Eric Crittenden joins Matt Zeigler and Jason Buck for a deep dive into trend following and managed futures.

They discuss why systematic macro trend investing works, how risk transfer creates a return premium, and how trend can fit inside a diversified all-weather portfolio.

Standpoint Funds

https://www.standpointfunds.com/

Topics covered:

• Why trend following can struggle during fast reversals and thrive after regime shifts

• How systematic investors manage whipsaws, drawdowns, and emotional pressure

• The trade-offs between short-term, medium-term, and long-term trend signals

• Why Eric prefers simple, durable systems over complex models and constant tinkering

• When it makes sense to remove a futures market from a systematic portfolio

• Why trend following may earn a risk transfer premium from hedgers and commercial users

• How copper producers, options markets, and insurance help explain trend following returns

• Why rising interest rates and short bond positions can benefit managed futures

• How trend following can pair with global equities in an all-weather portfolio

• Why smoothing a trend strategy can reduce its value when investors need convexity most

• The behavioral challenge of holding diversifiers that look wrong at the wrong time

• Why investors and advisors often want alternatives but struggle to stick with them

Timestamps:

00:00 Why trend following opportunities appear under pressure

04:39 Pro-growth positioning before the whipsaw

09:32 Short-term vs long-term trend signals

13:46 The danger of tinkering with systematic strategies

18:43 What actually changes in a durable process

23:27 Rising rates, short bonds, and collateral yield

28:00 Copper hedging and why trend followers buy rising prices

32:00 Options, insurance, and risk transfer through time

36:28 Regime shifts and supply-demand imbalances

41:00 What investors choose when asset classes are anonymized

45:11 Building a portfolio for 30-year terminal wealth

50:06 Why portfolio construction is different than judging individual strategies

56:15 Why trend following and value investing require faith

01:00:42 Reducing errors vs chasing highlight-reel winners

01:05:36 Where to follow Eric and Standpoint

Cliff Asness returns to Excess Returns for a greatest hits tour through some of his most important and entertaining investing ideas.

We discuss bubble logic, today’s AI market comparisons, why volatility still matters as a risk measure, private equity “volatility laundering,” international diversification, market timing myths, pulling the goalie, and how machine learning is changing quantitative investing.

Cliff Asness on X
https://x.com/CliffordAsness

AQR Capital Management
https://www.aqr.com/

Papers Discussed

Bubble Logic: Or, How to Learn to Stop Worrying and Love the Bull
https://www.aqr.com/Insights/Research/Working-Paper/Bubble-Logic-Or-How-to-Learn-to-Stop-Worrying-and-Love-the-Bull

Rubble Logic: What Did We Learn From the Great Stock Market Bubble?
https://www.aqr.com/Insights/Research/Journal-Article/Rubble-Logic

My Top 10 Peeves
https://www.aqr.com/-/media/AQR/Documents/Insights/Journal-Article/My-Top-10-Peeves.pdf

Volatility Laundering
https://www.aqr.com/Insights/Perspectives/Volatility-Laundering

I Did Not Predict What Is Going on in Privates
https://www.aqr.com/Insights/Perspectives/I-Did-Not-Predict-What-is-Going-on-in-Privates

(So) What If You Miss the Market's N Best Days?
https://www.aqr.com/Insights/Perspectives/So-What-If-You-Miss-the-Markets-N-Best-Days

International Diversification Works (Eventually)
https://www.aqr.com/Insights/Research/Journal-Article/International-Diversification-Works-Eventually

International Diversification - Still Not Crazy after All These Years
https://www.aqr.com/Insights/Research/Journal-Article/International-Diversification-Still-Not-Crazy-after-All-These-Years

Perhaps the Most Important Essay I Will Ever Co Author
https://www.aqr.com/Insights/Perspectives/Perhaps-the-Most-Important-Essay-I-Will-Ever-Co-Author

Main topics covered:

