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How Data and AI are Changing Investment Analysis

05 May 2021 Christine Chow

Part of the Strategic Governance and ESG Integration Series

Investment professionals use principal component analysis (PCA) to identify key risk factors that impact portfolio performance. This is one of the traditional statistical methods used, along with other techniques, such as multi-factor risk analysis or a hybrid combination of them. The toolbox for statistical analysis has expanded significantly over the last 30 years. Now, we have a catchier option that uses machine learning. We often call it Artificial Intelligence (AI).

For AI to be useful in investment analysis, we need three key elements:

  • Why do we use AI? Know your problem
  • What do we have and what is missing? Know your data
  • How to do it? Know your algorithm

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Posted 05 May 2021 by Christine Chow, Executive Director, IHS Markit

IHS Markit provides industry-leading data, software and technology platforms and managed services to tackle some of the most difficult challenges in financial markets. We help our customers better understand complicated markets, reduce risk, operate more efficiently and comply with financial regulation.

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