---
title: Code-Paper Consistency in Economics and Finance
url: https://www.ml-quant.com/papers/ssrn/7681378/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-11-14
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 7681378
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7681378
featured: 2026-11-13
citations: unknown
topic: Econometrics & Forecasting
---


# Code-Paper Consistency in Economics and Finance

A systematic audit of 1,820 economics and finance papers finds 10% of claims show discrepancies between code and description, with 49% containing potentially serious errors affecting central results.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7681378
- Identifier: SSRN 7681378
- Released: 2026-11-09
- First featured: Quant Letter No. 139 (2026-11-13): https://www.ml-quant.com/issues/2026-11-13/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting
- Authors: Christoph Huber, Paul Fabian Alexander Seidel, Sina Seyfi, Erkki Vihriälä

## Related

- [TKAN: Temporal Kolmogorov-Arnold Networks](https://www.ml-quant.com/papers/ssrn/4825654/): The article presents Temporal Kolomogorov-Arnold Networks (TKANs), a new neural network design that merges the benefits of Recurrent Neural Networks and Long Short-Term Memory for improved multistep time series forecasting.
- [Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022](https://www.ml-quant.com/papers/arxiv/2305.04811/): The article explores deep learning models for financial forecasting, discussing their structures, uses, pros and cons, and potential future research areas.
- [Robust agents learn causal world models](https://www.ml-quant.com/papers/arxiv/2402.10877/): The research suggests that intelligent agents must learn an approximate causal model to generalize to new domains, impacting fields like transfer learning and causal inference.
- [Directional-shift Dirichlet ARMA models for compositional time series with structural break intervention](https://www.ml-quant.com/papers/arxiv/2601.16821/): The article introduces a new Bayesian model that analyzes compositional time series data, effectively handling structural breaks and enhancing forecasting accuracy during these changes.
- [Blameocracy: Causal Rhetoric in Politics](https://www.ml-quant.com/papers/arxiv/2504.06550/): U.S. Causal Rhetoric: Growing blame/credit language in congressional tweets changed donation patterns, fueled protests and polarization, and shifted public trust.
- [FinCARE: Financial Causal Analysis with Reasoning and Evidence](https://www.ml-quant.com/papers/arxiv/2510.20221/): KG+LLM for Financial Causal Discovery: Combines SEC knowledge graphs, LLM reasoning, and causal discovery to build more accurate finance‑grounded causal models.