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Artificial intelligence, or AI, may seem like science fiction to many people, but recent breakthroughs are opening the door to new possibilities. The Rise of Big Data provided the raw materials; graphical processing units (GPUs) provided the hardware; and new algorithms expanded the application of AI to new fields, such as language processing.
AI-powered technologies have also made their way into finance with the rise of algorithmic trading and robo advisors. A new crop of fintech companies are further leveraging artificial intelligence (and machine learning) to do everything from automating savings to managing risk for both individual consumers and institutional investors.
Let’s take a closer look at artificial intelligence, the implications for portfolio management and some drawbacks to keep in mind.
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Artificial intelligence is a broad term used to describe any technology that makes computers behave in a way that previously required human intelligence. For example, Apple’s Siri and Amazon’s Alexa use natural language processing (NLP) algorithms to interpret language whereas AlphaGo defeated a world champion Go player in a highly complex game.
Some of the most important AI techniques include:
Machine learning is arguably the most popular branch of artificial intelligence and is focused on developing algorithms that automatically improve through experience. For example, Pandora’s music recommendation algorithms use machine learning algorithms to compare your listening history and ratings with those of other users to make intelligent suggestions.
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Artificial intelligence could fundamentally transform asset allocation, trading processes, risk management and other areas of portfolio management. In fact, many robo advisors already use these technologies to deliver portfolios with better out-of-sample performance for investors while rebalancing and automatically managing risks with minimal transaction costs.
Some of the most popular portfolio management applications include:
These possibilities are likely to expand over time as artificial intelligence techniques evolve and expand into new areas. Portfolio managers should keep an eye on these trends, particularly as robo advisors and other competitors are actively exploiting them.
Artificial intelligence makes decisions using complex neural networks, genetic algorithms and other techniques. While these strategies tend to produce better results, their sheer complexity makes it difficult to understand what’s happening under the hood. The “black box” algorithms may include inherent biases or be unprepared to cope with “black swan” events.
There are also several data-related challenges with AI:
Artificial intelligence also requires an extensive use of technology in general, which can increase a firm’s risk exposure. One example would be a cybersecurity breach that exposes sensitive data, but other examples might include data loss that impacts the efficacy of AI algorithms or the loss of an algorithm to a competitor that hurts a firm’s competitive edge.
Artificial intelligence is a broad term that covers many different concepts, including machine learning. With advances in big data, GPU processing and deep learning algorithms, the financial markets have started to experience a significant shift. Portfolio management in particular could benefit from many of these trends.
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