The Need for Authentic AI in Financial Markets

By Andreas Roell


The Merriam-Webster dictionary defines “Authentic” as both “worthy of acceptance or belief as conforming to or based on fact” and “conforming to an original so as to reproduce essential features”.  I caught myself using the phrase “Authentic AI” in one of my discussions and it made me think about this concept that I have been using for years.  What does it truly mean to me and others (yes – I am not the only one using it)? 


Contrary to the standard definition of the word “Authentic” above, the authenticity of AI, is really not a matter of “Fake” to be contrasted with “Real” but rather, about the essential features of AI which need to be acknowledged in a particular use case and what these “authentic” features are.


It does not necessarily mean that algorithms must be custom or built from scratch, if the applicability into a certain application is acceptable.  It simply means, in layman’s terms, that the models fit for a specific use case.


In order to reach the necessary level of requirements, it is essential to deploy a full immersion approach to a specific application or sector. In our case at AlphaTrAI, for us to truly deploy authentic AI, it was required to deeply understand, simulate and develop the necessary scenarios applicable to the highly dynamic and ever-unique conditions of the financial markets. Looking at one of my previous posts on the ever changing rules of the financial markets (you can find here), it should become very clear that off-the-shelf AI models for prediction and detection may function as a base for Authentic execution in financial markets but cannot be the solution. 


In my mind, these are the key building blocks of Authentic AI for financial markets:


  • Models that successfully function for prediction and detection based on very small data sets. This is due to my previous argument that markets cannot be predicted successfully in the long-term if the models are reliant on patterns created from long data periods. Markets are fast and ever changing.
  • Connected to the previous point, is to operate with models that are more “general” in nature when it comes to identification and comprehension of market conditions. Solely relying on models that function on predefined signals are not only limited in possibility of survival (At AlphaTrAI, we operate under the belief that each signal is like a star in the universe that changes in brightness or effectiveness and evaporates at one point) but also become very ineffective in fat tail market scenarios. Ability to detect market rotations in order to identify underlying dynamics of the market. So the answer to this very “tactical” form of algorithmic execution is to either function on, or at minimum, provide an “air cover” model framework that has the ability to recognize market regimes. Such a general concept of algorithms can be compared to an EQ element of a human’s decision making process, which we all have learned the older we get, enables us to make more rational situational decisions.
  • The ability for the algorithmic models to solve the entire problem versus only a portion of it. For financial markets this will mean that an integration of models should not only be able to decide what to trade, but must also have the ability to successfully execute the trades. A common shortcoming of back tests is seen when the models trained are not set up for slippage and executions for high volume positions, making the real trading results vastly different to these simulations. Another example is the integration of trading across a multitude of asset types or short-long positions.


Having been an operator for a while in the financial markets, I have noticed an acceleration of “AI based” financial offerings.  As a result, I see a significant amount of interest in AI strategies from both retail and institutional investors.  Where I am taking a leap is, and what my many years in AI have shown me, is that only an “Authentic approach to AI” can effectively work in financial markets. My findings on the current state of the majority of AI executions are such that are heavily dominated by human engineered rule-based systems, non-fully automated decision making and execution of models with a heavy reliance on a very narrow (often single) set of signals. 


So I encourage you to consider the authenticity of AI before making investments into an AI based investment product. Maybe even use some of my points as part of your due diligence list.


Happy AI investing.