Artificial selection

Q&A: Artificial Intelligence and How It Shapes Investment Decisions

Aisan shares rose in early trading as Tokyo jumped more than 2% – Copyright AFP Kazuhiro NOGI

Artificial intelligence has started shape investment decision making, with investors taking advantage of the opportunities provided by algorithms to gain predictive insights into data. Technology is playing a vital role in the investment world as companies look for ways to incorporate artificial intelligence capabilities into the search and selection process. With the power of machine learning, the ability to scale, adapt and identify patterns, investment managers can leverage this technology to potentially improve performance and improve efficiency.

For example, in January 2022, SoftBank announced it was investing $146 million in a provider of artificial intelligence investment solutions, Qraft Technologies to accelerate the development of artificial intelligence in asset management. As the industry has been a late adopter of technological innovation, Softbank’s investment could pave the way for solutions in a future market environment where achieving alpha may be difficult in years to come.

Digital diary took the opportunity to speak with Robert Nestor, former head of BlackRock iShares Factor ETFs and president of Direxion ETFs. Nestor is currently the US CEO of Qraft Technologies. Qraft is an AI-enabled investment technology company that develops and operates deep learning-based algorithms for asset management and wealth advisory firms.

Digital Journal: How is artificial intelligence improving traditional fundamental analysis in stock picking?

Robert Nestor: Some people think that integrating AI into investment management is about programming the robots to make all the investment decisions. However, true practitioners in this space understand that human intuition is very powerful, so attempting to completely remove humans from the process makes little sense. Although traditional fundamental analysis may rely on some facets of AI techniques, it is still a human-driven decision-making process. However, humans, and machines for that matter, have limitations.

What naturally differentiates us from machines is our emotion, which most people would say is a positive thing but generally not a value added investment. By integrating AI technology, the emotional aspect can be largely eliminated. More importantly, the human brain, while powerful in its decision-making, cannot match the scale, reach, and speed potential of machines. AI in investment management is inherently about combining the best of both worlds, which means we use AI techniques to scale analysis and decision-making processes, but always with some form of human oversight. .

DJ: What types of data do AI algorithms process in the investment decision-making process?

Nestor: Data is the power source of any AI process, and AI investment processes are no different in this respect. The more the better and, in theory, there is no limit to what could be relevant, i.e. what could be predictive of investment returns. In practice, usually only a very small subset of data is usually useful. Over 99% of data is just noise with no predictive power, but you can’t know that until it’s analyzed. The data can run the gamut from traditional stock-specific data (prices, earnings, etc.) to less traditional data such as employee growth or patent levels. But it’s not just raw data that might be relevant, derived data such as the various data relationships (often overlooked), and their correlation and intuition to secure returns. These potential relationships can grow into the hundreds of millions, or even billions, very quickly. There is also macroeconomic data and so-called unstructured data such as web traffic, image recognition, etc.

DJ: Why has traditional asset management been slow to adopt artificial intelligence in decision-making?

Nestor: I often say that the most powerful force in nature is inertia. It’s a very different approach to what’s standard in the profession today, and most people don’t want to change what they know and feel comfortable with. I think there are four main reasons why the industry is slow to adopt AI:

  • There is still a widespread and fundamental lack of understanding of AI. While that clearly hasn’t stopped widespread adoption in other industries, I’d say most people think understanding how AI will help manage money is more important than understanding how AI will help manage money. ‘AI dictates routes in Google Maps. It is difficult to disagree with this view.
  • Early AI practitioners moved into other industries such as healthcare, marketing, etc.
  • Building AI-driven investment platforms and capabilities from scratch comes at a significant cost.
  • There are fears that decision makers will be replaced. The goal is not to eliminate humans from the process, but you need fewer people in the process.

That said, I think there are some favorable winds for AI adoption, including:

  • There will likely be a lower yielding investment environment in the future compared to the past decade.
  • There will be continued pressure on fees/margins in asset management, which will further push for scale.
  • The growing influence of ESG in investment decision making, which is a huge data cleaning challenge.
  • There are increasingly complicated investment challenges as a larger population retires and dips into their wallets.

DJ: How do you effectively develop AI capabilities as an investment manager?

Nestor: It is not easy. It starts with people, which may not seem intuitive for what is largely a technology-driven process. But you need to have people who deeply understand AI processes and techniques such as machine learning, deep learning, attention models, etc., as well as a strong sense of investment to apply it correctly. You cannot be effective without a pronounced experience in both areas. You also need time and a highly disciplined approach to building, researching, refining, and evolving AI models. It can take years to be confident in the effectiveness of what has been built. Qraft started in 2016 and few platforms have our experience. You have to feed the beast – data is the lifeblood of the AI ​​process, and large datasets can be expensive to acquire and clean.

DJ: What is the impact of artificial intelligence on investment costs?

Nestor: Frankly, I don’t think that’s the case yet, because its adoption isn’t widespread enough yet. But I think it will inevitably happen and could have a profound impact. A largely technology-driven process will simply provide more scale and efficiency over time, reducing the cost of investing for alpha goals. It will take at least a few years to materialize, but AI will inevitably play a significant role in investment decision-making processes, reducing costs for all investors.

DJ: How is AI a positive force for people interested in ESG investing?

Nestor: Clearly, interest in ESG issues has a fundamental impact on investment decision-making. More and more investors are looking to improve the world through their use of capital, on their terms.

However, there are major practical challenges. First, reliable longitudinal data to define companies’ ESG attributes is still scarce, but it is being methodically discovered, and some really exciting work is being done to accelerate it by companies like EMAlpha.

Second, not everyone agrees exactly on how and for what cause(s) they wish to allocate capital. The desire for more personalized solutions in this space is likely to grow tremendously. Data and delivery at scale are hallmarks of AI, and the techniques used here will only accelerate the entry points for more personalized solutions and confidence in ESG-driven investment decisions. .