US asset manager: Non-linear factor modelling for systematic investment strategies

  • 10,000+

    assets analysed

  • 5+

    external data pipelines built

  • 100+

    data-quality controls built

  • Senior Portfolio Managers, Head of Quantitative Development
  • The client wanted Acuity to enable traders to systematically generate alpha signals on their stock portfolio construction through machine learning factor model processes
  • Deployed a team of 1 data scientist and 1 data engineer to design, develop and deploy a Windows BAT scheduling framework to automatically acquire different financial datasets, engineer features and predict factors for each stock in-scope
  • Used a Non-Linear Adaptive Style Rotation (N-LASR) trained model on engineered features including forward returns, risk-adjusted factors, and premiums to predict and compute factors for each stock
  • Developed a slick UI to view current and historical alpha signals and factors of each in-scope stock
  • Automated alpha signal generation, enabling traders to make faster trading position decisions
  • Trading support is able to identify and resolve data and process issues via the UI dashboard
  • Enabled quantitative developers to easily modify the data sources, features and model algorithms in a configurable manner
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