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

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.

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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.

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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

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

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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.

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In this episode of Excess Returns, Graeme Forster of Orbis joins us to discuss two major research papers: Six Courageous Questions for 2026 and Sunrise on Venus. We explore how long-running global trends may be reversing, what that means for U.S. dominance, the future of international and emerging markets, the risks and opportunities created by AI and massive CapEx spending, the dollar’s shifting role, and how investors should think about valuation, humility, and navigating a world where the economic “water” is changing. This conversation is packed with global macro insight, long-term investing lessons, and practical frameworks for building more resilient portfolios.

Topics Covered:
• Why long-term market “water” becomes invisible to investors
• Self-reinforcing global cycles and how China’s WTO entry reshaped the world
• Signs the 25-year U.S. outperformance cycle may be breaking
• How tariffs, political shifts, and corporate reforms change the global landscape
• Why international and emerging markets may now offer better expected returns
• Why U.S. large caps are not the entire story of American exceptionalism
• How to think about valuation, margins, and discounted cash flow models across markets
• The AI boom, bubbles, capital cycles, and asymmetric outcomes
• How AI CapEx constraints influence winners and losers
• The shifting role of the U.S. dollar and why market shocks may behave differently
• Maslow’s hierarchy, needs vs. wants, and the return of state-driven capital investment
• Deglobalization, reshoring, and the national-security lens for investing
• How to evaluate China and Taiwan inside emerging markets
• Why humility is an investor’s greatest edge

Timestamps:
00:00 Introduction
01:02 Why Orbis wrote Six Courageous Questions for 2026
03:44 The David Foster Wallace “water” analogy and investing
06:12 How a 25-year self-reinforcing cycle powered U.S. outperformance
10:12 Signs the cycle may be breaking
12:00 Corporate reform and opportunity in Asia
13:55 Why active share, benchmarking, and incentives distort investor behavior
17:31 Decomposing S&P 500 returns: margins, valuations, fundamentals
20:20 Expected returns inside and outside the U.S.
22:34 Why international stocks offer richer opportunity sets
24:25 Currency implications and weakening dollar dynamics
26:18 American exceptionalism beyond the top 10 mega caps
28:49 Where Orbis is finding value today
30:25 Biotech, healthcare, and post-COVID dislocation
31:05 How Orbis thinks about valuation in an intangible-heavy world
32:09 Is AI a bubble or the beginning of something bigger?
34:30 Game theory of AI CapEx and right-tail outcomes
36:00 CapEx cycles, history, and who benefits
38:00 Indirect AI beneficiaries and the SK Square example
40:35 Maslow’s hierarchy and the shift from wants to needs
42:32 Deglobalization, national security, and domestic reinvestment
44:00 Capital returning to home markets and strategic industries
46:00 Can anything reverse these structural trends?
48:00 Balancing bottom-up investing with macro awareness
49:45 The deeper risk in emerging markets: owning vs. avoiding
51:00 Valuation still matters for long-term returns
52:29 Corporate behavior, dividends, and re-rating cycles
53:52 How Orbis views China vs. bottom-up opportunity
55:34 Why great investors must be right 90–95% of the time in decision quality
58:00 One lesson Graeme would teach the average investor

James Grant, legendary founder of Grant’s Interest Rate Observer, joins us for a wide-ranging conversation on cycles, interest rates, inflation, credit, the Federal Reserve, private markets, gold, and the future of investing. Grant brings five decades of historical perspective to today’s market extremes, explaining why this era of ultra-low interest rates created distortions that will shape returns for years to come — and where patient investors may ultimately find opportunity.

Topics Covered
• The historical patterns that define major market cycles
• Why interest rate cycles unfold over generations
• What the 2021 bond market top tells us about the next decade
• How inflation behaves like an underground coal fire
• The shift from “capitalism without capital” to the “tangible twenties”
• Geopolitical tension, military spending, and inflation risk
• The Fed’s role in shaping today’s market distortions
• The long-term consequences of QE and financial repression
• Private credit, opaque marks, and the fragility beneath the surface
• Rising risks inside life insurance balance sheets
• Why credit cycles always go further than anyone expects
• The challenge of finding long opportunities in today’s market
• Why liquidity and patience may be the biggest opportunities
• Whether the classic 60/40 portfolio still works
• Gold as money and why confidence in paper currencies is eroding
• Jim Grant’s one lesson for the average investor

Timestamps
00:00 Cycle extremes and market absurdities
01:00 Interest rates over generations
07:00 Defining major tops and bottoms
12:30 Where we are in the current rate cycle
14:00 Inflation, armed conflict, and tangible investment
18:00 The “tangible twenties” and data center boom
19:00 Coal fire inflation analogy
20:00 Fed independence, politics, and monetary power
25:00 The long shadow of the 2008 crisis
30:00 QE, zero rates, and long-term consequences
33:00 Housing affordability and locked-in rates
34:00 Risks in private credit and opaque marks
36:00 How far the credit cycle has progressed
38:00 Japan, value investing, and long cycles
43:00 Where opportunities exist today
47:00 The future of the 60/40 portfolio
49:00 Structural risks from low-rate distortions
51:00 Freedom, politics, and economic consequences
56:00 Gold as money
58:00 What Jim Grant believes most investors disagree with
59:30 The one lesson Jim Grant would teach the average investor