The Value-Add of AI in Investment Management

Responding to the increasing complexity of financial markets, Asset and Wealth are introducing Artificial Intelligence (AI) into the investment process to clearly guide their investment decisions. From regime analysis to portfolio construction, there are many areas in which AI has proved capable of adding value and efficiency to investment strategies. Nonetheless, it is the ability to improve the signal-to-noise ratio and focus on a holistic understanding of market dynamics that truly sets the adoption of AI in to the investment process apart.

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Jonah Hill plays a recent Ivy League graduate who explains to Beane (Brad Pitt) how data can help find undervalued baseball players - “Moneyball” (2011).

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‍AI: a game changer in investments

AI has re-defined a wide array of industries by allowing us to process data more efficiently and dramatically improve our ability to understand and adapt to the complexity of the real world. 

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For these reasons, AI is being increasingly adopted also by the investments industry. Indeed, the introduction of AI is taking shape at a time of increasing complexity and competition in financial markets, making the chase to find information hidden in the noise more important than ever.  However, rather than a totally new framework, AI seems to step out of the old quantitative approach to investing, by overcoming the well-known limitations of traditional models (the inability to quickly adapt when things change and understand the structure of time-varying and complex relationships) and better comprehending the underlying dynamics of markets. Indeed, as financial markets represent a traditionally complex and dynamic environment, the opportunity offered by AI is twofold: improve the quality of investment decision-making and quickly adapt to the gradual evolution of financial markets.

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The scientific method in the world of investments

A very useful example to understand the role of AI in investing is offered by looking at how pharmaceutical companies develop a new drug. For instance, in the last few years, finance has heavily drawn from the body of knowledge developed in many other fields of study such as biology and medicine, especially for what concerns the application of the scientific method to investing.

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Let’s take as an example the development of a new drug. The biggest effort for researchers is understanding the cause-effect relationships between the drug composition and bacteria, so as to deduce their interactions with human cells and justify why patients will get better after taking the drug. Only at a later stage, when the final drug has passed several tests to account for the possible side effects, will they proceed with the clinical trial, instead of starting out by trying the drug directly and seeing if it works to cure a disease. Similarly, applying statistical techniques to study the behaviour of financial markets without a thorough understanding of their underlying mechanisms may prove to be inadequate, not only for the high degree of complexity that they involve, but also to keep pace with their constant evolution.

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Being able to effectively separate the signal (the part that contains useful information) from the noise (which does not contain any) is therefore essential to model and control the interaction between the many variables involved and obtain valuable insights. Starting from the knowledge of the underlying dynamics of financial markets and their economic and behavioural rationale, the scientific method helps in finding out how financial markets will play out under many different circumstances and have control over their evolution. 

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As a matter of fact, a traditional quantitative approach to investing could look like developing a new drug and directly trying it on a few patients to evaluate if their conditions improve. AI’s approach to investing is more similar to discovering how the drug’s molecules interact with human cells, how they impact antibodies and proteins, and after that testing it on patients.

The two stages of drug development

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Think of AI as Moneyball

This is because AI enters the stage to help decision-makers create value by extracting insights from vast amounts of data. Quoting the famous CNBC reporter, Eamon Javers: “Think of AI as Moneyball, but not just for baseball, for everything”. In fact, AI’s role in the investment process is similar to how predictive analytics changed baseball twenty years ago, inspiring the movie “Moneyball”. 

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“Moneyball” tells how in 2002, Oakland A’s General Manager Billy Beane completely transformed the way teams were built, by adopting unbiased and data-driven assisted decision-making to eliminate biassed factors, such as players’ reputation or appearance, thus truly unlocking their value. And, this is what AI does in financial markets: it helps to make sense of complexity by turning data into meaningful insights, and it is critical to mitigate typical human biases, like overfitting, ensuring more reliable results. And if unbiased and data-driven assisted decision-making led Coach Beane to 20 consecutive victories, today AI is making it possible for investment managers to unlock unseen value also in investing.

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A necessary evolution rather than a complete revolution

In contrast to AI models, traditional models only take into account a fraction of the relevant information available, remaining at the surface of what is really needed to understand a given market dynamic. AI instead, works as a sophisticated scanning system skimming the depth of data, bringing to light the information that stays hidden behind the noise.

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Traditional vs AI-driven analysis of financial markets

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Eventually, with a more holistic, rather than narrow, understanding of the inner workings of market dynamics, it becomes possible to see all the risks and opportunities to make more informed investment decisions, while reducing the risk of being hit by unexpected events that causes strategies to not deliver expected results.

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By leveraging automated tools to identify promising model configurations, understanding the evolution of results over time and analysing interactions with other variables, AI-driven models do the work of thousands of analysts, thus making the chase for the statistical relevance of the investable signal hidden among data possible. Therefore, by using AI to gain control over the market dynamics, asset and portfolio managers can develop investment strategies that are better positioned to turn the valuable pieces of information they collect into profitable and resilient portfolios that help them navigate market turmoil.  With an improved signal-to-noise ratio and a holistic, rather than narrow, understanding of market dynamics, AI seems to be the secret weapon that will lead investment managers to 20 and maybe even more victories, as was the case for Oakland A’s General Manager Billy Beane, back in 2002.