Much has been said about how artificial intelligence (AI) and machine learning might change the investment profession.
Whether it is the advancement of automated trading, using alternative data to find new investment signals, or robo-advisors becoming more capable, the main focus to date has been on how these tools will cause the investment manager’s job to evolve, or even disappear under more extreme scenarios.
Some believe that not enough attention has been given to the second order impact of AI on the industry, and what I perceive to actually be the much larger and potentially more interesting outcome.
By second order impact, I refer to the impact of AI on the industries and companies we analyse and invest in.
Market fundamentals, financial analysis and sometimes just gut feel drives a lot of investment decisions outside of quantitative fund management, but how will this traditional analysis change in the coming decade?
While there are challenges around talent and finding high value problems to solve with machine learning, industries are well on their way to implementing models across their value chains.
Will businesses compete for investments based on their level of AI adoption?
If machine learning modelling becomes central to speeding up key processes more accurately, very soon companies may be competing based on their data and modelling skills, and this will become crucial to their ongoing business growth. There are many such examples starting to develop.
But as an investor or analyst, how much do we know about a company’s AI activities? There are several related factors that need to be taken into consideration, when making investment recommendations.
It’s not necessarily only about the performance and skill of data scientists in organisations, but also how the modelling in information-based industries might be skirting the lines of ethics and allowable uses of personal information.
High profile issues are well known surrounding the likes of Facebook and Google collecting personal information and using it to generate revenue. All it might take is one data leak, a case of unintentionally disadvantaging a single demographic, or a change in regulation to cause these types of business models to become problematic.
The challenge we have as an industry going forward, at least for those who will be working for the next 20-30 years, is understanding what we need to know about wider AI activities, and in how much detail, to make informed investment recommendations.
Regulators and financial reporting bodies will also need to decide on how to regulate AI, and what companies should publicly disclose in order to help analysts and shareholders better understand the landscape and the risk.
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By Andrew Morgan, CFA , a management consultant working in the area of innovation and data science across financial services and CFA Institute Contributor.
Article originally published at the CFA Society United Kingdom website.
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.
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