• How the dot-com bubble created its own internal logic

• Why Dow 36,000 and Cisco message boards captured bubble thinking

• What investors learned, and failed to learn, from the tech bubble

• How today’s AI market compares with the dot-com era

• Why long periods of underperformance make even good strategies hard to stick with

• Why Cliff still defends volatility as a useful risk measure

• Why “cash on the sidelines” is a misleading market narrative

• How private equity smoothing can make risk look lower than it really is

• Why the private markets debate is not a short-term prediction

• Why the “missing the best 10 days” argument against market timing is incomplete

• Why international diversification can still matter after decades of US outperformance

• What pulling the goalie can teach investors about risk, incentives and career risk

• How machine learning changes quant investing without eliminating economic intuition

Timestamps:

00:00 Why certainty is dangerous in investing
04:58 Why Bubble Logic never became a book
10:18 Cisco, Yahoo message boards and bubble psychology
14:16 Rubble Logic and the lessons investors failed to learn
18:04 What today’s AI market has in common with the dot-com bubble
22:23 Why the long run can lie to investors
26:02 Volatility, permanent loss of capital and real risk control
30:19 Why there is no cash on the sidelines
34:00 Private equity, smoothing and volatility laundering
39:47 Why Cliff did not call the private markets downturn
43:19 The flaw in the missing the best 10 days argument
49:00 Why international diversification still works eventually
53:35 Why crashes are global but lost decades are local
57:30 Pulling the goalie and asymmetric risk
01:01:00 Why coaches and investors avoid optimal decisions
01:07:36 Machine learning, overfitting and economic intuition
01:10:50 Leverage, short selling and derivatives in quant portfolios
01:16:26 Where to follow Cliff Asness

Ben Carlson joins Excess Returns to discuss his new book Risk and Reward and the biggest lessons investors can learn from market history. We cover how to think about risk, inflation, market timing, bear markets, lost decades, diversification, compounding and why surviving volatility is the key to building long-term wealth.

Ben's Book
https://amzn.to/4dFHsQz

Ben Carlson on X
https://x.com/awealthofcs

Ben's Blog
https://awealthofcommonsense.com/

Main topics covered:

• Why risk is hard to define and always involves trade-offs

• How vivid risks like sharks and headlines distort investor decision-making

• Why doing nothing can be one of the hardest parts of investing

• How inflation should be viewed through personal finance, human capital and long-term investing

• Why stocks can be an inflation hedge even if they struggle during inflation spikes

• Why waiting for the market coast to clear often fails

• What the world’s worst market timer teaches about saving and staying invested

• How loss aversion shapes investor behavior

• What the Great Depression, bear markets and 30-year returns teach about long-term investing

• Why there is no perfect portfolio and the best strategy is one you can actually stick with

Timestamps:

00:00 Ben Carlson on why risk and reward are attached

06:35 Doing nothing, action bias and better investing behavior

11:51 Inflation psychology and lessons from the 1970s

16:55 Why stocks can hedge inflation over the long run

21:07 Why waiting for the coast to clear is a market timing trap

26:30 Time horizons, loss aversion and portfolio behavior

31:49 Government rescue, left-tail risk and unintended consequences

35:54 Recessionary vs non-recessionary bear markets

42:09 Why the stock market and economy can diverge

47:24 Why compounding is about holding, not trading

51:37 Starting valuations, lost decades and future returns

55:40 Risk, reward and the biggest lesson for investors

AI is moving from hype to real enterprise adoption, and Gene Munster and Doug Clinton join Excess Returns to explain what that means for investors, technology stocks, energy demand, jobs and the next phase of the AI trade. We discuss why AI may still be early in its bubble cycle, how frontier models like GPT, Claude, Gemini and Grok compare, why AI-powered investing is becoming more practical, and where the biggest second-order opportunities may emerge.

