How much value does a hedge fund manager contribute to the success of their fund?
Turns out it’s more modest than traditional measures imply, according to the latest research using artificial intelligence. 

Ashish Tiwari, finance professor at the Tippie College of Business, said researchers suspected for decades that the perceived success of a fund was often the result of relying on imperfect performance benchmarks that did not adequately control for the fund’s risk exposures. This includes factors outside of a manager’s control. 

But thanks to recent research using artificial intelligence, researchers now have a better understanding of how much the manager’s skill contributes to a fund’s success. He said machine learning–based benchmarks provide more accurate performance measurement and suggest that a substantial share of a hedge fund’s returns reflects exposure to systemic factors rather than managerial skill.  

After controlling for these effects, the incremental value added by fund managers is often smaller than traditional measures suggest.

Tiwari said that research using AI has shown that models developed over decades by researchers and analysts to explain asset returns turned out to do doing a poor job. He said machine learning techniques offer flexibility that can better explain a fund’s returns, especially actively managed funds and hedge funds. 

Hedge funds tend to be more opaque in their portfolio strategies and models using conventional factors do a poor job of identifying their underlying risk exposures. It turns out machine learning algorithms do a much better job at figuring out what the managers are doing and what they’re contributing to the fund’s performance.    

Tiwari shared more of his thoughts about AI and investing on “Tippie Leads,” the Tippie College of Business’ podcast.