Maxime Duffort

Rothschild & Co is pleased to serve as exclusive financial advisor to Nxtfood (ACCRO | Alternatives végétales) on its €49m fundraising led by CREADEV - Creating for People alongside Clay Capital, Roquette Ventures and IRD INVEST #GlobalAdvisory #plantbased #frenchtech #foodtech #Bcorp #Consumer #Fundraising

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Tikehau Capital is pleased to announce the launch of Tikehau European Private Credit (TEPC), the Group's first ELTIF 2.0 semi-liquid private debt fund for private investors accessible outside French life insurance. 📌 TEPC will finance the growth of a wide range of profitable mid-sized European companies, with the aim of offering investors an attractive risk-return profile. 📌The launch of this fund is part of the Group’s ongoing efforts to democratise access to unlisted assets with the aim of making these strategies accessible to a broader audience. 📌 TEPC will leverage Tikehau Capital's Credit platform, one of the largest in Europe, with over 50 investment professionals and €23.3 billion of assets under management. The fund will co-invest alongside the Group’s institutional credit vehicles, which has invested nearly €70 million of its own balance sheet in the vehicle. Read the press release (EN) 👉 https://lnkd.in/ez9dGs9Z Read the press release (FR) 👉 https://lnkd.in/eEM_NbRe #privatemarkets #credit #assetmanagement #privatedebt

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💥 New Deal 💥 We’re proud to announce our investment in Genesis AI’s $105M Seed round, alongside Eclipse, Khosla Ventures, HSG, Alven, Eurazeo, Drysdale Ventures, Eric Schmidt & Xavier Niel ! 🚀 Half of global GDP is physical. Less than 5% of it is automated. Today’s robots are narrow, rigid, and too expensive to scale. Genesis AI is here to fix that. Large language models are trained on vast datasets of text, but AI models for robotics must be trained on data on the physical world. However, acquiring that real-world data makes for a costly and time-consuming endeavor. To overcome that, Genesis has built a proprietary simulation engine capable of accurately modeling the physical world (already +25k stars on Github) in order to train a robotics foundation model capable of controlling any type of robot for any task. 🤖 Zhou Xian, Theophile Gervet and their all all-star team are uniquely positioned to rise to the challenge of unlocking unlimited physical labor & we are super excited to support them 🦾 Joy Boustani Thibault Passaga Véronique Jacq Paul-François Fournier Nicolas Dufourcq #AI #robotics #frenchtech #foundationmodel

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Credit swaptions: pricing and strategies Very happy to teach once again the course 'Quantitative Strategies Applied to Credit' which is part of the Master 2 Probability and Finance at Sorbonne Université in collaboration with Julien Turc. The credit market offers a wide range of instruments and possible strategies for investors along with numerous challenges for quants. Credit index swaptions can be used to design various types of strategies, such as hedging strategies to protect against a widening of credit spreads or volatility carry strategies to capture the spread between implied and realized volatility in the credit market. But pricing models to value credit swaptions can become quickly complex and very time consuming to compute as the price of the instrument is a function of the joint distribution of the level of future spreads and the number of defaults. Pedersen (2003) proposed a simplified framework using for the default risk an estimation of the expected loss and assuming that forward spreads follows a lognormal distribution. In this framework the price of the swaption can be obtained with a simple integral. What do you think? Nicole El Karoui Gilles Pagès Huyên Pham

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Just finished updating the slides for this Friday’s lecture on Quantitative Investing at Università di Brescia, where I’ll spend the day with Master in Finance students exploring a broad range of systematic trading strategies — from cross-sectional to intraday. We’ll start with factor-based investing, presenting empirical examples such as momentum, low volatility, value, and other characteristics that have historically delivered significant alpha. For this part, we’ll use our new Factor Tracker, a free tool developed by Concretum and now available on our website. Next, we’ll shift to time-series models, introducing the logic and long-term results of classic trend-following strategies applied to futures markets. As a case study, I’ll share recent findings from our research on timing crypto markets, published in Catching Crypto Trends. We’ll then move into volatility trading, from simple straddle portfolios to more advanced instruments like variance swaps. We’ll discuss why implied volatility tends to be overpriced, and how short-volatility exposure can be harvested — provided it’s managed with robust risk controls. I’ll also present a daily rebalanced, dynamic long-short strategy that systematically times the volatility risk premium using VIX-linked ETPs. To close the day, we’ll explore intraday trading signals in US equity markets, with a focus on the logic and Python implementation of one of our most effective high-frequency models currently running live. It’s always a pleasure to return to the classroom and an honor to teach in a university located in one of the oldest academic cities in Italy. A special thanks to Prof. Ignazio Giorgio Basile for the renewed invitation to his course and the opportunity to contribute again this year.

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Venture capital (VC) funds and the funding of innovation At the last meeting of the research committee of Autorité des marchés financiers (AMF) – France (https://lnkd.in/en63xcV8) – I discussed the paper by Maxime Bonelli on VC funds (https://lnkd.in/eXMwEDET), which raises an important question: will VC funds continue to support innovative startups with the advent of machine learning (ML) screening technologies? The paper shows that VC funds using ML techniques become better at screening “safe horses” (companies with business models mirroring existing ones), but falter at discovering “future stars” (highly innovative companies poised for IPOs). The paper concludes with a concern: relying more on ML could potentially stifle the financing of groundbreaking ideas. I am not sold on this conclusion (yet). Let’s consider two segments of the startup market : “safe horses” and “future stars”. Both segments offer positive expected returns, albeit with diseconomies of scale—the higher the funding, the lower the return. With ML, it becomes easier to screen “safe horses”, which increases their expected return relative to “future stars”. As the funding of “safe horses” becomes more attractive, how do we restore equilibrium? The paper argues that VC funding flows out of “future stars” towards “safe horses” until the expected returns in both segments equalize. As a result, we have less funding for innovation. But this scenario is just one of many paths towards equilibrium. One possibility is that there are few barriers to using ML. As new VC funds enter the “safe horses” segment, returns equalize without any drop in funding for innovation. Another possibility is that the diseconomies of scale in the startup market are strong (i.e., a small change in funding has a big impact on return). In this case, returns equalize with very little change in VC funding for innovation. Thus, the effect of ML on innovation funding heavily depends on the VC industry’s structure, making it challenging to predict at this stage. For more insights (including my methodological concerns), see my detailed discussion below. And to conclude: Maxime is the winner of the 2024 AMF prize for young researchers in economics (congrats again Maxime !)

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