Gene Munster on X
https://x.com/munster_gene

Doug Clinton on X
https://x.com/dougclinton

Deepwater Asset Management
https://www.deepwatermgmt.com/

Intelligent Alpha
https://www.intelligentalpha.co/

Main topics covered:

• Why Doug Clinton still thinks AI could become a bigger bubble than dot-com
• How Claude Code, Codex and frontier AI models are changing enterprise productivity
• The job disruption risk for knowledge workers and why AI adoption may become a survival skill
• Why the AI model race may not be winner-take-all
• How Intelligent Alpha uses large language models to evaluate stocks and earnings expectations
• Why GPT, Claude and DeepSeek perform differently across investing tasks
• The AI infrastructure boom and why energy may be one of the most underappreciated bottlenecks
• Hyperscaler CapEx, data centers and the investment case for continued AI spending
• How major AI IPOs like SpaceX, Anthropic and OpenAI could affect public markets
• Why space, orbital data centers and zero-gravity manufacturing could become real investment themes

Timestamps:

00:00 AI, electricity and intelligence
04:33 Why new AI models changed the semiconductor trade
09:14 What AI means for knowledge worker jobs
14:03 Codex, Claude Code and Google’s AI challenge
18:50 OpenAI, Apple and the model capacity race
23:03 How many frontier AI models can survive?
27:18 Intelligent Alpha’s AI earnings benchmark
31:34 Why AI investors avoid emotional bias
35:33 Where to invest in the AI stack
39:00 Why AI energy demand is still underappreciated
43:43 How markets are judging hyperscaler AI spending
48:00 The investment opportunity in space
52:20 Final thoughts and closing

Jeremy Grantham joins Excess Returns to discuss The Making of a Permabear, mean reversion, market bubbles, AI, the Magnificent 7, and the long-term lessons investors can take from his career at GMO. We cover why he rejects the simple “permabear” label, how he thinks about valuation and bubbles, why AI may be both transformative and dangerous for investors, and why long-term thinking is so hard but so essential.

The Making of a Permabear: The Perils of Long-term Investing in a Short-term World
https://groveatlantic.com/book/the-making-of-a-permabear/

GMO
https://www.gmo.com/americas/

Grantham Foundation
https://granthamfoundation.org/

Topics covered:

• Why Jeremy Grantham thinks the “permabear” label misses the point

• The difference between being generally bearish and making a true “abandon ship” call

• Mean reversion, valuation cycles, and why history still matters for investors

• Why monopoly power helped reshape U.S. profit margins and market concentration

• How AI could turn today’s monopoly winners into brutal competitors

• Why new technology often becomes a cost of doing business rather than a permanent profit boost

• How Grantham defines bubbles using two-sigma market events

• Lessons from Japan, the dot-com bubble, the housing bubble, and the 2021 speculative peak

• Why institutional investors struggle to stick with value strategies during bubbles

• The role of purpose, climate risk, toxicity, and long-term thinking in Grantham’s later career

• The one lesson Grantham would teach ordinary investors about pessimism, realism, and time horizons

Timestamps:
00:00 Jeremy Grantham on unpleasant news and long-term investing
04:18 Reinvesting when terrified in 2009
08:43 Why Grantham told investors to abandon ship in 2008
10:28 Mean reversion and why history matters
14:00 Monopoly power, the Mag 7, and rising market concentration
17:14 Why AI is important but impossible to forecast
20:21 AI as a cost of doing business
21:24 From monopoly profits to brutal AI competition
24:05 How investors should think about valuation mean reversion
27:00 Why high returns on capital should eventually attract competition
29:47 How Grantham defines a market bubble
33:00 Japan’s extreme bubble and GMO’s zero weight decision
34:19 The dot-com bubble and the pain of being early
38:00 Grantham’s bubble warning signal in 2021
41:35 Whether today’s market is showing classic bubble behavior
43:00 QuantumScape, meme stocks, and speculative excess
46:35 How ChatGPT interrupted the 2022 bear market
49:12 Investor behavior and the cost of underperforming in a bubble
55:00 Purpose, philanthropy, climate risk, and useful work
01:01:03 The one lesson Grantham would teach average investors

Marc Rubinstein joins Excess Returns to explain what private credit, bank earnings, insurance balance sheets, fintech growth, and arbitrage firms reveal about the modern financial system. The conversation covers why private credit risks may not be systemic in the traditional banking-crisis sense, but still matter for investors because of redemption gates, hidden leverage, opaque structures, incentive conflicts, and correlations that can spike when markets are under stress.

