This is the second installment in a three-part series exploring the impact of artificial intelligence (AI) on investment management. I want to thank the speakers at the AI and the Future of Financial Services Forum, hosted by CFA Institute and CFA Society Beijing, for inspiring this series. The first installment offered a primer on the AI technologies that are relevant to investment professionals.
Artificial intelligence (AI) is coming to the investment world.
With the help of deep learning techniques, AI researchers have made significant strides in natural language processing (NLP), speech recognition, and image recognition. Computers can now see, hear, and understand human beings. They have also demonstrated shrewd decision making.
What does this mean for investment management professionals?
In December 2017, we invited some of the brightest minds in AI and investing to discuss how AI is transforming the investment business at the AI and the Future of Financial Services Forum in Beijing. Their collective conclusion was nothing short of mindboggling: AI will eventually replace most, if not all, investment managers.
Let’s walk through their reasoning step by step.
By such common standards of intelligence as language skills, mathematical skills, and memory, computers are gaining an edge over humans. That margin will only grow wider over time. Will that edge translate into better investment skills?
“The biggest advantage of a computer [over a human being] is its practically unlimited memory,” Eric Chang of Microsoft Research Asia explained. The 152-layer-deep neural network his team at Microsoft developed can tell what’s in a picture with more accuracy than humans.
Training such complex models requires a tremendous amount of data, more and more of which has become available in recent years. “Data alone is not enough though,” Chang said. “Our focus is on getting insights from the data.”
Tang Xiaodong, CFA, CEO of China Asset Management, gave the audience an example of such insight. Investors have used image recognition programs to find oil tankers on satellite imagery. “Some have been able to get a better gauge on oil supply by analyzing the tankers’ tonnage, routing, and port arrival times,” he said.
Many analysts listen to quarterly conference calls from corporate management to detect clues that they can use to estimate corporate earnings and build valuation models. “With the help of voice recognition programs,” Tang said, “they can zoom in on a small number of companies where AI raises a red flag based on changes in management’s speech patterns.”
Shu Ming of Lingfeng Capital explained how one of their portfolio companies applied AI algorithms to help a bank client evaluate its risk exposure to a potentially problematic borrower:
“We used NLP and knowledge maps to go through regulatory filings, legal proceedings, and online information about related transactions, company ownership structures, business transactions, loan guarantees, and key personnel movements to map out corporate relations. The program detected over 800 accounts related to the problematic borrower. The banks originally thought there were four.”
Chang said that Microsoft applies its image recognition models to understand investor personalities. They can then harvest that data to build more customized portfolios, demonstrating how deep analysis can inform better decisions.
Better investment decisions come, in part, from more precise asset pricing. More-in-depth analysis provides more accurate inputs for valuation models. For example, if the information on oil tankers gives you an edge over your competition in forecasting oil prices, it will also help you better model revenues and costs for oil companies and airlines. If your program succeeds in catching CEOs in their mistruths on conference calls, you’ll likely capture alpha by selling those companies when you hold them and avoiding them when you don’t. And is still capable of accomplishing so much more.
AI’s freedom from emotions and behavioral biases should also lead to better investment decisions. Although neural networks operate in different ways than a typical quant model, they share that same lack of emotions. (More on the difference in the next post.) And as the saying goes, “The market does not beat them. They beat themselves . . . ”
Behavioral biases will continue to influence our investment decisions, often to our detriment. For example, investment managers are often prone to herding, or following the crowd. At the height of the tech bubble, for example, too many investors chased a stock simply because management added a .com to the company name.
But machines won’t follow the next machine. Unless we program them to do so.
The End Game
“To invest successfully over a lifetime . . . what’s needed is a sound intellectual framework for making decisions and the ability to keep emotions from corroding that framework,” Warren Buffett wrote in his preface to Benjamin Graham’s The Intelligent Investor, which Buffett described as “the best book about investing ever written.”
Given AI’s superior brain power and lack of emotions, Tang believes the market will eventually be dominated by a small number of AI programs, maybe even a single one: “If an algorithm eventually beats all the rest, you’ll have to either hand over your money for it to manage or withdraw from the market entirely,” he said. “You cannot afford to keep losing.”
Case in point: The Man Group, a hedge fund, had an AI program manage a small portion of the assets in one of its largest funds. By 2015, the AI accounted for roughly half the profits.
AI also has support in academic circles. Campbell R. Harvey of Duke University believes AI will assume a major role in investment decision making and that the proliferation of AI and big data will result in “15 to 25 investment management superpowers that can harvest all that data.”
So the big question is when — not if — AI will supplant human investment managers.
For more from Larry Cao, CFA, check out Fintech 2018: The Asia Pacific Edition.
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All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.
Image credit: ©Getty Images/ nevarpp
Larry Cao, CFA, senior director of industry research, CFA Institute, conducts original research with a focus on the investment industry trends and investment expertise. His current research interests include multi-asset strategies and FinTech (including AI, big data, and blockchain). He has led the development of such popular publications as FinTech 2017: China, Asia and Beyond, FinTech 2018: The Asia Pacific Edition, Multi-Asset Strategies: The Future of Investment Management and AI Pioneers in Investment management. He is also a frequent speaker at industry conferences on these topics. During his time in Boston pursuing graduate studies at Harvard and as a visiting scholar at MIT, he also co-authored a research paper with Nobel laureate Franco Modigliani that was published in the Journal of Economic Literature by American Economic Association.
Larry has more than 20 years of experience in the investment industry. Prior to joining CFA Institute, Larry worked at HSBC as senior manager for the Asia Pacific region. He started his career at the People’s Bank of China as a USD fixed-income portfolio manager. He also worked for US asset managers Munder Capital Management, managing US and international equity portfolios, and Morningstar/Ibbotson Associates, managing multi-asset investment programs for a global financial institution clientele.
Larry has been interviewed by a wide range of business media, such as Bloomberg, CNN, the Financial Times, South China Morning Post and the Wall Street Journal.