Investment Management Analytics Using Data Science for Asset Management

Asset Management with Data Science-Driven Insights

The client required sophisticated investment management analytics to elevate the stock portfolio construction of their clients. They tasked Acuity Knowledge Partners with implementing data science asset management solutions, aiming to empower traders to systematically generate alpha signals using machine learning factor models. The challenge lay in effectively processing and analyzing over 10,000 assets. Acuity responded by constructing 5+ external data pipelines and establishing 100+ data-quality controls, ensuring data integrity. This comprehensive approach enabled the target users to leverage advanced quantitative techniques, providing them with data-driven insights to refine portfolio strategies and enhance performance. By integrating machine learning and robust data management for financial services, Acuity facilitated the generation of actionable alpha signals, directly addressing the users’ need for systematic portfolio optimization.

  • 10,000+

    assets analysed

  • 5+

    external data pipelines built

  • 100+

    data-quality controls built


CLIENT PROFILE AND CHALLENGES

  • 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

OUR APPROACH

  • 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

IMPACT DELIVERED

  • 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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What we have done

Data tagging and analysis focused on fundamental sub-sector equity strategy
What we are proud of

800+ merchant names tagged

Optimised and scalable solution across datasets and sectors

Enterprise-level data management powered by analytics
What we are proud of

Expertise in market data

Handled end-to-end data-pipeline transformation for more than 1,000 datasets