Walter Maciel

Man AHL publishes outstanding research on the #CTA space. No question. They just wrote a fascinating analysis of dispersion among CTAs and different #trendfollowing models. Short answer: diversify. (My answer: replicate 😀) My only quibble is that the model numbers seem off by, well, a lot. Over 25 years, the average Sharpe ratio (see below, vertical axis) seems to be around 0.75. The awkward reality is that the actual Sharpe ratio of the SG CTA Trend index is 0.36. Assuming a 12% average vol, 0.4 of "missing" Sharpe ratio is 500 bps a year. 🤯 If I'm reading the cumulative log returns chart correctly, 18 of the twenty models have outperformed actual #hedgefunds; pick the best one (long term, alts markets, naturally) and you made 13x your money over 25 years, a mere 4-5x the index. 🚀 🚀 🚀 🚀 🚀 Given that they have five (!!!) co-authors, it's entirely possible that I'm missing something. If so, someone poke a hole and I'll eat humble pie. 🥧 If not, then the most likely explanation is that true implementation costs are consistently higher than managers publicly estimate. Here's a link to a recent note I wrote that shows how higher than expected implementation costs might be a factor in why #ETFs have been outperforming hedge funds and #mutualfunds: https://lnkd.in/e6XKyQfn Here's a link to Man's paper: https://lnkd.in/eMBWMnRe

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Macro vs. VIX: is market becoming complacent with the economic backdrop? 🚨 A recent analysis we did with Selbi Muhammetgulyyeva reveals a concerning disconnect worth monitoring: while the VIX ("fear gauge") sits comfortably at ~15%, turning macro data into a "macro VIX" points to a higher level of risk, in the region of 20%+. Figure 3 illustrates this widening gap between perceived risk and fundamental risk - a pattern that has historically preceded significant market events. Similar divergences appeared before the dot-com crash, 2008 crisis, and pandemic turbulence. This suggests markets are currently pricing a non-threatening environment while economic data indicates a more elevated stress levels. The summer months ahead could prove decisive: either economic data improves to match sentiment, or markets will adjust to reflect reality. This sounds like a call for investors who wish to remain into markets to hold a decent level of diversification, neglecting no asset class, inclusive of emerging equities, cash and different types of bonds. Read the full analysis on Lombard Odier Investment Managers website here: #MarketInsights #VolatilitySignals #InvestmentRisk #MacroAnalysis

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Scott Bessent signaled that the U.S. is prepared to support Argentina amid its economic crisis, viewing the country as a key ally in Latin America. He outlined possible measures—such as swap lines, dollar purchases, and using the Treasury’s Exchange Stabilization Fund to buy Argentine debt—showing Washington’s seriousness. The support is linked to President Javier Milei’s reform agenda, which Bessent praises for its focus on fiscal discipline and growth. U.S. backing could stabilize the peso, restore investor confidence, and counter China’s influence in the region. However, it carries risks: taxpayer exposure, Argentina’s history of failed reforms, and the danger of dependency. Bessent stressed that U.S. aid will only come with expectations of real follow-through from Buenos Aires, making success dependent on Argentina’s political and economic discipline. Source: StockMarket.news

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The Past, Present, and Future of Systematic Fixed Income with Jeffrey Rosenberg, CFA. (Links in comments below...) 🏦 From sell-side roots to buy-side breakthroughs: the evolution of quant fixed income 📉 Lessons from the early-2000s crises and the GFC: how systematic strategies were reshaped 🆚 The distinct complexity of fixed income vs equities—signal mining, transaction costs, liquidity 🔍 Factor investing in credit: uncovering value, quality, and intelligent signal categorization ⚖️ Pure alpha ambitions: managing factor exposures and deploying smart tools for returns 🌊 ETFs' role in enhancing liquidity—and their ripple effects on strategy viability In this episode I speak with Jeffrey Rosenburg, Managing Director at BlackRock where he leads active and factor investments for mutual funds, ETFs, and institutional portfolios for the Systematic Fixed Income team. In the first half of the conversation we discuss the history of quant fixed income. Specifically, its evolution within the halls of sell-side institutions and how solutions were shaped by demand for underwriting, securitization, and derivatives. We then make the leap to the buyside, where Jeff outlines the topology of systematic fixed income solutions at BlackRock. We quickly dive into the details, discussing topics such as: why factor investing exists predominately in the credit space, why characteristic specificity within the fixed income space is so important, why quant fixed income needs more PMs but fewer researchers than quant equity, how ETFs changed the liquidity landscape, and whether the equity pod-shop model is possible for fixed income. What ultimately becomes clear, through both explanation and example, is that while the terms and ideas of systematic fixed income will be familiar to those in the quant equity space, the Devil lies deeply in the details of implementation. I hope you enjoy my conversation with Jeff Rosenberg.

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Valuation extremes do matter Deutsche Bank's Jim Reid nailed it again! The chart depicts S&P 500 Shiller cyclically-adjusted P/E ratio (CAPE) and 10-year subsequent nominal and real inflation-adjusted total returns from valuation extremes, both peaks and bottoms. Investment implications for investors are unequivocally clear: Valuation extremes do matter for the mid- to long-term investment horizon! As you can see, all four valuation bottoms were followed by triple-digit 10-year subsequent returns, both nominal and real, while the last three major valuation peaks were followed by negative 10-year subsequent real inflation-adjusted returns. S&P 500 CAPE stands currently at 38x which corresponds with the 98th percentile across the whole time-series since 1881, indeed a valuation extreme again. Let’s see how the current market cycle will play out in the upcoming years.

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