Marc Rubinstein on X
https://x.com/MarcRuby

Net Interest
https://www.netinterest.co/

In this episode, we discuss:

• Why the Fed says private credit redemption risks are limited and manageable

• What Blue Owl’s redemption gates reveal about private credit liquidity

• How post-2008 bank regulation pushed risk into private credit, hedge funds, trading firms, and exchanges

• Why banks and private credit firms are both competitors and collaborators

• The “layer cake” of leverage connecting banks, private credit, and borrowers

• How HSBC’s loss tied to Atlas and MFS highlights hidden credit risks

• Why insurance companies have become increasingly tied to private credit

• Why rapid growth can be dangerous in financial businesses

• What bank earnings show about the gap between weak consumer confidence and resilient spending

• Why post-mortem reports from SVB, Credit Suisse, and other failures reveal what investors could not see in real time

• How Revolut became one of the most interesting fintech stories in global banking

• Why Marc calls this a potential golden age of arbitrage

• What Jane Street, public BDC discounts, private asset valuations, and geopolitical fragmentation tell us about market structure

• Why investors may still be too anchored to the 2008 banking playbook

• Where Marc sees risk and opportunity in financials, banks, Europe, and non-bank financial institutions

Timestamps:

00:00 Private credit, hidden risks, and correlation spikes
05:03 Why Blue Owl became a private credit warning sign
10:20 How private credit grew after the 2008 financial crisis
15:30 Banks and private credit as financial “frenemies”
19:44 HSBC, Atlas, MFS, and the layer cake of leverage
24:11 Apollo, Athene, insurance assets, and private credit incentives
29:20 Why higher rates have not broken more of the financial system
33:40 Bank earnings, consumer confidence, and resilient spending
37:20 Why “I don’t know” can be a powerful signal from bank CEOs
41:46 Revolut and the ambition to build a truly global bank
47:38 Why growth can be dangerous in finance
52:19 Private assets, public BDC discounts, and arbitrage opportunities
56:34 What investors misunderstand about banks today
59:31 How Marc would think about financials as a long-short investor

First Principles with Andy Constan launches with a deep dive into market bubbles, AI, semiconductor stocks, and the financial conditions that can turn powerful technological change into a dangerous investment regime. Andy explains how bubbles form, why they are almost impossible to time, how today’s AI boom compares to past episodes like 1987, the dot-com bubble, housing, and the bond bubble, and what investors should watch as expectations, financing, and FOMO build.

Andy Constan on X
https://x.com/dampedspring

Damped Spring Advisors
https://dampedspring.com/

Topics covered:

• Why bubbles are easy to identify in hindsight but nearly impossible to define in real time

• The difference between an expensive market and a true bubble regime

• How new technologies, easy money, regulation, and exogenous shocks can create bubble conditions

• Why AI may rhyme with the internet boom without being an exact repeat

• The role of ChatGPT, Microsoft’s OpenAI investment, and semiconductor earnings expectations

• What the 1987 crash, Japan, housing, bonds, and dot-com bubble can teach investors today

• Why human nature, FOMO, and “keeping up with the Joneses” make bubbles so powerful

• How the late-1990s Fed response to Long-Term Capital Management helped fuel the final phase of the tech bubble

• Why tech’s current size in the economy and market may limit how far the AI boom can grow

• How AI capex, hyperscaler spending, buybacks, debt issuance, and IPO supply could determine what happens next

