Issue #225

November 24, 2023

This weeks book is "Causal Factor Investing" by Marcos M. López de Prado, his most recent work in quantitative finance. López de Prado introduces a unique approach by emphasizing causality, a factor often overlooked in traditional quantitative models. The book provides a refreshing perspective on financial markets, challenging conventional wisdom and offering a comprehensive guide to understanding and implementing causal factor investing.

What sets this book apart is its practicality. López de Prado not only delves into the theoretical underpinnings of causal factor investing but also provides real-world examples and case studies. This hands-on approach ensures that readers can not only grasp the concepts but also apply them in their own investment strategies. The author's writing style is both engaging and approachable, striking a balance between academic rigor and practical applicability.

In a landscape where financial strategies are constantly evolving, "Causal Factor Investing" stands out as a timely and relevant resource. López de Prado's expertise shines through, making this book essential for both seasoned professionals and those new to quantitative finance. It's a must-read for anyone serious about staying ahead in the dynamic world of investment, offering a thought-provoking and informative guide that will shape the future of quantitative finance.

Causal Factor Investing

1. Q-learning [wikipedia.org]
2. How to Stop Taking Work So Personally [hbr.org]
3. Cryptographers Solve Decades-Old Privacy Problem [nautil.us]
4. Scientists combine climate models for more accurate projections [phys.org]
5. Incremental Processing using Netflix Maestro and Apache Iceberg [netflixtechblog.com]
6. Knowledge Graph Transformers: Architecting Dynamic Reasoning for Evolving Knowledge [towardsdatascience.com]
7. Generative AI for Beginners - A Course [microsoft.github.io]

• Covid-19 vaccine effectiveness against post-covid-19 condition among 589 722 individuals in Sweden: population based cohort study (L. Lundberg-Morris, S. Leach, Y. Xu, J. Martikainen, A. Santosa, M. Gisslén, H. Li, F. Nyberg, M. Bygdell)
• Network homophily via tail inequalities (N. Apollonio, P. G. Franciosa, D. Santoni)
• Using a Bayesian approach to reconstruct graph statistics after edge sampling (N. A. Arnold, R. J. Mondragón, R. G. Clegg)
• LLMs cannot find reasoning errors, but can correct them! (G. Tyen, H. Mansoor, P. Chen, T. Mak, V Cărbune)
• The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4 (Microsoft Research AI4Science, Microsoft Azure Quantum)
• In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steering (S. Liu, L. Xing, J. Zou)
• The social costs of tropical cyclones (H. Krichene, T. Vogt, F. Piontek, T. Geiger, C. Schötz, C. Otto)

Intro to Large Language Models

November 24, 2023

​