Predicting missing values for various financial datasets through machine learning for consistency and better model predictive power for a US-based Quantitative Hedge Fund

Client: Large US Quantitative Hedge Fund

 

Objective

 

To help a leading US-based quantitative systematic hedge fund improve data quality and enhance fundamental investment research by using Machine Learning techniques to impute missing values in financial statements.


CRISIL's Solution

 

  • The client provided large data extract from a single market vendor for nearly 9,000 US companies
  • CRISIL rapidly set up a team of Machine Learning and Accounting experts
  • The team carefully assessed missing data and client requirements and proposed the MICE approach to impute missing values
  • Used Expectation Maximization algorithm with statistical techniques like auto-regression and PMM to arrive at final results
  • The model was validated using different sub-samples of data, using in-sample and out-sample techniques, and the validation of output against actual reported results using small sample of companies for key line items
  • Model performance was measured using Mean Absolute Deviation and RMSE for benchmarking against using previous year values, pmm as imputation methodology
     

Client Impact

 

  • CRISIL's MICE approach outperformed results from other vendors
  • Enhanced quality data improved the Hedge Fund’s fundamental analysis of companies across equities/credit portfolios

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