We just posted an update of our Large-Orders-in-Small-Markets paper. The paper models execution of large uninformed orders in the presence of strategic market makers. You find the new version of the paper here: lnkd.in/drre-kn

"Machine Learning And Data Sciences For Financial Markets" bit.ly/MLDSFM2023 shows how techniques should be used (1) in conjunction with knowledge of financial markets, (2) keeping a "model-driven mindset". Chapter 2 explains why markets are themselves a learning machine

"Machine Learning And Data Sciences For Financial Markets" bit.ly/MLDSFM2023 shows how modern techniques should be used (1) in conjunction with existing knowledge of financial markets, (2) keeping a "model-driven mindset". Chapter 3 focuses on high frequency finance

Machine Learning And Data Sciences For Financial Markets bit.ly/MLDSFM2023 shows how modern techniques should be used in financial markets. Chapter 4 gives all that is needed to build portfolios using modern techniques, including statistical arbitrage using neural networks

"Machine Learning And Data Sciences For Financial Markets" bit.ly/MLDSFM2023 shows how modern techniques should be used in conjunction with existing knowledge of financial markets. This chapter shows how to use ML and GANs to solve control problems, PDEs, and trading games

"Machine Learning And Data Sciences For Financial Markets" bit.ly/MLDSFM2023 shows how modern techniques should be used with existing knowledge of markets. Chapter 6 focuses on the way ML and Data Sciences (DS) connect financial decisions to real-world observations.

"Machine Learning And Data Sciences For Financial Markets" bit.ly/MLDSFM2023 shows how modern techniques should be used in conjunction with existing knowledge of financial markets. Chapter 7 shows how overcome natural limitations of machine learning and data sciences.

I am honored to have been the inaugural speaker at the Blockchain Scholars Podcast. Check out the video lnkd.in/eGdGNFX8 In this first episode, I explain the functioning mechanism of automated market makers for practitioners and academics

Maximal extractable value is a key concern for smart-contract blockchains which support DeFi and other financial innovations. In this paper, papers.ssrn.com/sol3/papers.cf… joint with Ruizhe Jia and Ye Wang from Macau, we show that private submission pools do not always mitigate MEV.

I am looking forward to presenting my just-in-time liquidity paper (see papers.ssrn.com/sol3/papers.cf…) at the 6th Sydney Market microstructure conference in a week. Check out the high-quality program below, with lots of interesting talk on market microstructure and digital finance.

🎙️Our 3rd episode of #CraftingtheCryptoEconomy 📚with @CBER_Forum dives into "Just-In-Time (JIT) Liquidity at Decentralized Exchanges" Agostino Capponi breaks it down in our podcast: ➡️open.spotify.com/episode/2mNpMF… ⬅️ Andreas Park

Can crypto be socially valuable? 👉 youtu.be/2ZNCyhQ1V9s 👀 Agostino Capponi emphasizes the need for cryptocurrencies to generate value through useful services provided by their underlying blockchains, rather than from speculative activities. Columbia University The Data Science Institute at Columbia University

6/N In an environment where vertical integration is not feasible, the authors show how society can end up under investing in capacity. Great paper!🔖

Call for Papers for Gillmore Centre for Financial Technology (wbs.ac.uk/research/resea…) on DeFi & Digital Currencies 28 September, 2024 at WBS The Shard, London. Delighted to give a keynote speech - for full details, see warwick.ac.uk/fac/soc/wbs/su… Looking forward to seeing you there

Join me on June 26th at the Digital Assets Connection 2024 lnkd.in/eBU7TWTS The event is featuring a fantastic lineup of panels that are relevant to all members of the Digital Assets ecosystem – including practitioners, academics, and policy makers

Professor Agostino Capponi is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), the highest U.S. government honor for early-career scientists and engineers. Capponi has developed groundbreaking frameworks and tools to mitigate risks and

Tremendously honored by this recognition

How much additional alpha can you generate if you build new factors out of supply chain data? Check out our new paper to learn more papers.ssrn.com/sol3/papers.cf… Reach out at [email protected] too if interested in discussing more

Check out the Prediction-Enhanced Monte-Carlo method proposed in our recent paper arxiv.org/pdf/2412.11257 to see how modern machine learning techniques can be used to construct cheap and parallelizable simulations, and attain higher accuracy in variance swaps and swaption pricing