First off, data analytics can be used to derive the actual investment process best practices from a given firm’s most skilled investors. That’s a useful exercise in itself, if only to ensure that the investment process being used is consistent with what the firm is selling to investors. But the next step is to use technology to train newer investors to follow that process, and to show them, on an ongoing basis, what they are doing that’s working and what isn’t.
Technology can also supercharge the learning process is by automating the collection of data about the contexts in which each investment decision – whether it’s a decision to trade or not to trade – is made. In other words, recording a rich and accurate “game tape” on each position, which can be watched back later.
Finally, technology can be used to automatically prompt investors, regardless of their tenure, to avoid making the same mistakes repeatedly, by notifying them when a detrimental (or beneficial) pattern may be reappearing.
Technology need not replace the investor as a fundamental decision-maker – indeed, artificial intelligence is not yet at the stage where it is able to do this – but it can be used to empower the decision-maker by enabling accurate and structured reflection, in the same way that athletes use game tapes and data analytics to continuously improve their performance.
The investment management industry is historically poor at reflection, thus the sole reliance on time as a teacher. It uses investment performance – a very noisy and imperfect measure of skill, which provides very little information about what to do differently in future – as its feedback loop. As a result, portfolio managers tend to reflect only for the sake of client reporting, and not in a concerted effort to improve. Yet academic research shows that people who build reflection into their processes learn faster than those who use that time to just “do more”.
The happy byproduct of using technology to guide, facilitate and measure an investment process is the ability to prove that the process itself is followed, and that it works. In light of the active management industry’s weak performance, clients are less and less persuaded by vague investment processes that rely upon a traditional “star” culture. Investors want to see a clear, repeatable decision-making process that is the subject of constant incremental improvement, with consistent, comparable data across individuals, teams and time.