Artificial Intelligence for Asset Management and Investment - Al Naqvi

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what to trade, when to trade it, how to trade it, how long to hold, when to exit. This all sounded like a dream come true for AI. It was perfection at its peak. As long as no one had to push the red button, things seemed in control. Except, in 2018, the hedge fund was suddenly liquidated. Bloomberg reported that the fund had not made any money in 2018, after making a meager 4% in 2017 (Kishan and Barr, 2018). The failure of the fund can be attributed to the approach, or to the technology, or to the lack of human guidance, or to the fact that no single strategy can be viewed as the winning strategy, or that perhaps strategy was not consistent with the market conditions. We may never find out. But what we can learn from this failure are two lessons: (1) the human role in investment management needs to be more than the red button pusher or cheerleader for a strategy; and (2) the role of broader business strategy is critical for investment management. The former because we know that human intervention in the strategy could have observed that the strategy was not working (actually, a machine could have helped point that out) if humans were not emotionally invested in the fully autonomous model and did not view the human intervention as counter to the business model. The latter because we operate in a dynamic system where the environment evolves and a change in strategy becomes necessary. It is likely that if the investment team were surrounded by a diverse set of thinkers, the chances of groupthink could have been minimized. Strategy is not just about investment. It should not be limited to investment strategy. The broader strategy for the organization is just as important, if not more. This chapter is dedicated to strategy building, and its value will become clear as we go through the book.

      I vividly recall the experience of buying a new car about a decade before this book was written. I visited several dealers and inquired about various features. It was not my first car. I had purchased a total of nine cars before that. When buying cars, I tend to ask lots of questions. From typical car performance attributes to its physical features, I asked dozens of questions. I looked at domestic cars and foreign cars, at sedans and SUVs, at electric and gas cars. Eventually I ended up buying a car. Looking back at the questions I asked during my car buying escapades from 1992 to 2010, I realize that the nature of my questions did not change much. Then I made of list of questions I was asking to buy cars recently, and suddenly a list with very unusual questions emerged: “Does this car park itself?” “Does it stop itself if a peril develops?” “Does it drive itself?” Stop for a moment and ask yourself what just happened. We are asking questions about a thing (the car) and associating some level of sentient or intelligent behavior with it. Something has changed. We are expecting things to be intelligent. In the past, beyond humans, such a question may have been asked for a horse or a dog or a cat—“Would this animal be able to return home?”—but not for an inanimate object. What changed?

      Of course, we are now living in the intelligence era. What was once uniquely ours, intelligence, is now expected to be part of inanimate objects. This means that as consumers we expect products and services to be intelligent and to display intelligent behavior. Intelligization of objects is not a small shift. It introduces many different types of business dynamics as it alters the fundamental drivers of competition.

      For instance, one key factor intelligization introduces is that in addition to all other product or service attributes, the fundamental driver of competitive advantage can also be the intelligence embedded in your product or service. For examples, consumers would now compare cars not only based on factors such as quality and safety but also based on autonomous driving features. Intelligence has become a primary attribute of competitive differentiation. People may compare smart phones, their bank services, credit cards, home security systems, financial advisors, and even sofas and toilets based on product intelligence.

      Let us first observe what intelligence means in the form of products and services. It has three related implications:

      Intelligence in Products

      As the previous example illustrated, what changed in the modern economy is that now you expect your things to have intelligence. Whether it is business systems or personal, it is as if objects have come alive or developed a mind of their own. You do that when you order your smart phone assistant to make a call, check emails, or search for something. The objects around us are now embedded with intelligence, and that itself is a powerful change.

      Intelligence in Production Platforms

      In addition to embedding intelligence in products, what really drives competitive value is having intelligence in the production and operational platforms of a firm. This means that all the production environment that is used to create, manufacture, offer, distribute, and service the products of a firm is also made intelligent. In this context I am using the term “production platform” to signify all the activities necessary to get the product to a point where it can be consumed.

      Intelligence of an Interlinked Network of Systems

      The Design School “proposes a model of strategy making that seeks to attain a match, or fit, between internal capabilities and external possibilities” (Mintzberg et al., 1998). From a design school perspective, products, production platforms, and interlinked systems are designed to take advantage of the opportunities by the intersection of internal and external possibilities. It is assumed that in the cognitive era, the assessment of what internal and external states are would also be performed by an intelligent engine (or engines). From that perspective, every product, production platform, and interlinked network is responsive to the inner states and capabilities of a firm as well as to what transpires outside.

      In the Planning School the combination of planning tools (for example, SWOT) and mission statements of executives and leaders of companies are used to create short-, medium-, and long-term plans for companies. From that perspective, the intelligence-centric planning implies that tools and methods that decipher strategy are applied; however, their implementation will be quite different than in human-oriented strategy development. For example, the strengths, weaknesses, opportunities, and threats need to be analyzed for automated intelligent systems.

      In the Positioning School, unlike the Design and Planning schools, which placed no limits on the number of strategies that a firm can have, Mintzberg et al. explain “only a few key strategies—as positions in the economic marketplace—are desirable in any given industry: ones that can be defended against existing and future competitors.” The emphasis on competitive dynamics clarifies that in the era of intelligent automation, the competitive dynamics are defined based on intelligence in products, production platforms, and interlinked network of systems.

      The Entrepreneurial School is based on vision and vision setting and is usually centered on one leader. In this school the entrepreneur develops a vision, a mental model, and applies skills to create value. AI can assess

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