Excess Returns

Kai Wu of Sparkline Capital shows how to uncover hidden AI exposure, overlapping ETF holdings, and unintended factor bets in your portfolio. Owning several funds can still leave you dependent on the same companies and the same AI spending cycle.In this episode of Excess Returns, Kai joins Matt Zeigler and Justin Carbonneau for a walkthrough of Sparkline Analytics. Using examples from the S&P 500, Nasdaq, emerging markets, and active funds, they examine what investors actually own beneath the fund labels.Kai explains his distinction between AI infrastructure, early adopters, and laggards, then demonstrates how factor analysis, intangible asset data, and a portfolio builder can help investors ask better questions about diversification and manager performance.Explore Sparkline Analytics:https://app.sparklinecapital.com/Topics covered:How AI infrastructure exposure can overlap across the S&P 500, Nasdaq, and thematic AI fundsWhy Kai Wu distinguishes AI early adopters from infrastructure builders and laggardsHow rising capital expenditures can change the economics of formerly asset-light businessesWhy a fund's number of holdings can overstate its diversificationHow semiconductor exposure can connect emerging markets to the same AI tradeThe trade-off between reducing AI infrastructure exposure and increasing exposure to laggardsWhat ARKK illustrates about separating stock selection from factor headwindsWhy dividend, value, and low-volatility funds can have similar underlying exposuresHow patents, R&D, and human capital reveal assets traditional value metrics can missUsing fund comparisons and portfolio look-through tools to identify overlapping positionsChapters:00:00 Sparkline Analytics and hidden AI exposure in the S&P 50008:05 AI adopters, laggards, and multinational businesses12:47 How index exposure changes as the AI cycle evolves20:41 Does adding an AI fund diversify your portfolio?25:44 Capital spending and effective portfolio concentration30:14 Capital intensity and AI exposure beyond Big Tech35:44 Diversifying without retreating into AI laggards40:48 Screening funds and looking inside ARKK46:11 Separating stock-picking alpha from factor exposure52:35 Dividend, value, and low-volatility fund overlap57:52 Intangible value and a fund-diligence checklist01:05:39 Building a portfolio and looking through its holdings01:10:39 Where to find Sparkline AnalyticsLearn more about the Excess Returns podcast network:https://excessreturns.coNo information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.

Daniel Crosby explains why investors can lose money in winning funds and how to build a portfolio they can actually hold. Picking the right investment is only part of the challenge; staying with it through discomfort is where behavior can overwhelm the plan.In this episode of Excess Returns, psychologist and Orion Chief Behavioral Officer Daniel Crosby joins Matt Zeigler to discuss the new paperback edition of The Behavioral Investor. Daniel shares the story of a fund that returned over 18% annually for a decade while its average investor lost money, then explains how safety, stability, and success portfolios can help align investment decisions with human psychology. They also explore gambling apps, the limits of risk questionnaires, social media feedback loops, and the human side of financial advice in an AI-driven world.Topics covered:- Why a fund's returns can look very different from the returns its investors earn- Building safety, stability, and success buckets around different financial needs- The differences between risk tolerance, risk capacity, and risk composure- Why knowing what to do does not guarantee doing it during a drawdown- Daniel Crosby's case for rules, diversification, and meaningful benchmark differences- How sports betting and prediction markets blur the distinction between investing and gambling- Why recent market experience can create unrealistic expectations about future returns- Education, environment, and encouragement as tools for changing investor behavior- How algorithms reinforce market biases and why excitement can signal a blind spot- Where AI can free financial advisors to spend more time on human relationshipsThe Behavioral Investor - paperback edition:https://link.amazon/B00hgkHGGDaniel Crosby on X:https://x.com/danielcrosbyOrion Behavioral Finance:https://orion.com/behavioral-financeResearch discussed:The heat of the moment: the effect of sexual arousal on sexual decision makinghttps://onlinelibrary.wiley.com/doi/abs/10.1002/bdm.501Chapters:00:00 Daniel Crosby and The Behavioral Investor04:51 Why winning funds can have losing investors11:46 Risk tolerance, capacity, and composure17:38 Stability portfolios and rules-based stock selection23:06 Investing, gambling, and prediction markets27:27 Recency bias and unrealistic return expectations31:30 Addictive apps and changing investor behavior35:48 Investor distractions and the media diet40:51 Investment excitement, humility, and AI45:00 What music reveals about consistency and experimentation49:29 Where to find Daniel Crosby and the bookLearn more about the Excess Returns podcast network:https://excessreturns.coNo information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.

