature importance, conditioning effects, and model structure 32:00 Model retraining, stability, and long-term persistence 36:00 The future of automation and human oversight in investing 40:00 Why ChatGPT-style models struggle with portfolio construction 45:00 Portfolio construction, diversification, and ETF implementation 51:00 Rebalancing, transaction costs, and practical execution 56:00 Surprising insights from machine learning models 59:00 Closing lessons on investing and avoiding overtrading

    1 小时 1 分钟

  4. 12月15日

    The 100 Year Thinkers | Chris Mayer and Robert Hagstrom on the Dangers of Abstraction

    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 Spotify ⁠⁠https://open.spotify.com/show/5IsVVM27KWP6SUW6KN2ife⁠⁠ Subscribe on Apple Podcasts ⁠⁠https://podcasts.apple.com/us/podcast/the-100-year-thinkers-long-term-compounding-in-a-short-term-world/id1845466003⁠⁠ Subscribe on YouTube ⁠⁠https://youtube.com/@excessreturns⁠

    1 小时 13 分钟

  5. 12月13日

    Magnet Above. Trap Door Below | Inside the Options Flows Driving Markets with Brent Kochuba

    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 Spotify ⁠https://open.spotify.com/show/4KR2YVJqk2lnVETMKDavJf⁠ Subscribe on Apple Podcasts ⁠https://podcasts.apple.com/us/podcast/the-opex-effect/id1711880009⁠ Subscribe on YouTube ⁠https://www.youtube.com/channel/UCPYvx_y92dvI1PSdiho0ALw

    1 小时 10 分钟

  6. 12月11日

    He Was Overweight Tech for 15 Years. He Just Downgraded the Mag Seven | Ed Yardeni Explains Why

    Ed Yardeni returns to Excess Returns to break down the evolving market landscape, why he moved the Magnificent 7 to underweight, and how AI, productivity, interest rates, global markets, and sector leadership will shape the next stage of the Roaring 2020s. Ed explains why the economy has remained so resilient, what could finally trigger a true market broadening, and how investors should think about everything from tech competition to inflation, private credit risks, and Fed policy heading into 2026. Main topics covered • Why Ed reduced the Magnificent 7 and tech from overweight to market weight • How extreme sector concentration affects portfolio construction • The escalating competition inside AI and large-cap tech • The AI CapEx boom and how it changes earnings, margins, and valuation • Valuation considerations for tech leaders at this stage of the cycle • Whether the Mag 7 should be compared to past tech bubbles • How AI adoption may spread to the broader economy and boost productivity • Economic impact of AI on jobs, wages, and long-term inflation • Why the US economy avoided recession despite persistent warnings • Rolling recessions vs traditional recessions and how they shape markets • Private credit risks and whether they pose a systemic threat • Prospects for small caps, mid caps, financials, industrials, and healthcare • Why 2026 may finally bring true market broadening • The outlook for international investing and emerging markets • Ed’s S&P 500 roadmap to 7,700 next year and 10,000 by 2029 • Fed policy, rate cuts, inflation, bond vigilantes, and political pressure • Key risks investors should monitor heading into 2026 Timestamps 00:00 Mag 7 concentration and the case for rebalancing 03:00 How Ed builds probability-based market scenarios 04:30 Why the Roaring 2020s thesis still holds 06:00 The no-show recession and economic resilience 07:00 Why he moved the Mag 7 and tech to market weight 09:30 How every company is becoming a technology company 12:20 Knowing when a successful thesis has run its course 13:30 The dominance of the US market and global diversification 15:00 Why market weight, not overweight, for tech and the Mag 7 16:00 Tech competition, AI leapfrogging, and margin pressure 18:30 The CapEx boom and valuation questions 21:00 Comparing today’s tech leaders to the 2000 era 23:00 How AI could lift productivity across the entire economy 25:00 Putting AI in historical context 27:00 How new technologies solve constraints like energy and compute 29:00 AI’s long-term impact on productivity and growth 30:00 Labor market disruption and job transition dynamics 31:20 Will AI be deflationary over time? 32:30 Technology, China, automation, and global deflation forces 33:00 Ed’s forecast for the S&P 500 through 2029 35:00 Why recession indicators failed this cycle 37:00 How liquidity facilities prevent credit crunches 39:00 Private credit risks and transparency challenges 40:45 The potential for market broadening in 2026 42:20 Takeaways from the latest Fed meeting 44:00 Should the Fed be cutting rates? 45:00 Fed independence under political pressure 47:00 Why bond vigilantes may return in 2026 48:00 International investing opportunities and ETFs 49:30 Closing thoughts and key risks ahead

