---
title: An Auditable Public-Data U.S. Equity Research Pipeline: Point-in-Time Factors, Asset Pricing, Portfolio Construction, and Walk-Forward Machine Learning
url: https://www.ml-quant.com/papers/ssrn/7528419/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-10-02
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 7528419
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7528419
featured: 2026-10-02
citations: unknown
topic: Asset Pricing & Factors
---


# An Auditable Public-Data U.S. Equity Research Pipeline: Point-in-Time Factors, Asset Pricing, Portfolio Construction, and Walk-Forward Machine Learning

A reproducible pipeline for factor research finds no model reliably beats simple approaches after accounting for transaction costs in point-in-time factor tests.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7528419
- Identifier: SSRN 7528419
- Released: 2026-09-28
- First featured: Quant Letter No. 133 (2026-10-02): https://www.ml-quant.com/issues/2026-10-02/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Asset Pricing & Factors
- Authors: Amit Kumar Dudi

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