Excess Returns

In this episode of Excess Returns, Rupert Mitchell returns to break down a rapidly shifting global macro landscape and explain how he is positioning across regions, assets, and market regimes. The conversation spans emerging markets, commodities, China, Latin America, US market leadership, and the risks building beneath familiar narratives. Rupert walks through the charts, frameworks, and portfolio construction decisions that underpin his current outlook, with a focus on duration, cash flows, and real assets in a changing cycle.
Topics covered include:
Why US equity leadership is showing signs of fatigue after a decade-plus run

The case for emerging markets as a multi-year relative trade

Latin America as a commodity-driven opportunity rather than a political bet

Brazil, Mexico, and Peru through the lens of fiscal policy and real assets

Why India stands out as expensive within emerging markets

China’s equity market inflection and the role of domestic savings and fiscal support

The difference between onshore A-shares and offshore Chinese equities

Why Rupert prefers lower-beta, dividend-oriented exposure in China

How AI is being deployed differently in China versus the US

The risks facing enterprise software and long-duration growth assets

Portfolio construction, benchmarking, and managing drawdowns across cycles

How Rupert thinks about hedging, trend following, and capital preservation

Timestamps:
00:00 Macro market backdrop and early warning signals
01:00 Venezuela, oil, and why context matters more than headlines
04:40 The chart of truth and US versus international equities
07:00 Emerging markets relative performance and historical parallels
10:00 Duration risk, valuation, and the shift toward real assets
14:30 Mag 7 leadership, software weakness, and AI disruption
18:00 India valuations and the role of flows and derivatives
20:40 Latin America beyond politics: commodities and fiscal drivers
26:00 Brazil, Mexico, and country-level positioning
29:50 Benchmarking and why Latin America is a major overweight
32:10 China’s equity inflection and the ABC framework
36:00 Fiscal policy, buybacks, and domestic savings in China
41:00 Tencent versus Alibaba and managing drawdowns
44:30 AI capex discipline in China versus the US
46:00 Stock selection in China and second-derivative opportunities
51:00 Portfolio construction, benchmarks, and risk management
58:00 Blind Squirrel Macro, live shows, and ongoing research

In this episode of Excess Returns, we sit down with Gary Mishuris, Managing Partner and CIO of Silver Ring Value Partners, to explore how deep fundamental analysis, behavioral insight, and disciplined process come together in real-world investing. Gary shares formative lessons from his early career at Fidelity during the post-tech bubble period, including firsthand experiences learning from legends like Peter Lynch, and connects those lessons to how he evaluates value, quality, and mispricing today. The conversation spans a detailed case study on Warner Bros. Discovery, portfolio construction under uncertainty, selective use of options, and how artificial intelligence is reshaping the research process for long-term investors.
Topics covered in this episode
• Lessons from Peter Lynch and Fidelity on why “just cheap” does not work
• The Silver Ring origin story and how early life experiences shaped a value investing mindset
• Warner Bros. Discovery as a good business plus bad business mispricing case study
• How hated stocks, spin-offs, and catalysts can unlock hidden value
• Conviction, position sizing, and staying rational when the market disagrees
• When and why options can be used in a value investing framework
• Auctions, ego, and why prices can overshoot intrinsic value
• The role of mental models like reflexivity, activation energy, and lollapalooza effects
• How AI fits into an investment research process without replacing judgment
• What average investors should understand about incentives and simplicity
Timestamps
00:00 Introduction and why “just cheap” does not work
02:20 Early career at Fidelity and lessons from Peter Lynch
07:40 The Silver Ring story and learning what real value means
12:00 Warner Bros. Discovery and the good company bad company problem
18:30 Conviction, mispricing, and maintaining discipline in hated stocks
26:40 Using options selectively and managing portfolio-level risk
34:10 Auctions, ego, and when price can detach from intrinsic value
44:30 Entertainment, media disruption, and evergreen demand for content
49:50 How AI is changing equity research and idea generation
55:40 What AI can see that humans often miss
01:00:30 One lesson for the average investor

In this episode of Excess Returns, we sit down with Mike Green of Simplify Asset Management for a deep dive into how passive investing has reshaped market structure, altered price discovery, and created new sources of systemic risk beneath the surface of today’s equity markets. Mike explains why index funds are not as passive as most investors believe, how daily flows drive prices in increasingly inelastic markets, and why the growth of passive strategies may be pushing markets toward an unstable endpoint. The conversation also explores macro implications, AI-driven capital spending, demographic shifts, and what all of this means for investors navigating the years ahead.
Topics covered
How passive investing and ETF flows actively influence market prices

The inelastic market hypothesis and why markets absorb flows differently than investors expect