Timestamps:
00:00 Intro and the challenge of identifying bubbles
04:32 Expensive markets vs true bubble regimes
09:57 The five bubble episodes Andy compares to today
14:35 Root conditions, escalation events, and the peaking phase
19:20 Why the 1987 crash may also have been a bubble
24:25 The late-1990s setup and the Netscape Navigator moment
28:00 Crisis analogs, easy financial conditions, and today’s AI parallels
32:20 Long-Term Capital Management and rocket fuel for the tech bubble
36:11 Why tech’s market share matters more today than in the 1990s
43:18 Policy mistakes, subsidies, and how governments feed bubbles
47:42 Semiconductor earnings expectations and valuation risk
53:45 The AI capex chain and where the money has to come from
58:42 IPOs, corporate debt, and the financing risk behind the AI boom
01:02:27 What investors should do differently in a bubble regime

Edward Chancellor joins Kai Wu on the latest episode of the Intangible Economy to discuss what financial history and capital cycle theory can teach investors about today’s AI boom. They explore why transformative technologies can still produce terrible investor returns, how overinvestment develops, where anti-bubbles may be forming, and what past episodes like the railway mania, the dot-com bubble, China’s investment boom and the post-2008 interest rate regime suggest about the risks and opportunities today.

Subscribe on Spotify⁠

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Topics covered:

• How capital cycle theory applies to the AI data center boom

• Why railway mania, autos, aircraft and the dot-com bubble offer lessons for today

• Why markets often fund major technology transitions but fail to identify the winners

• The prisoner’s dilemma driving hyperscaler AI spending

• Whether AI demand can justify the supply being built

• How GPU depreciation and AI capital spending may affect reported earnings

• Why hallucinations and reliability may limit the total addressable market for large language models

• The case for looking at AI anti-bubbles instead of shorting the bubble directly

• Why China shows that strong GDP growth does not guarantee strong shareholder returns

• How intangible capital, SaaS valuations and human capital fit into capital cycle analysis

• Whether bubbles can be good for society while still being bad for investors

• Why the long-term interest rate cycle may have changed

• The role of gold in a world of expensive stocks, rising debt and vulnerable bonds

Timestamps:

00:00 Edward Chancellor on capital cycles, bubbles and AI
04:42 Why the railway mania became a classic overinvestment cycle
09:00 Why markets fund technology booms but often miss the winners
13:19 The prisoner’s dilemma behind AI spending
17:30 Will AI demand justify the supply being built
20:00 How capital spending can inflate profits before the bust
25:08 The AI Hindenburg moment and the limits of large language models
30:55 Why AI hype may exceed the proven technology
35:55 Why the anti-bubble may matter more than shorting AI
40:00 The energy transition bubble and the opportunity in overlooked assets
45:08 China’s lesson on GDP growth and shareholder returns
49:27 Big Booze, GLP-1s and the Lindy effect
54:23 Can intangible capital have its own capital cycle
59:54 SaaS valuations and the index creation warning signal
01:04:10 Why bubbles can help society but hurt investors
01:09:09 Why long-term rates may be in a new multi-decade cycle
01:14:07 Why Edward Chancellor still sees a role for gold

Brent Kochuba of SpotGamma joins Jack Forehand for the May 2026 OPEX Effect to break down what options positioning is saying after a massive AI and semiconductor-led market rally. They discuss SPX call volume, zero DTE options, dealer gamma, VIX expiration, NVIDIA earnings, oil risk, AI CapEx, and why options flows may help explain both the market’s recent melt-up and the potential for a volatility shift after OPEX.

