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Why Investors Won't Embrace AI
The biggest challenge quantitative managers face may not be developing powerful, predictive AI-based investment models, but convincing investors against trusting their own judgment, writes columnist Angelo Calvello.
November 13, 2017
© 2017 Institutional Investor LLC
Source; excerpts follow:

The first 2 paragraphs tell you what you need to know about the author, and I give him kudos for his forthrightness:


I've staked my career on the view that advanced artificial intelligence methods will radically transform both our investment-decision-making processes and our business models.

With few exceptions, asset owners, asset managers, and consultants do not agree with my outlook on the future…

He goes on to make his case, citing various investment industry individuals, and making mentions of academic-literature and -research. (I would have liked some hyperlinked references.):


… It's disconcerting that these usually critical thinkers fail to see that AI's transformative power allows for better predictions than do traditional approaches. The disconnect is not because of the disparity between our worldviews, but because of the internal inconsistency of their own views.

These same individuals accept that in the past few years, advanced AI technologies have started to solve complex problems better than humans (e.g., Deep Mind's AlphaGo and Mount Sinai's Deep Patient) without explicit human programming.

They fully understand the commercial benefits of AI and can often explain how many traditional companies like Walmart, UPS, and Toyota have achieved commercial benefits by incorporating AI into their core businesses…

The internal inconsistency of those who disagree with my AI worldview does not reflect a deficiency of cognitive powers. Rather, it's the result of a specific behavioral bias: algorithm aversion…

This behavioral bias explains how investors can hold the contradictory position that though AI can help other businesses make better predictions and decisions, it is of less value in investing…

Interestingly, [hedge fund CQS's Michael] Hintze's comment reveals not only the problem of, but also a potential cure for, algo aversion: Give individuals a sense of control over the algos, and they are more likely to accept their forecasts…

[Mark Rzepczynski:] "If some modest amount of control is given to the decision maker, he will choose the algo over his own decision making. Allow the individual to adjust the model output by 10 percent, and [he is] happy. Allow the individual to reject 10 percent of the output from the model, and [he is] happy. Aversion falls when the human has some control over the model. You could say that the way to combat one behavior bias — algorithm aversion — can be through another bias, the illusion of control."

… I've come to see that the biggest challenge we face may not be developing powerful predictive AI-based investment models, but simply convincing investors not to trust their own judgment. More broadly, the winners and losers will be decided not by the current market position of a firm or even the size of its checkbook, but by its ability to overcome its anthropocentric prejudice and trust AI like it would trust a human being.

I have mixed feelings here. As a longtime employee of a "quant firm", I recognize the potential value in "black box investment decisions". You push the data through the models and recommended trades come out the other end. Human portfolio managers (PMs) are there to check these for reasonableness, and then sign off on the suggested trades, which are then are passed on to the trading desk for execution. Note that there is extensive testing for data irregularities, which do occur, so the PMs have "veto power" over the recommended trades. However, they cannot make trades based on "gut feelings" or "favorites", etc. However, this is not AI because the models are human constructs, are checked by humans, and can only be updated by humans.

On the other hand, (let's say) "HAL 9000" is designed to comb through googols of bytes of data, derive attractive investment targets, and then (gasp!) actually enter trade orders for execution. With potentially millions of dollars at stake on each trade, and the risk of bad data, that's scarier than an autonomous car in the Indy 500. Just because the AI made the decision does not necessarily mean that it's going to be a winning trade. If the investment goes south very badly, then who's to blame?

The investment management firm, of course.

Investment managers have a fiduciary duty to act in the best interests of their clients. They won't be able to throw up their hands and say: "Well, HAL 9000 made the trade". The very nature of IMAs (Investment Management Agreements) would have to change; and who knows how regulatory watchdogs would handle this? (For example: Look how long it took the SEC to accept electronic confirms and statements over paper; they're not well-known for being on the cutting edge of technology.)

While the author makes some good points, and may even be correct that AIs are the future of investment, there are still very pragmatic reasons why investment managers are reluctant to "bet the ranch" on the application of this technology.

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