Artificial Intelligence in Investment Analysis: The Future of Stock Picking

Mar 4 / Geoff Robinson

As the landscape of investing continues to evolve, so too do the methods used for investment analysis. Among the most prominent of these emerging technologies is artificial intelligence (AI), which is now being used to augment and improve the process of investment analysis and stock picking. This blog post will dive into how AI is changing the face of investment analysis and whether traditional banking institutions can keep pace with these rapid advancements.

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AI in Investment Analysis
AI is the capability of a computer program to learn and think. When applied to investment analysis, AI can process vast amounts of data quickly and accurately and make predictive models based on the patterns it recognizes1.

AI can analyze financial reports, news stories, social media posts, and other relevant information to predict how these factors might influence a stock's price.
 Additionally, machine learning, a subset of AI, can improve these predictions over time as the algorithm learns from its successes and failures.

Venture capitalist Marc Andreessen has famously said that "software is eating the world, "2, and AI is undoubtedly feasting on the financial world. Big data is increasingly critical in investment analysis, and AI is the tool that allows investors to make sense of this massive influx of information.

The Evolution of Investment Analysis
The days of simply analyzing a company's balance sheet and income statement give way to a more holistic and complex approach. Investors are now considering environmental, social, and governance (ESG) criteria, geopolitical risks, consumer sentiment, and other non-traditional data points. This increased complexity requires advanced tools, such as AI, to effectively analyze and draw insights.

Alok Kumar, Professor of Finance at the University of Miami, explains, "The more you can automate, the more you can focus on higher-level questions. AI tools can help [analysts] to digest large-scale information quickly and efficiently." 3

Can Big Banks Keep Up?
The question remains: Can large banking institutions keep pace with this evolution in investment analysis? The answer largely depends on their willingness to adapt and invest in AI capabilities.

Large banks have the resources to invest heavily in AI, and some, like JPMorgan Chase, have already begun to do so4. However, these institutions face challenges, including integrating AI into legacy systems and navigating complex regulatory environments.

The banking sector is generally conservative and slows to change, but using AI in investment analysis is not a fad—it's the future. Banks that fail to adapt risk being outpaced by more nimble, tech-forward competitors.


The potential for AI in investment analysis is immense. It has the power to analyze big data swiftly and accurately, consider a wide array of factors, and evolve with the changing market conditions. While big banks are well-positioned to leverage AI due to their resources, their ability to keep pace

"Artificial Intelligence: The Next Frontier in Investment Management?" CFA Institute, ↩
Andreessen, Marc. "Why Software Is Eating The World." The Wall Street Journal, August 20, 2011. ↩
"AI and the Future of Financial Modeling." The Enterprisers Project, ↩
"JP Morgan Is Unleashing Artificial Intelligence on a Business That Moves $5 Trillion for Corporations Every Day." CNBC, ↩