    50 分钟

  7. 12月10日

    Why Most Investors Won't Buy the Best Diversifier | Andrew Beer on Managed Futures

    In this episode of Excess Returns, we sit down with Andrew Beer to break down managed futures, hedge fund replication, diversification, and what investors can realistically expect from these alternative strategies. Andrew explains why managed futures can act like a “cloudy crystal ball,” how trend strategies capture major macro shifts, why complexity isn’t always your friend, and how advisors can communicate these concepts to clients. We also explore fees, model portfolios, allocation decisions, global macro themes, and what smart-money positioning looks like heading into 2025. Topics Covered What managed futures actually are and how they work How trend strategies capture big macro shifts Why diversification is most valuable during market stress Why investors struggle with complexity and line-item risk The statistical case for adding managed futures to a 60/40 portfolio Barriers to adoption and how advisors should explain the strategy The role of model portfolios and why slow rebalancing can hurt in regime shifts Why Andrew prefers simplicity over complexity in managed futures Fee sensitivity, ETFs, and how this strategy goes mainstream Indexing, replication, and building more efficient alternatives Why manager selection is hard in this space The “rush to complexity” and why it often hurts returns How hedge fund replication works and what it captures What smart money is positioned for today across equities, rates, currencies, and commodities Macro themes: inflation, rate cycles, the dollar, yen, and global equity opportunities Why international equities may finally be turning How managed futures complement – not replace – stocks and bonds What mainstream adoption might look like over the next decade Timestamps 00:00 Intro and why managed futures matter 02:00 Explaining managed futures in simple terms 06:18 The four major asset classes trend funds trade 10:00 Why trends form and how information reveals itself in prices 11:55 Diversification and how managed futures improve portfolios 14:00 Why investors haven’t widely adopted the strategy 17:01 Communicating the “what,” not the “how,” with clients 18:55 How model portfolios behave in regime change 21:55 How managed futures can move faster than traditional allocations 24:00 Why a simple portfolio of major markets works 26:00 Making alternatives feel less risky 28:00 Performance dispersion across managed futures ETFs 30:00 Why complexity doesn’t equal value 35:20 Fees, ETFs, and what mainstream adoption requires 38:00 The real reason for the industry’s “rush to complexity” 40:35 Should managed futures exclude equities and bonds? 43:00 Why it’s so hard to handicap what will work in advance 46:00 The human side of alternatives and advisor communication 47:00 Hedge fund replication explained 50:00 How replication identifies major themes 52:00 Why replication works only in certain strategies 53:10 What smart money positioning looks like today 55:45 Inflation, rates, the dollar, and global opportunities 58:00 The path to managed futures becoming a standard allocation 59:22 Where to find Andrew Beer online

    1 小时 1 分钟

  8. 12月8日

    The Single Most Important Metric | Matt Reustle on the Patterns That Separate Great Businesses

    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 Spotify https://open.spotify.com/show/7zu6lFpPohoPKhcu0Er9kB Subscribe on Apple Podcasts https://podcasts.apple.com/hr/podcast/teach-me-like-im-five-investing-concepts-made-simple/id1815975642

    1 小时 8 分钟
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关于

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.

信息

  • 创作者
    Excess Returns
  • 活跃年份
    2019年 - 2025年
  • 单集
    431
  • 分级
    儿童适宜
  • 版权
    © 905628
  • 节目网站
    Excess Returns

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