Big Data and ML in funds research

Fund Manager evaluation demands an assessment across multiple dimensions, including the investment process, risk management, operations, and the business model.

We have compiled the latest research on using Big Data and Machine Learning (ML) in funds research.

In a live webinar organised by the CFA Society India in April 2021, Sabeeh Ashhar of Morningstar discusses best practices in understanding manager investment styles, skill sets, and risk management using Cloud and Big Data technologies. Sabeeh also demonstrates use cases that provide a multi-lens view of fund manager selection.

The session provides –

overview of factor model construction and methodology.
comparison of factor model outcomes to better understand fund manager investment styles, performance attribution, skill sets, and factor timing capability.
ideas for execution of analytics at scale through Cloud and Big Data technologies.

The FDP Institute, part of CAIA, recently held a webinar on ‘Practical Machine Learning in Asset Management: Manager selection and valuing the secondary sale of private assets’

Join Lydia Ofori of Hunter Labs, Adam Duncan of Cambridge Associates, and Keith Black of FDP Institute as they discuss how machines are interfacing with humans to effect manager selection and private asset secondary sales.
The webinar will discuss key questions that sit across technology and finance:

• Can machines actually uncover hidden patterns that escape the human eye and emotions?
• How would the interface between technology applications enhance our ability to pick the best managers and companies?
• What are the limitations on artificial intelligence and other machine learning based models that are most likely to hinder or interrupt results?
• Can AI be used to expedite the secondary sale of private assets?

The webinar recording is available here.

The slide deck is here.

Past performance is not a good guide to future performance…really

The presentation on manager (funds) research concluded that –

Returns-based measures are nearly information-less. But financial performance can be good for predicting how other investors might act. Such analysis might also be useful for other analysis such as detecting anomalies and governance.



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