{"id":102234,"date":"2021-09-21T09:41:24","date_gmt":"2021-09-21T09:41:24","guid":{"rendered":"https:\/\/dnbsame.com\/?p=102234"},"modified":"2021-10-07T10:01:13","modified_gmt":"2021-10-07T10:01:13","slug":"using-alternative-data-to-predict-stock-performance","status":"publish","type":"post","link":"https:\/\/dnbsame.com\/ar\/using-alternative-data-to-predict-stock-performance\/","title":{"rendered":"Using Alternative Data to Predict Stock Performance"},"content":{"rendered":"

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By<\/span> Chang Lin Director, Predictive Analytics Dun & Bradstreet Inc March 25, 2019<\/div>\n

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How the D&B Data Cloud and Machine Learning Help Predict Stock Returns & Performance<\/h2>\n

Dun & Bradstreet has the largest global commercial database on the planet. At the core is proprietary trade payment data collected from thousands of trade suppliers, which is closely related to\u00a0account payables<\/i>\u00a0on company\u2019s balance sheet. The Credit Score Archive Database (CSAD, 2004-2018) and Detailed Trade Risk Index (DTRI, 2011 \u2013 2018), the engines that help power D&B Credit, are directly derived from the raw trade payment data, and they describe companies\u2019 payment behavior and various risk assessments.<\/p>\n

Including further mathematical transforms, 400+ attributes are extracted from CSAD and DTRI databases to form the foundation of the Dun & Bradstreet US Equity Alpha Factor Library. Through rigorous statistical analysis and machine learning, I and my colleagues in Analytics Innovation demonstrate their values in forecasting future stock returns.<\/p>\n

I\u2019ve authored several whitepapers that detail our work studying capital markets against payment data stock returns. Overall, we believe Dun & Bradstreet data is a unique data source that, with complete coverage on public stocks in the US, provides extra information not available in other alternative data sources.<\/p>\n

Click on any of the papers below to get an in-depth look at our stock market insights.<\/p>\n

D&B US Equity Alpha Factor Library<\/h2>\n

Here we formally introduce the alpha factor library including data sources, factor constructions, and test methodologies. We systematically test 400+ Dun & Bradstreet trade factors for a high degree of statistical significance in forecasting future one-month excess stock returns (or alphas). Such alphas are not explained by Carhart\u2019s Four-Factor Model (i.e., the Fama-French 3-Factor Model plus Momentum). Test results include single factor alpha (residual returns), exposure to Carhart\u2019s 4 factors and t-statistics, as well as historical performance separation between top and bottom deciles. The complete list of attributes identified, with test statistics, is available upon request.<\/p>\n

Download Whitepaper<\/a><\/p>\n

Using Dun & Bradstreet US Equity Alpha Factor Library and Machine Learning as an Alternative Data to Improve Stock Portfolio Returns<\/h2>\n

We applied machine learning to model equity future beta adjusted returns using Dun & Bradstreet data attributes. With 400+ factors, we can pick the worst-performing stocks with reasonable success, over 16 quarters (2014 \u2013 2017). The results compare favorably with benchmarks using single factors (both public and proprietary).<\/p>\n

Download Whitepaper<\/a><\/p>\n

High Relative Trade Credit Underperforms<\/h2>\n

Here we found that stocks with high relative trade credit (to sales) underperform those with low relative\u00a0trade credit<\/a>. The underperformance is statistically significant after adjusting for the Fama-French 3-factor model + MOM (aka FF3+MOM).<\/p>\n

Download Whitepaper<\/a><\/p>\n

The Hidden Cost of Growing Trade Supplier Networks Too Fast<\/h2>\n

Dun & Bradstreet found that stocks with the fastest-growing number of trade suppliers year-over-year underperform those with slowest-growing trade suppliers. The underperformance is statistically significant after adjusting for the Fama-French 3-factor model + MOM (aka FF3+MOM).<\/p>\n

Download Whitepaper<\/a><\/p>\n

When Slow or Negative Payment Experiences Accelerate<\/h2>\n

Dun & Bradstreet found that stocks with the fastest increase in the number of slow or negative payment experiences month-over-month underperform those with slowest increase. The underperformance is statistically significant after adjusting for the Fama-French 3-factor model + MOM (aka FF3+MOM).<\/p>\n

Download Whitepaper<\/a><\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

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