Guest Links

Brent Kochuba on X
https://x.com/spotgamma

SpotGamma
https://spotgamma.com/

Topics Covered

• Why the market has ignored oil shocks and geopolitical risk while AI earnings dominate investor attention

• How AI CapEx, semiconductors and mega-cap tech have driven a powerful melt-up in stocks

• Why options volume and zero DTE trading are increasingly important for all investors

• How dealer hedging, delta and gamma can affect stock market moves

• Why options expiration can create short-term turning points in markets and volatility

• What the May OPEX setup says about call-heavy positioning in the S&P 500

• Why single-stock options activity in NVIDIA, Tesla, Apple, Amazon and AI-related names matters

• How record SPX call volume is being driven by short-dated options flows

• Why Brent is watching VIX expiration, NVIDIA earnings and May 19 to May 20 for volatility expansion

• What oil, VIX, correlation and dispersion are signaling about market risk

Timestamps

00:00 Intro: SPX call volume, call-heavy positioning and transient options flows
00:57 Are we in melt-up mode?
05:29 AI, UFOs and how fast market narratives are changing
09:00 Why options flows matter more for everyday investors
13:39 Could SpaceX become the next huge options market?
16:00 How dealer hedging, delta and gamma move through the market
20:44 Why OPEX can become a turning point for stocks and volatility
23:22 Why May OPEX is so call heavy
28:07 The market rally into May expiration
33:00 AI rebranding, meme behavior and downside headline risk
36:07 Reviewing last month’s oil and volatility setup
40:17 How the war flipped market leadership back to tech
44:13 Dealer gamma support in the S&P 500
49:19 Single-stock gamma in NVIDIA, Tesla, Apple and Amazon
51:06 Record SPX call volume and the role of zero DTE
54:55 Semiconductor, AI and memory call volume
57:50 From bearish positioning to peak-bull dispersion
59:22 Oil, the S&P 500 and changing correlations
01:03:06 COR1M, dispersion risk and when Brent considers hedging
01:04:57 Brent’s key takeaways for May OPEX and volatility expansion

Elena Khoziaeva, Co-Chief Investment Officer and Portfolio Manager at Bridgeway Capital Management, joins Excess Returns to discuss factor investing, small caps, value investing, market concentration, intangibles, passive investing, market neutral strategies, and the role of AI in quantitative investment research.

We cover how Bridgeway combines disciplined quantitative models with human judgment, why the S&P 500 may be less diversified than investors think, and how investors can think about diversification when mega-cap growth stocks dominate market returns.

Bridgeway Capital Management
https://bridgeway.com/

I Know What You Did Last Summer
https://bridgeway.com/perspectives/i-know-what-you-did-last-summer/

How Many Stocks Are Effectively in the S&P 500?
https://bridgeway.com/perspectives/how-many-stocks-are-effectively-in-the-sp500/

Topics Covered

• Why quantitative investing still needs human judgment and skepticism

• The difference between smart beta and true multi-factor portfolio construction

• How Bridgeway combines value, quality, sentiment and risk controls

• Why the size premium may depend on how small-cap stocks are defined

• Why recently fallen large caps and IPOs can distort small-cap research

• How the small-cap universe has changed as companies stay private longer

• How intangible assets affect traditional value and quality metrics

• Why value can work in bursts and why timing factor rotations is so difficult

• How concentrated the S&P 500 has become using the HHI framework

• Why passive investing may create opportunities for active small-cap managers

• How market neutral strategies can help investors manage equity market volatility

• How AI can help with data, text analysis and trading without replacing investment judgment

Timestamps

00:00 Why fewer than 50 stocks are driving S&P 500 returns
01:04 Bridgeway’s evidence-based investing approach
02:59 Why quantitative models need human judgment
07:52 Smart beta vs multi-factor investing
11:32 How Bridgeway builds multi-factor portfolios
16:08 Rethinking the size premium
20:31 Has the small-cap universe gotten worse?
23:49 How intangibles change value investing
28:05 Does value still work?
30:09 Why value returns can be episodic
33:11 Why factor investors need patience
35:22 How concentrated is the S&P 500?
40:29 Factor strategies as portfolio diversifiers
41:41 Passive investing and market structure
44:27 Managing volatility with market neutral strategies
49:40 How systematic managers update their models
55:02 How Bridgeway is using AI
01:00:03 Elena’s biggest lesson for investors