Ted Mortonson of Baird explains why conviction in the AI boom doesn't mean buying every dip in tech stocks.He joins Jack Forehand and Justin Carbonneau to explain what experienced technology investors watch before putting money back to work—and why he still sees AI as the most powerful technology cycle of his career.The discussion moves from lessons of the dot-com bust to the companies Ted believes can capture AI's profits. They explore the importance of free cash flow, the financing demands of the data center buildout, and how large IPOs could force portfolio managers to sell existing holdings. Ted also shares his views on Apple, Meta, Nvidia, Tesla, and SpaceX, before closing with lessons from his service as a naval aviator.Topics covered:- Why Ted Mortonson's institutional clients aren't rushing to buy the semiconductor dip- What past tech cycles teach about taking profits, preserving capital, and waiting for selling to settle- Why Ted Mortonson sees a stronger long-term AI opportunity than the dot-com cycle- How geopolitics, energy costs, regulation, and investor sentiment shape technology investing- Why free cash flow, engineering resources, and manufacturing scale matter for AI beneficiaries- Financing the infrastructure buildout before enterprise adoption delivers returns- How large IPOs and circular financing can create risks for existing shareholders- Ted Mortonson's concerns about Apple and his outlook for Meta, Tesla, and SpaceX- Optical networking, advanced chip architectures, and high-bandwidth memory- Navy lessons on decisions under uncertainty, teamwork, and integrityChapters:00:00 Ted Mortonson on the risks facing tech investors05:42 Market sentiment and why his clients aren't buying yet12:00 Dot-com lessons and the danger of buying every dip16:17 Why Ted sees a different AI cycle23:06 Which companies could capture AI's profits?27:38 Regulation and financing the AI buildout35:10 Can the market absorb massive AI IPOs?39:30 Circular financing and the Tesla-SpaceX thesis46:06 Apple, Meta, and competing AI strategies54:19 SpaceX, advanced chips, and memory technology58:26 Lessons from serving as a naval aviator01:03:35 Closing thoughtsTed Mortonson at Baird:https://www.rwbaird.com/corporations-and-institutions/institutional-equities-research/team/ted-mortonson/Learn more about the Excess Returns podcast network:https://excessreturns.coNo information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.

Brian Portnoy of Shaping Wealth explores why experienced investors build very different portfolios—and what that means for your own money.In How I Invest My Money Volume Two, 25 new contributors explain how they invest their personal wealth. Brian joins Matt Zeigler to discuss what those stories reveal about the gap between an optimal portfolio on paper and one you can actually live with.From large cash reserves and paid-off mortgages to investments in their own businesses, their choices reflect family needs, career risk, and priorities that a model can struggle to capture.The conversation also explores why low-cost index funds don't settle every investing question, how financial professionals think about bonds and alternatives, and what it means to compound relationships, knowledge, and opportunities alongside money.How I Invest My Money Volume Two:https://link.amazon/B0aVpNlfSTopics covered:Why knowing how markets work doesn't produce one right portfolio for everyoneWhat Ben Hunt and other contributors reveal about investing beyond a brokerage accountConnecting personal goals to asset allocation and risk toleranceWhy business ownership and household needs change the role of stocks and bondsWhy financial professionals may deliberately hold substantial cash reservesThe difference between spreadsheet optimization and a portfolio you can stick withPaying off a mortgage, feeling secure, and accepting the opportunity costThe complexity and illiquidity that can come with alternative investmentsMatt Zeigler's approach to compounding human, intellectual, social, and financial capitalBrian Portnoy's experience editing the book with his son Zach and sharing opportunities across generationsChapters:00:00 How finance professionals invest their own money05:35 Why investing extends beyond index funds12:20 Connecting financial goals to portfolio choices18:32 Why investors choose different stock and bond allocations25:05 Cash reserves, career risk, and peace of mind34:55 Paying off a mortgage: security versus opportunity cost40:11 Alternatives, complexity, and the investment industry44:40 Matt Zeigler on investing in more than money52:22 Brian Portnoy on family, opportunity, and working with his sonShaping Wealth:https://www.shapingwealth.comFollow Brian Portnoy on X:https://x.com/bportnoy_Learn more about the Excess Returns podcast network:https://excessreturns.coNo information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.

Rob Arnott, founder of Research Affiliates and chairman of Syzygy Asset Management, examines S&P 500 concentration and who could benefit most from AI. Owning today’s market leaders is not necessarily the same as owning tomorrow’s biggest investment winners.Subscribe on Spotify⁠⁠⁠⁠⁠⁠⁠⁠Subscribe on AppleIn this episode of The Intangible Economy, Rob joins Kai Wu to explore how index construction shapes what investors own, why index additions can create hidden costs, and why he would consider buying a richly valued IPO before index funds have to buy it. The conversation also covers AI’s shift toward capital-intensive business models, GPU depreciation, intangible assets, and the importance of testing investment ideas rather than chasing impressive backtests.Topics covered:Why Rob Arnott sees a real AI revolution alongside risks in the prices of its leading stocksHow AI customers could capture value beyond the infrastructure buildersValuation changes versus fundamental growth in large and smaller companiesTesla, SpaceX, and the price pressures surrounding index inclusionHidden implementation costs that do not appear in an index fund’s expense ratioSelecting index members by business size rather than market valueAI financing, customer economics, and the risk of rapid hardware obsolescenceR&D, intangible assets, and what conventional book value can missCompetitive moats, leadership, and creating value for customersUsing AI in research while resisting overfitting and performance chasingResearch discussed:⁠The Active Side of Indexing⁠⁠Fundamental Growth⁠Learn more about ⁠Syzygy Asset Management⁠.Timestamps:00:00 Rob Arnott on index concentration and AI’s future winners12:13 What S&P 500 investors own and pay for20:35 Why Rob Arnott would buy before index inclusion25:41 Hidden index costs and fundamental membership rules30:37 Passive investing’s embedded decisions36:40 Big Tech’s shift from asset light to capital intensive42:41 GPU depreciation and economic obsolescence47:03 R&D, intangible assets, and value investing51:04 How durable are competitive moats?59:01 Using AI in business and investment research01:05:11 Performance chasing and the backtest trap Learn more about the Excess Returns podcast network:⁠excessreturns.co⁠No information discussed in this podcast should be construed as investment advice. Securities discussed may be held by the hosts and guests, their firms or their clients.