From c6d24f7332f7c775ab15c7f6606cc7dcc08a52ed Mon Sep 17 00:00:00 2001 From: Wilson Freitas Date: Sat, 12 Sep 2026 21:41:32 -0300 Subject: [PATCH] Link autoencoder paper citation to accessible AQR page --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 656bc50..b1966ac 100644 --- a/README.md +++ b/README.md @@ -680,7 +680,7 @@ A curated list of insanely awesome libraries, packages and resources for Quants - [Quant-Finance-With-Python-Code](https://github.com/lingyixu/Quant-Finance-With-Python-Code) - Repo for code examples in Quantitative Finance with Python by Chris Kelliher. - [QuantFinanceTraining](https://github.com/JoaoJungblut/QuantFinanceTraining) - This repository contains codes that were executed during my training in the CQF (Certificate in Quantitative Finance). The codes are organized by class, facilitating navigation and reference. - [book_irds3](https://github.com/attack68/book_irds3) - Code repository for Pricing and Trading Interest Rate Derivatives. -- [Autoencoder-Asset-Pricing-Models](https://github.com/RichardS0268/Autoencoder-Asset-Pricing-Models) - Reimplementation of Autoencoder Asset Pricing Models ([GKX, 2019](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3335536)). +- [Autoencoder-Asset-Pricing-Models](https://github.com/RichardS0268/Autoencoder-Asset-Pricing-Models) - Reimplementation of Autoencoder Asset Pricing Models ([GKX, 2019](https://www.aqr.com/Insights/Research/Working-Paper/Autoencoder-Asset-Pricing-Models)). - [Finance](https://github.com/shashankvemuri/Finance) - 150+ quantitative finance Python programs to help you gather, manipulate, and analyze stock market data. - [101_formulaic_alphas](https://github.com/ram-ki/101_formulaic_alphas) - Implementation of [101 formulaic alphas](https://arxiv.org/abs/1601.00991) using qstrader. - [Tidy Finance](https://www.tidy-finance.org/) - An opinionated approach to empirical research in financial economics - a fully transparent, open-source code base in multiple programming languages (Python and R) to enable the reproducible implementation of financial research projects for students and practitioners.