Why index funds no longer fit the classic definition of passive investing

The growing share of passive ownership and what happens as it continues to rise

Potential market instability and the theoretical limits of passive dominance

How demographics, retirement flows, and 401k defaults affect market structure

Critiques of arguments downplaying the impact of passive investing

Why large-cap concentration keeps increasing despite slowing fundamentals

Implications for active management, stock selection, and liquidity

The role of AI, capital expenditures, and energy constraints in the macro outlook

What rising electricity demand and infrastructure investment mean for the economy

Housing market distortions, demographics, and long-term structural challenges

Timestamps
00:00 Introduction and why passive investing is not truly passive
03:00 The inelastic market hypothesis explained
06:00 Daily flows, index funds, and price impact
08:20 How much of the market is now passive
11:40 What happens if passive investing keeps growing
14:20 Retirement flows and demographic effects on markets
19:00 Responding to critiques of passive market impact
23:00 Liquidity, concentration, and large-cap dominance
27:00 Why market cap does not equal liquidity
33:00 Active management under pressure
38:00 Current market conditions and early-year rotations
41:50 Economic growth, GDP, and underlying volatility
43:30 AI capex, overinvestment, and market incentives
47:00 Energy, electricity demand, and long-term constraints
52:40 Housing, demographics, and policy challenges

This episode of Excess Returns features Gene Munster and Doug Clinton breaking down their 2026 technology and market predictions, with a deep focus on artificial intelligence, big tech, and where investors may be misreading the current cycle. The conversation explores how far along the AI bull market really is, what fundamentals still support it, and where the biggest opportunities and risks may emerge over the next several years. Munster and Clinton discuss market structure, capital spending, valuation, and technological inflection points across AI, software, hardware, and autonomous driving, offering a grounded but forward-looking framework for long-term investors.
Main topics covered
Why the AI bull market may still have multiple years left and how fundamentals support current valuations

Nasdaq return expectations through 2026 and what earnings and multiples imply for investors

The case for small-cap and non–Mag Seven tech outperforming as the AI cycle matures

Hyperscaler AI capital spending and why CapEx growth could exceed current expectations

Whether AI pricing pressure leads to commoditization or expanding long-term value creation

How AI is changing the economics of infrastructure, platforms, and asset-heavy tech businesses

Apple’s AI strategy, the future of Siri, and why expectations matter for valuation

Alphabet, Amazon, and the evolving AI competition among the largest technology companies

Energy constraints, data centers, nuclear power, and the infrastructure needed to support AI growth

Tesla, Waymo, and the realistic timeline for autonomous driving and robotaxi adoption

How physical AI, autonomy, and robotics could reshape transportation and consumer behavior

Timestamps
00:00 AI cycle outlook and why the bull market may still be early
05:00 Nasdaq return expectations and earnings fundamentals
10:30 Small-cap tech versus Mag Seven performance
17:15 Hyperscaler AI CapEx and Nvidia’s signals
24:00 Infrastructure, pricing power, and AI commoditization debates
32:30 Apple, Siri, and consumer AI assistants
38:50 Alphabet, Amazon, and AI competition among mega-cap tech
45:00 Energy, data centers, and nuclear power considerations
48:10 Tesla, autonomy, and robotaxi timelines
54:15 Waymo, market share, and the future of transportation

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 generations
Timestamps
00:00 Investing is about price, valuation, and early thoughts on AI and market risk
01:54 Personal investing philosophy and why portfolios must be investor-specific
03:00 Diversification, number of holdings, and managing downside risk
05:00 Valuation frameworks and buying companies at the right price
06:00 Stories versus numbers and avoiding the circle of competence trap
08:20 Political risk and why some sectors are hard to value
08:47 Watchlists, patience, and waiting for price to meet value
11:43 When and why to sell stocks as a value investor
12:00 Using probability distributions and simulations in valuation
15:48 Sell discipline, fund flows, and separating skill from luck
18:00 Company lifecycles, aging businesses, and management discipline
23:18 Apple, Meta, and contrasting approaches to AI investment
24:08 AI bubbles, winner-take-all dynamics, and capital cycles
27:48 Infrastructure investing, debt risk, and societal spillovers
32:20 Cross-ownership risks and AI ecosystem fragility
35:00 AI’s impact on profit margins and competition
39:41 Where AI value may accrue over time
44:38 AI tools, valuation bots, and the rise of investment scams
49:17 Private markets, alternatives, and cost structures
53:05 Cash, collectibles, and diversification beyond equities
56:33 Reentering markets after selling and avoiding market timing traps
58:35 Time horizon, legacy investing, and generational wealth