By Andreas Roell
Last week, I had the pleasure of speaking on a virtual panel to an audience with a mix of quantitative and traditional asset managers and advisors. The conversation very quickly shifted to the topic of how to make allocators and investors comfortable with algorithmic investing versus what they are currently doing.
As our discussion progressed, it occurred to me that there is a good deal of work to be done to help bring allocators and investors along the educational journey to give them a confident foundation of how algorithms work and what questions to ask.
However, what I became more curious about is the level of questions or diligence that algorithmic/quantitative asset managers go through in comparison to traditional asset managers. Obviously, it is our responsibility to increase credibility and time will help in driving comfortability among investors. At the same time, however, I feel that there is an opportunity for algorithmic asset managers like ourselves to take a look in the mirror when exposed to perhaps imbalanced scrutiny.
Here is an example of what I am referring to. In a recent conversation with a fellow asset manager, I was asked questions specifically around our algorithms, such as: How do they work? What are the specific data sets that are used? What market triggers drive decisions? What are the specific signals that make our models work? Among others.
I tried to answer them as thoroughly as I could without giving away trade secrets and felt pretty satisfied with my answers, but one never knows, as some of the questions cannot be simply answered without getting into details.
Since he was a fellow manager of a hedge fund (over $200mm in AUM) who focused on “human based” investment strategies, in this case a sector specific pair trading approach, I started to turn the tables on him and began asking him counter questions.
Questions like: What are some examples of pairs that were successful for him last year? How does he typically identify a pair? How long does a pair signal last for him?
A couple of his responses were surprising. One was, “You are asking me more detailed and technical questions than my investors. Neither seed or institutional investors have ever asked me these questions in my 30 year investment career.” Second, “I am not able to tell you all the details as I would give you my recipe.” Funny, I thought, since these are questions I face in every conversation that I have with investors.
This contrarian perspective was validated a week later when I talked to the head of all products at one of the most well known financial institutions in the world. He as well appeared to start “geeking out” on us with curiosity the longer our conversation went. Here again, once he was done firing question bullets at me, I asked him simply if these are the types of questions his clients ask him when deciding or choosing from their offered menu of investment products. The clear answer was a laughing no.
I started thinking about why this is the case. Why is there so much more interest, diligence or simply curiosity in the detailed mechanics of investment decisions when it comes to algorithmic trading solutions than we have with traditional fund offerings?
It reminded me of my digital marketing days, when I started my first agency in the late 90s and helped a large portion of leading hotel companies use the early days of the internet to generate direct reservations. There, I had to compete for media budgets with the traditional formats, such as television ads. During that time, I just could not understand why every dollar I spent on an ad for them was held accountable to revenue generation and TV had no clue at all about their impact. Fast forward to now, and this perspective has changed in both directions. TV has lost a significant amount of their media budget share, while also being tracked as much as possible. While on the other side, digital advertising has become also a medium for immediate revenue generation (what is called Direct Response), but also for awareness and branding purposes.
So, the moral of the story here is that over time in advertising, marketers learned from both sides and applied layers from one to the other. This is something that I predict will happen in the asset management industry as well over time.
“Traditionalists” will become accustomed to higher scrutiny and detailed questions about their investment decision process, while the acceptance of algorithmic-at-the-core traders will increase. The level of understanding and comfort around proprietary decision making processes will increase. And as such, the algorithmic side of the coin will receive less scrutiny. This is at least my prediction.
Here at AlphaTrAI, where we are obviously an algorithmic asset manager, we believe that we have an obligation to increase the level of understanding and thus comfort that investors have at this point in time with algorithmic investing.
We aim to work hard first, at bringing more knowledge of this new approach into the investment community, and at the same time, we know that we need to show up as transparent and less secretive as much as possible. It is our belief that this is the obligation of all algorithmic investors. The more we do it, the better our collective reputation and investor willingness to shift to this new form of asset management will be. Just like what happened during my early days in digital advertising.
Happy AI investing.