The hottest Quantitative Analysis Substack posts right now

The story highlights the importance of adapting to changing market conditions. The team learned they needed to pivot their strategies quickly to stay ahead in trading.
Building successful trading systems requires not just good math, but also understanding market behaviors and managing risks effectively. The approach taken focused on quickly executing trades to capitalize on market noise.
Competition plays a big role in financial markets. As more players adopted similar strategies, the initial advantages of their system decreased, emphasizing the need to continuously innovate.

A new computational method can measure the shadow rate, which helps in comparing different investment types. This can give investors better insights.
Using multi-agent systems for investment research allows adaptation to changing market conditions, leading to improved performance over traditional models.
Machine learning continues to show promise in finance, with various models effectively predicting market behavior and improving investment strategies.

A model for pricing VIX options has proven effective in markets like Germany's power and TTF gas markets. This model uses multiple factors to improve accuracy.
The HJM and Lifted Heston Model aims to connect historical data of futures contracts with current implied volatility. This helps better predict market behaviors.
Understanding these models can enhance strategies in quantitative finance, especially for those working with options and futures trading.

Data science is more than just algorithms; real-world applications require a broad set of skills. Understanding the context and how to deal with data is crucial.
Computer vision can be fooled by certain images, which raises important security concerns. This highlights the need for ongoing research in making AI more reliable.
Breaking into data science can be tough because interviews often cover a wide range of topics. It's important to prepare for both programming and statistics in your job search.

A new method for valuing private data is proposed, aiming to enhance current data market systems.
The study introduces risk models using tree structures to better manage interconnected risks within a portfolio.
These advancements could lead to improvements in how data is acquired and utilized in finance.

Get a weekly roundup of the best Substack posts, by hacker news affinity:

Proxy models move away from traditional finance theories like CAPM, focusing instead on how markets actually price investments. They try to explain returns based on observable factors rather than assumptions about investor behavior.
Research by Fama and French found that factors like market capitalization and price-to-book ratios are better at explaining stock returns than the original CAPM betas. This means smaller companies and those with lower price-to-book ratios tend to have higher returns.
While proxy models can improve expected return calculations, they come with risks like data mining and standard error problems. This means the results may not always be reliable or may misrepresent the true risk involved.

Using data from US corporate bond holdings can help predict credit risk better than traditional ratings. It means more real-time information for making investment decisions.
A new investment strategy called Betting Against Bad Beta is introduced. This strategy aims to improve how investors can bet against stocks with poor performance.
Machine learning is becoming more important in finance, especially for analysis and predicting risks. This technology helps make smarter investment choices.