Recent Quant Links from Quantocracy as of 03/23/2025

Imagine yourself, a great retail trader with an algorithm that flawlessly predicts stock movements for monthsuntil a surprise Fed rate hike sends markets into chaos. Overnight, the models accuracy plummets. Why? Concept drift: your model no longer finds patterns in historical data and now underperforms its predictions. For machine-learning-based traders, this is a latent enemy. But, what if

Here we present a yield curve interpolation method, one thats based on conditioning a stochastic model on a set of market yields. The concept is closely related to a Brownian bridge where you generate scenario according to an SDE, but with the extra condition that the start and end of the scenarios must have certain values. In this paper we use Gaussian process regression to generalization

A good trading or investment strategy is only as good as the data behind it. High-quality data is essential if you are backtesting a quant model, analyzing market trends, or building an algorithmic trading system. Prerequisites: To make the most of this blog, it is essential to have a strong foundation in market data sources, data handling techniques, and financial data processing. Start with

ChatGPT and Deepseek: Can They Predict the Stock Market and Macroeconomy? Jian Chen (Xiamen University), et al. February 2025 We study whether ChatGPT and DeepSeek can extract information from the Wall Street Journal to predict the stock market and the macroeconomy. We find that ChatGPT has predictive power. DeepSeek underperforms ChatGPT, which is trained more extensively in English. Other large

Portfolio allocation is an important research area. In this issue, we explore not only asset allocation but also the allocation of strategies. Specifically, I discuss tactical asset and trend-following strategy allocation. Tactical Asset Allocation: From Simple to Advanced Strategies Tactical Asset Allocation (TAA) is an active investment strategy that involves adjusting the allocation of assets

Market practitioners often rely on experience-based wisdom to navigate currency markets, and one such widely held belief is that low dispersion in global bond yields signals weak future returns for carry trades (and high dispersion implies high future carry returns). While this intuition makes sensewhen yield differentials are compressed, the incentive to exploit them diminishesa recent

Mutual funds and ETFs whose main directive is index replication incur adverse selection costs from responding to changes in the composition of the stock market because indices rebalance in response to composition changes (due to IPOs, delistings, additions, deletions, new seasoned issuance, and buybacks) to maintain a value-weighted portfolio. While this approach successfully tracks the index, it

big forces to worry about: growth and inflation. Each could either be rising or falling, so I saw that by finding four different investment strategieseach one of which would do well in a particular environment (rising growth with rising inflation, rising growth with falling inflation, and so on)I could construct an asset-allocation mix that was balanced to do well over time while being

In this study, we explore the application of simple spread trading strategies using Bitcoin ETFs and cryptocurrency infrastructure ETFstwo highly correlated asset classes due to the broader influence of cryptocurrency market movements. Given their strong relationship, this setup provides a compelling case for implementing pair trading strategies based on mean reversion principles. Building on