CFA-Institute-RPC/Synthetic-Data-For-Finance

This repository contains accompanying code for the CFA Institute's Research and Policy Center 'Synthetic Data in Investment Management' report.

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When you need to create realistic financial datasets but are limited by historical data, privacy concerns, or overfitting issues, this resource can help. It provides an overview of advanced generative AI methods to produce synthetic financial data. Financial analysts, quantitative traders, risk managers, and portfolio managers can use this to generate new data for tasks like risk modeling, portfolio optimization, forecasting, and sentiment analysis.

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Use this if you need to generate high-quality, artificial financial datasets for training models, backtesting strategies, or conducting research without relying solely on real, potentially sensitive or scarce, data.

Not ideal if you are looking for a plug-and-play software tool without understanding the underlying generative AI techniques.

financial-modeling quantitative-finance risk-management portfolio-optimization financial-data-analysis
Stale 6m No Package No Dependents
Maintenance 2 / 25
Adoption 5 / 25
Maturity 15 / 25
Community 8 / 25

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Language

Jupyter Notebook

License

MIT

Last pushed

Jul 28, 2025

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