![July 2021 Chronicles FI](/sites/default/files/styles/full_width/public/2023-07/July%202021%20Chronicles.png?itok=Ebqi0jew)
Dear Investment Professional,
Not everyone has to be a master coder or a world-class quant, but it’s becoming increasingly important for investors of all stripes to understand how data science, artificial intelligence (AI), and machine learning (ML) will impact investment processes. Across asset classes, more managers are utilizing these tools to generate differentiated sources of return, but allocators also see the incremental benefits of the operational efficiencies and scale that AI and ML have introduced. On the flip side, as I wrote in a recent What About Beta? post1, allocators need to be wary of overfitting and “machine-washing” in this space. Indeed, everyone seems to claim that they incorporate machine learning in their techniques, but as definitions keep evolving, responsible investors should be able to discern when these technologies are simply automating DCF spreadsheets to save time or offering access to genuine sources of alpha.
The CAIA Content Team
John L. Bowman, CFA, Senior Managing Director, Twitter, LinkedIn
Keith Black, PhD, CFA, CAIA, FDP, Managing Director Content Strategy, Twitter, LinkedIn
Guowei Jack Wu, CFA, CAIA, Content Director, APAC, Twitter, LinkedIn
Aaron Filbeck, CFA, CAIA, CIPM, FDP, Director, Global Content Development, Twitter, LinkedIn
![Thinking inside the box](/sites/default/files/styles/full_width/public/2021-07/Picture1_resized.png?itok=2JPcm46O)
![Domain experts still needed](/sites/default/files/styles/full_width/public/2021-07/Picture2.png?itok=RVWSB4on)
Data scientists tend to criticize the application of machine learning in financial markets, noting the “noisiness” of the markets and the (lack of) predictability of returns. Still, according to an article published earlier this year by Factset Insight, these well-intended data scientists are often starting in the wrong places; instead, equity investors should start with the quantitative factors they already know well and use ML merely to enhance how they build and iterate on their models. When computers can effectively take over the data-driven parts of the process, professional investors can instead “concentrate on bringing their domain expertise to bear by testing hypotheses on how to improve their models and strategies further.”
Have the domain expertise but still lacking the data science skills you need to advance your career? Skill up with the Financial Data Professional credential. Active CAIA Members can enjoy 20% off the program. Learn more on the FDP Institute website
![Who’s winning the race for AI investment?](/sites/default/files/styles/full_width/public/2021-07/Picture3_resized.png?itok=V5dGNTDN)
![How private equity hopes to benefit from AI](/sites/default/files/styles/full_width/public/2021-07/Picture4_resized.png?itok=3pgaE0OI)