Artificial Intelligence for Asset Management and Investment. Al Naqvi

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СКАЧАТЬ It is a loop where, after you act, you observe your environment again, reorient, decide, and act. Basically, you go through these loops, and you make your trades. Human traders, he argued, are constantly looking, second-guessing, checking their current state, analyzing their profitability, deciding whether they need to make a decision, what decision to make, reading the signals, understating the context, and taking action. As you go through the loop, you adapt and readjust. The AI apparatus he described was composed of random forest and an evolutionary rule-based genetic algorithm (an approach known as Pittsburgh-Style Learning Classifier System, which is used to evolve a solution composed of multiple rules). He claimed that news is a lagging measure in terms of useful signal since news is already reflected in the time series. At best, news can increase confidence in the signal, but it is not signal itself, he contended. Hence, if you genetically evolve the solution, a machine can pick patterns throughout the trading day, and while it can explore millions of options, it may take only a handful of buy-sell actions. He said that his hedge fund was fully automated and required absolutely no human intervention. In his fund, staff set things up, and they do have a big red button to press only if all hell breaks loose—other than that, the system decides everything: 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.

AI—A REVOLUTION OF ITS OWN

      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.

INTELLIGENCE AS A COMPETITIVE ADVANTAGE

We often hear the words “intelligent automation” in the context of learning systems. While there are various meanings attributed to this in the literature, I approach it as two different aspects: (1) what intelligent is and (2) what intelligent does. Using some of the concepts below, we will develop some theoretical foundations of why intelligent automation is critical for business survival and growth.

      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

Thirdly, while products and their production platforms are interlinked, both products and production platforms can be linked with other systems. They form a nexus or ecosystem of informing, sharing, and taking intelligent actions in accordance with each other's information and states. This system of interlinked intelligent agents—which may include both humans and machines—offers a new way to create competitive advantage. In their seminal work Strategy Safari, Mintzberg et al. introduced ten schools of strategy (Mintzberg et al., 1998). In the section below we will summarize those schools. The discussion in the next section is derived from Mintzberg et al.

INTELLIGENCE AS A COMPETITIVE ADVANTAGE AND VARIOUS STRATEGY SCHOOLS

      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 СКАЧАТЬ