The advent of artificial intelligence, or AI, has created incredible efficiency in many industries, but less so in the financial domain.
True, AI has improved security trading, however, the broader asset management sector has been slower to accept it due to factors including a low level of understanding of the artificial intelligence process and a group of AI talent talent that has moved into other industries. Finally, the massive move towards passive indexing over the last decade has resulted in little incentive to develop entirely new alpha generation platforms.
Nevertheless, artificial intelligence brings significant potential to disrupt asset management in the coming years, especially in stock selection, advising, and risk management. And as its capacities continue to develop, we see five drivers accelerating this movement.
Alpha in a low return environment. Even though U.S. markets have experienced tremendous growth since the 2008-09 financial crisis, and few predict that the S&P 500 will continue to generate returns close to those of the past 12 years. The move to a lower-yield environment will encourage a renewed focus on achieving “alpha” or excess yields, which will slow, if not even out, the growth of the index adjustment approach. AI has the potential to transform fundamental big data analysis and processing, making it fast, efficient, and cost-effective. At the moment, the results are short, but they show a promising alpha delivery.
Robo-advisors. The delivery of advice on these platforms comes in the form of pre-selected portfolios of stocks, ETFs and mutual funds seeking alpha in accordance with the set risk parameters. AI can further automate this process, making portfolio construction more precise, flexible, and scalable in scope - allowing advisors to serve multiple clients at a fraction of current costs. While there has certainly been progress on this front so far, most have focused on the part of the advisory model that provides services to clients, and less on the driver of the investment decisions themselves.
Return to risk management. During the 12-year bull market, what you invested in mostly didn’t matter - as long as you invested. With a few exceptions, risk and volatility were low by historical standards during that time, leading many investors to place less emphasis on risk management. But more modest returns and typical levels of volatility will increase interest in risk management among investors, and artificial intelligence can make it a more efficient and effective process by visualizing, appearing on the surface, and responding to evolving risk dimensions on a scale unparalleled by human judgment alone.
The emergence of ESG. Despite the current headwinds, interest in environmental, social and managerial factors promises to fundamentally influence stock selection and portfolio building as investors seek to improve the world through the use of capital. But for ESG investing to deliver on its promise, it will have to overcome two practical challenges. First, reliable data on ESG attributes of companies is missing. Second, customized solutions relating to how and what causes of capital allocation are likely to increase enormously. Data delivery and scope are features of artificial intelligence, which can help process and frame ESG data, while producing deeper analytics that improve understanding of ESG investment and its potential return, thus enabling customized solutions.
The wave of retirement is moving. A large decumulation of the portfolio is underway as approximately 10,000 people in the U.S. turn 65 every day. In relation to accumulation, spending from the pension portfolio is a more complex undertaking, especially with the disappearance of pension guarantees and the transfer of risk to pensioners in the form of 401 (k) s and the IRA. Accumulation options are of limited benefit to retirees in the event of a health crisis or market crash, especially when withdrawals are needed to cover significant non-discretionary spending.
Although not an elixir of inadequate savings, the ability of artificial intelligence to aggregate a number of interrelated variables and respond quickly to change has the potential to make the accumulation process easier through integrated portfolio allocation, risk and spending optimization - among the most complicated combinations of investment professionals.
As AI becomes more capable of performing the above functions, and asset manager resistance to technology decreases, it will be important to assess the expertise and resources of potential industry-specific artificial intelligence suppliers. Factors such as experience in all asset classes should be considered, as well as whether suppliers have the resources to maintain support in the future.
Photo illustration by Barron’s advisor
Robert Nestor is the U.S. CEO of Kraft Technologies, a technology company that is driving growth in the asset management industry through its innovations in artificial intelligence (AI) and investment. Nestor has over 30 years of experience in the industry, previously as President of the ETF Directorate and Head of the iShares Factor ETF.