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Fund manager memos, market outlooks, and interviews; read for the framework, not the conclusion.
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In his latest memo, Howard Marks explores recent attempts to suppress long-term government bond yields. He states clearly that the only sustainable solution is to address the underlying factors driving interest rates higher, even if such measures face political resistance. Any other approach is essentially an attempt to defy the laws of economics, and such attempts have never succeeded.
Read originalNYU Professor Aswath Damodaran reviews the backdrop of rising U.S. Treasury yields in 2026, noting that the 10-year Treasury yield rose from 4.18% at the start of the year to 4.75% by the end of August, while 20-year and 30-year Treasury yields surpassed 5% during the year. He also compares the long-term trend of U.S. Treasury yields since 1962, as well as concurrent changes in government bond yields across multiple currencies, including the euro, Japanese yen, and British pound.
Key Takeaways:One can learn to construct an intrinsic risk-free rate using the inflation rate plus real GDP growth, and combine it with changes in corporate cash flows to break down the differentiated impact of rising interest rates on stock valuations across industries.
AQR co-founder and dean of Northwestern University’s Kellogg School of Management co-authored an article pointing out that AI can automate most analytical and technical capabilities in quantitative investment, making creativity and emotional intelligence enduring competitive advantages that are difficult to replace. AQR is leveraging the Quanta Academy, launched in 2015, to expand relevant capability development, while Kellogg School of Management also promotes students’ simultaneous development of analytical skills and soft skills through programs such as MBAi. Research shows that the impact of AI on empathy is not fixed, and depends on tool design choices and organizational culture building.
Read originalRob Carver has completed the final edits on his fifth trading book, "The Art And Science of Trading," scheduled for official publication on December 1 and now available for pre-order. Aimed at the mass market, this book serves as the unleveraged counterpart to his third book, "Leveraged Trading." It covers an introduction to trading delta-one instruments such as stocks, ETFs, and cryptocurrencies, explains trading science concepts like backtesting and the Kelly Criterion, and explores the integration of art and science within a semi-automated trading framework.
Read originalEpisode 125 of Deribit's Crypto Options Unplugged podcast, hosted by Imran Lakha and David Brickell, explores whether pressure in the global bond market could serve as a catalyst for Bitcoin's next major move.
Read originalAswath Damodaran, a professor at NYU Stern School of Business, writes that the current venture capital industry generally prioritizes corporate expansion over profitability, a tendency that has become more pronounced over the past two decades. He breaks down six core factors influencing a company's expansion potential, including market size, market growth rate, industry structure, capital intensity, customer inertia, and dependence on key individuals. He also identifies the key variables determining a company's profitability feasibility as unit economics, economies of scale, and competitive barriers.
Key Takeaways:You can master an analytical framework that distinguishes between a company's expansion potential and profitability feasibility, using market size, unit economics, economies of scale, and competitive barriers to judge whether a startup's expansion choices are reasonable.
NYU valuation professor Aswath Damodaran published a lengthy article proposing a four-stage framework for revolutionary change, arguing that AI is currently entering its coming-of-age phase, shifting from investment and infrastructure building to commercial implementation. The article notes that since the release of ChatGPT in November 2022, the Mag Seven contributed 45% of the total increase in U.S. stock market capitalization from 2022 to 2025. Six leading AI companies have cumulatively spent $1.7 trillion in capital expenditures, while the current upper bound of total revenue from AI products and services is only about $250 billion.
Read originalAswath Damodaran, a valuation professor at New York University, uses the complete journey of Leopold Aschenbrenner's Situational Awareness hedge fund—from explosive growth to liquidation following a blowup—as a case study to systematically break down the definition, sources, and real-world impact of investment conviction.
Read originalBridgewater has published an AI policy commentary arguing that AI is the most impactful technological change of our time, and that policy must be enacted immediately to balance its productivity benefits against risks of social disruption and safety incidents. Bridgewater proposes that US AI policy must prioritize societal resilience to technological shocks and establish a robust AI safety regulatory framework; otherwise, AI development will be forced to stall due to political unsustainability or major incidents.
Read originalAswath Damodaran, a professor at New York University's Stern School of Business, wrote an article analyzing the SEC's proposal to replace quarterly reports with semi-annual reports alongside new Federal Reserve Chair Kevin Warsh's proposal to reduce forward guidance, arguing that both are essentially reducing the amount of public information that markets have become accustomed to receiving.
Read originalNYU Professor Aswath Damodaran analyzes whether SpaceX, OpenAI, and Anthropic should be included in the S&P 500 after their IPOs. He breaks down index construction, including constituent selection, weighting methods, and index unit conversion mechanisms. He notes that the S&P 500 uses free-float market capitalization weighting, and that the June 15, 2026 closing level of 7554.29 corresponds to an index unit of 0.1190.
Read originalThe article points out that data mining and AI-driven 'quant by feel' are identical in essence and philosophy, both attempting to skip the core step of understanding how markets work and profit directly from the market. A genuine trading edge requires identifying the counterparty being profited from, the reason they continue to pay, and the rationale for one's own ability to participate in the trade; such structural edge cannot be identified through backtesting optimization alone.
Read originalIn his latest memo, Howard Marks outlines the evolution of the sub-investment grade credit market from the 1970s to the present, focusing on analyzing the drivers behind the rapid growth of direct lending after the global financial crisis, as well as the related issues brought by the rapid deployment of large-scale capital. He points out that the development of direct lending is deeply tied to that of private equity, and the performance of private equity portfolio companies is the core determinant of whether direct loans will succeed in the future.
Read originalDrawing on two academic papers, the author identifies three structural limitations of using LLMs in trading research: First, training data is saturated with trading common sense and lacks mechanism-driven edge thinking. Second, across 35+ models, accuracy in time-series value tracking declines logarithmically as the number of historical updates increases, and neither prompt engineering nor expanding the context window can fix this. Third, outputs from 25+ models on open-ended questions show 71%–82% similarity, and models with different architectures converge to the same common answers.
Read originalIn a special episode of The Insight: Conversations, Howard Marks shares his reflections on themes from Oaktree Capital's recent client conference. The discussion covers the evolution of credit markets, the nature of asset mispricing, and the importance of leadership.
Read originalThe author notes that AI has dramatically lowered the barrier to developing quantitative strategies, enabling fully coded and backtested strategies to be generated within minutes. However, cheap experimentation amplifies the multiple-testing problem, producing a flood of false backtest results that appear effective but are actually overfit to noise.
Read originalBridgewater Co-Chief Investment Officer Greg Jensen and AIA Labs Chief Scientist Jas Sekhon released a viewpoint arguing that Google Gemini 3 is currently the strongest publicly available general-purpose large AI model, with pre-training compute at least 2–3 times higher than GPT-4o, validating that the path of scaling pre-training remains sustainable. This development will drive the AI industry to continue increasing investment in compute, chips, and power, with significant implications for capital markets and the economy.
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