{"id":6846,"date":"2025-09-15T07:03:31","date_gmt":"2025-09-15T07:03:31","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/09\/15\/has_anyone_validated_synthetic_financial_data\/"},"modified":"2025-09-15T07:03:31","modified_gmt":"2025-09-15T07:03:31","slug":"has_anyone_validated_synthetic_financial_data","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/09\/15\/has_anyone_validated_synthetic_financial_data\/","title":{"rendered":"Has anyone validated synthetic financial data (Gaussian Copula vs CTGAN) in practice?"},"content":{"rendered":"

Has anyone validated synthetic financial data (Gaussian Copula vs CTGAN) in practice?
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I\u2019ve been experimenting with generating synthetic datasets for financial indicators (GDP, inflation, unemployment, etc.) and found that CTGAN offered stronger privacy protection in simple linkage tests, but its overall analytical utility was much weaker. In contrast, Gaussian Copula provided reasonably strong privacy and far better fidelity.<\/p>\n

For example, Okun\u2019s law (the relationship between GDP and unemployment) still held in the Gaussian Copula data, which makes sense since it models the underlying distributions. What surprised me was how poorly CTGAN performed analytically… in one regression, the coefficients even flipped signs for both independent variables.<\/p>\n

Has anyone here used synthetic data for research or production modeling in finance? Any tips for balancing fidelity<\/em> and privacy<\/em> beyond just model choice?<\/p>\n

If anyone\u2019s interested in the full validation results (charts, metrics, code), let me know, I\u2019ve documented them separately and can share the link.<\/p>\n

https:\/\/preview.redd.it\/lmsmleiki2pf1.png?width=1059&format=png&auto=webp&s=19cb6d9215e590e5fe6497bc0dd7152d9d85f119<\/a><\/p>\n<\/p><\/div>\n

submitted by \/u\/nlomb <\/a>
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Has anyone validated synthetic financial data (Gaussian Copula vs CTGAN) in practice? I\u2019ve been experimenting with generating synthetic datasets for financial indicators (GDP, inflation, unemployment, etc.) and found that CTGAN offered stronger privacy protection in simple linkage tests, but its overall analytical utility was much weaker. In contrast, Gaussian Copula provided reasonably strong privacy and […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,99],"tags":[202,84,805],"class_list":["post-6846","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-datascience","tag-anyone","tag-data","tag-synthetic"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/6846"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=6846"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/6846\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=6846"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=6846"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=6846"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}