Thought leadership is more than just a buzzword—it's a strategic tool that can significantly influence business decisions and relationships. But what makes thought leadership effective? How do you ensure your insights are not only heard but also trusted and acted upon? What role does generative AI play in enhancing the storytelling process, and how can it be leveraged to create compelling narratives that resonate with your audience? Cindy Anderson is the Chief Marketing Officer/Global Lead for Engagement & Eminence at the IBM Institute for Business Value (IBV).  She has co-authored research reports, published numerous articles, and delivered presentations on thought leadership, diversity, strategy implementation, project management, and technology to global audiences. She oversees a team of 30 editors, designers, and social media/email marketers. She is a founding board member of the Global Thought Leadership Institute at APQC, a new association that advances the practice of thought leadership. Anthony Marshall is the Chair of the Board of Advisors for The Global Thought Leadership Institute at APQC and the Senior Research Director of thought leadership at the IBM Institute for Business Value (IBV), leading the top-rated thought leadership and analysis program. He oversees a global team of 60 technology and industry experts, statisticians, economists, and analysts. Anthony conducts original thought leadership and has authored dozens of refereed articles and studies on topics including generative AI, innovation, digital and business transformation and ecosystems, open collaboration and skills. In the episode, Richie, Cindy, and Anthony explore the framework for thought leadership storytelling, the role of generative AI in thought leadership, the ROI of thought leadership, building trust and quality in research, and much more. Links Mentioned in the Show: The ROI of Thought Leadership book by Cindy and AnthonyAPQCConnect with Cindy and AnthonySkill Track: Artificial Intelligence (AI) LeadershipRelated Episode: How Generative AI is Changing Leadership with Christie Smith, Founder of the Humanity Institute and Kelly Monahan, Managing Director, Research InstituteSign up to RADAR: Skills Edition New to DataCamp? Learn on the go using the DataCamp mobile appEmpower your business with world-class data and AI skills with DataCamp for business

IAQF & Thalesians Seminar Series: The Economics of Automated Market Making and Decentralized Exchanges. A Seminar by Ciamac Moallemi.

6:00 PM Seminar Begins 7:30 PM Reception

Hybrid Event:

*EVENT ROOM CHANGE*

113 W 60th Street New York, NY 10023

*This event is now in the South Lounge in Lowenstein. Guests can enter it through the Ram Cafe in Lowenstein - it is up the escalator behind the security desk when you enter through the main entrance.

Free Registration!

For Virtual Attendees: Please email [email protected] for the link

Abstract: Automated market making (AMM) protocols such as Uniswap have recently emerged as an alternative to the most common market structure for electronic trading, the central limit order book. Relative to limit order books, AMMs are both more computationally efficient and do not require the participation of active market making intermediaries such as high frequency traders. As such, AMMs have emerged as the dominant market mechanism for trust-less decentralized exchanges (DEXs) implemented through smart contracts on programmable blockchain platforms such as Ethereum. In cryptocurrency markets, the aggregate trading volume on the Uniswap DEX exceeds that of the much better known Coinbase centralized exchange.

We develop a model the underlying economics of AMMs from the perspective of their passive liquidity providers (LPs). Our central contribution is a "Black-Scholes formula for AMMs". Like the Black-Scholes formula, we consider the return to LPs once market risk has been hedged. We identify the main adverse selection cost incurred by LPs, which we call "loss-versus-rebalancing" (LVR, pronounced "lever"). LVR captures costs incurred by AMM LPs due to stale prices that are picked off by better informed arbitrageurs. In a continuous time Black-Scholes setting, we are able to derive closed-form expressions for this adverse selection cost. Qualitatively, we highlight the main forces that drive AMM LP returns, including asset characteristics (volatility), AMM characteristics (curvature / marginal liquidity, fee structure), and blockchain characteristics (block rate). Quantitatively, we illustrate how our model's expressions for LP returns match actual LP returns for the Uniswap v2 WETH-USDC trading pair. Our model provides tradable insight into both the ex ante and ex post assessment of AMM LP investment decisions. LVR can also inform the design of the next generation of DEX market mechanisms—in fact, in the short time since our work has been released, "LVR mitigation" has already emerged as the dominant challenge among practitioners in the AMM protocol designer community.

This talk is joint work with Jason Milionis (Columbia CS), Tim Roughgarden (Columbia CS / a16z crypto), and Anthony Zhang (Chicago Booth). It is based on the following two papers:

https://moallemi.com/ciamac/papers/lvr-2022.pdf

https://moallemi.com/ciamac/papers/lvr-fee-model-2023.pdf

Bio: Ciamac C. Moallemi is William von Mueffling Professor of Business in the Decision, Risk, and Operations Division and the director of the Briger Family Digital Finance Lab at the Graduate School of Business at Columbia University, where he has been since 2007. A high school dropout, he received S.B. degrees in Electrical Engineering & Computer Science and in Mathematics from the Massachusetts Institute of Technology (1996). He studied at the University of Cambridge, where he earned a Master of Advanced Study degree in Mathematics (Part III of the Mathematical Tripos), with distinction (1997). He received a Ph.D. in Electrical Engineering from Stanford University (2007). Prior to his doctoral studies, he developed quantitative methods in a number of entrepreneurial ventures: as a partner in a $200 million fixed-income arbitrage hedge fund and as the director of scientific computing at an early-stage drug discovery start-up. He holds editorial positions at the journals Operations Research and Management Science. He is a past recipient of the British Marshall Scholarship (1996), the Benchmark Stanford Graduate Fellowship (2003), first place in the INFORMS Junior Faculty Paper Competition (2011), and the Best Simulation Publication Award of the INFORMS Simulation Society (2014). Aside from his academic work, he regularly consults for fintech companies. His research interests are in the development of mathematical and computational tools for optimal decision making under uncertainty, with a focus on applications areas including market microstructure, quantitative and algorithmic trading, and blockchain technology.