[With the growth of artificial intelligence (AI) and quantitative strategies such as smart beta, fund directors and wealth managers are increasingly asked to oversee funds that use AI and quantitative methods to manage investments. Fund directors especially must understand the basis of these technologies to properly govern the funds they oversee.
A research paper developed by Brian Bruce, CEO and CIO of Hillcrest Asset Management, provides a framework for directors and wealth managers to understand how to oversee these funds. The paper discusses the concepts of artificial intelligence and quantitative investing, particularly focusing on key areas that directors should understand. The research poses vital questions and provides important perspectives on these increasingly employed investment approaches and tools that can be helpful for asset managers, advisors and investors.]
Bill Hortz: How did you define and characterize Quantitative investing and artificial intelligence in investment management for your research paper?
Brian Bruce: Quantitative investment management is simply the use of computers and data in portfolio management. AI is a distinct set of investment strategies in which the machine has taken over not only the acquisition of data and its processing, but also the judgment behind the decisions.
Hortz: What do you feel are the main governance priorities in overseeing these strategies?
Bruce: The main priorities to determine for these strategies are:
Does the fund advisor have the expertise, knowledge, and resources necessary to carry out the intended strategy? Prior to any fund launch, directors should discuss whether the advisor has the expertise, knowledge, and resources to carry out the intended strategy of the new fund. However, boards may have a more difficult time assessing the advisor’s ability with respect to an AI strategy due to the newness of application and lack of substantial track records to be guided by.
How is quality assurance monitored? When adding AI funds, the advisor may need to change approval procedures. The procedures are sound for current strategies, but AI is much more of a black box than most existing strategies. This means that directors will need to learn additional information and will need to ask different questions to make sure that the AI is properly tested and implemented.
Hortz: Can you discuss with us some of your key findings in your research that you feel we need to consider in doing oversight of these strategies?
Bruce: A few key considerations would be:
1. Quantitative funds have been around for many years. They invest based on a predetermined set of rules created by the investment team. The difference with artificial intelligence funds is that there are no predetermined rules: AI looks at large amounts of data and creates its own rules.
2. Quantitative and AI strategies involve some sort of backtesting to confirm that the strategy will work going forward. We provide questions in our research report to ask the investment team to ensure that the testing was done properly, as well as SEC rules for disclosure of backtesting.
3. Directors should set up a framework for judging the AI process and its structure. They should also set criteria for expected outcomes in order to approve AI funds. Finally, directors should specify for management what they expect from the AI effort going forward and how that will be communicated. It is critically important that directors have a process that results in a consistent metric, which will enable them to better govern these new funds.
Hortz: What are the common problems you should be aware of that the SEC has with hypothetical backtesting?
Bruce: There are a quite a number of problems that the SEC has with hypothetical backtesting. We have also compiled an appendix to our report with specific questions you need to ask to evaluate a backtest in response to these SEC concerns:
1. Failure to disclose limitations. One common allegation is that firms fail to fully disclose the limitations on hypothetical back-tested performance (HBP).