Independent investment research provider Morningstar has announced a new analytics feature, “Notebooks” that combines multi-faceted data sets with automated analysis to generate previously difficult-to-find insights.

“Morningstar was built on the idea that investors need access to data and research to make better informed financial decisions. As the available data has skyrocketed, so has the pressure for financial professionals to harness and analyze it to help their clients and build new investment products. Existing Morningstar Notebooks have been well-received by clients, and we’re excited to create the ability for users to code, implement, and share their own Notebooks on Morningstar Direct,” said Frannie Besztery, Head of Morningstar Direct.

Today, five individual Notebooks are currently available to users of Morningstar Direct today, and three additional datasets will be released via Notebooks by the end of 2021.

The company announced the addition of a new analytics feature to Morningstar Direct, coupling multi-faceted data sets with automated analysis to generate previously difficult-to-extract insights. Morningstar is speeding up the release of interactive analytics and fresh research via Notebooks, enabling investors to gain new insights on diversity, ownership, and performance.

Financial professionals may use Notebooks to bring data to life in their everyday research, whether to examine managers, design portfolios or find new sources of risk they wouldn’t have noticed otherwise. Morningstar Direct subscribers can access five unique Notebooks right now, and three more datasets will be available via Notebooks by the end of 2021. 

The following are some of the new data sets available:

• Portfolio Manager Performance History, which plots a portfolio manager’s career history and fund performance data across time to aid in manager selection.
• Users can do equity ownership analysis on a certain lineup of funds and gain insight into managers who recently built or unloaded their position in a particular stock using Stock Ownership Analysis.
• Firm Diversity Data, which analyses asset management firms’ diversity makeup in order to increase transparency and make it easier for asset owners to obtain this information and choose firms that match their diversity requirements.
• The data can be pooled along various dimensions and offers specific information about fund groupings such as environmental, social, and governance (ESG) funds, exchange-traded funds, and index funds, making it easy to keep track of new fund launches.
• Time Series Factor Regression Analysis breaks down returns into factor exposures to derive an investment’s alpha and beta exposures, allowing asset management businesses’ performance analysts to keep track of their portfolios.