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
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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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