Machine-Learning-Generated Synthetic Data for Regulation Best Interest Compliance
12 Pages Posted: 11 Jul 2019
Date Written: July 9, 2019
Regulation Best Interest requires broker-dealers to understand potential risks, rewards, and costs associated with recommendations to retail customers. For those charged with this legal duty, historical financial data provides little comfort. There is too little historical financial data and too many ways to overfit it with investment strategies that may have poor risk-return performance in the future. We develop a method for generating synthetic data to test investment products for investor best interest. We illustrate our method by showing that the Dow Jones Industrial Average is highly outlier-dependent, such that forecasts of the strategy's future performance based on historical data would be overstated by over 80%. Our method can better screen out investment recommendations that are unlikely to be viewed by a later factfinder (in litigation/arbitration or a regulatory enforcement action) as not being in a retail client's best interest.
Keywords: Synthetic Data, Machine Learning, Best Interest, Compliance
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