A state of art factor research platform

Back-testing different factor-based quantitative portfolio strategies involves creating historical model portfolios across a long time horizon using the same quantitative rules and the available point-in-time historical data. Since this requires dealing with vast amounts of data and various permutations and combinations for rule-based selection and weighting of securities, it is heavily reliant on a robust technology platform and infrastructure. A strong technology platform, which is well integrated with a back-end database, can facilitate quick and seamless creation and analysis of a multitude of factor-based investment strategies. Such a platform can enable a factor-based asset manager to efficiently back-test a myriad rule-based portfolio strategies and quantitative ideas on a large universe of stocks over long periods by dynamically combining different rules pertinent to the use of factors and parameters for stock selection, portfolio weightages, and rebalancing frequencies among other inputs.

NJ Asset Management, a fully rule-based asset manager, has acknowledged the importance of a strong IT infrastructure for successfully employing factor-based investment methodologies. NJ Asset Management, in this endeavour, has developed the proprietary Smart Beta Platform, enabling its researchers to back-test factor-based strategies across a large universe of stocks, covering more than 1100 companies and 20 years of data. Through the Smart Beta Platform, research analysts can seamlessly analyse the historical performance of their back-tested portfolios vis-a-vis relevant benchmarks (for eg. Nifty 500 TRI, Nifty 50 TRI), historical composition of the portfolios including sectoral and market capitalisation exposures, portfolios’ churn, and the portfolios’ winners and laggards across different time periods.

Watch the demo video of NJ AMC’s Smart Beta Research Platform for details:

(i) Smart Beta Demo (English)

Link—https://www.youtube.com/watch?v=0LbhLAXY3AM

(ii) Smart Beta Demo (Hindi)

Link—https://www.youtube.com/watch?v=bbhaOmzNn4Y