<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>Research on AJ</title><link>https://ajsquestions.github.io/tags/research/</link><description>Recent content in Research on AJ</description><image><url>https://ajsquestions.github.io/AJpicture.jpg</url><link>https://ajsquestions.github.io/AJpicture.jpg</link></image><generator>Hugo -- gohugo.io</generator><language>en</language><lastBuildDate>Mon, 06 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ajsquestions.github.io/tags/research/index.xml" rel="self" type="application/rss+xml"/><item><title>Why ML Models Disagree About Stock Returns</title><link>https://ajsquestions.github.io/blog/why-ml-models-disagree-about-stock-returns/</link><pubDate>Mon, 06 Apr 2026 00:00:00 +0000</pubDate><guid>https://ajsquestions.github.io/blog/why-ml-models-disagree-about-stock-returns/</guid><description>When neural networks and gradient boosting look at the same stocks, they see different things. Here&amp;#39;s why that disagreement is geometrically structured — and what it means for building better ensembles.</description></item></channel></rss>