AI Tools Bring Institutional Market Data Closer to Retail Investors

AI is changing the value of market data by making it easier to search, combine and interpret. Brandon Tepper, Nasdaq’s Senior Vice President and Global Head of Data, says those capabilities are extending information and analytical tools once associated with large financial institutions to individual investors. The shift could narrow the practical gap between professional and retail users, though the usefulness of any AI output still depends on the data behind it.

“AI is really helping investors gather data in a more easy to use fashion to help tell stories from that data so that they can make informed investing decisions,” Tepper told Traders Magazine. In his view, AI’s role is not just to deliver more information. It can help users make sense of existing datasets and use them to investigate market questions.

Nasdaq is seeing new clients, applications and use cases as financial institutions, technology providers and individual investors look for ways to draw more value from data they already have. The change spans retail investors, institutional traders and quantitative funds. For data providers, that means serving more kinds of users and supporting tools that can work with information in new ways.

Data quality sets the limits

Tepper’s central caveat is that AI does not make weak information reliable. Nasdaq operates 18 exchanges globally, and its exchange data can serve as a foundation for analysis alongside complementary and alternative datasets. That layered approach gives users a starting point, but combining more sources also makes it important to know where information came from and whether it is accurate.

“In order to have good AI, as you know, you really have to have clean, trusted, and accurate data,” he said. Tepper also warned that AI systems can be wrong and that their predictions depend heavily on the information they receive. “The value of the underlying data is the basis for good AI, and there are risks of using incorrect data,” he said.

That dependence matters when an AI tool produces a confident-sounding explanation or suggests a response to changing market conditions. Users need controls, security measures and a clear understanding of what an application can and cannot do. If AI tools move from answering questions toward carrying out actions, the quality of those safeguards becomes more consequential.

Retail access moves closer to professional tools

Tepper sees a narrowing gap between retail and professional investors. “I think the gap is getting very narrow,” he said. Access to certain information once depended on expensive financial terminals or proprietary datasets; technology platforms are now making more market intelligence available to individuals.

Nasdaq’s expanded relationship with Robinhood is one example. The two companies have worked together for more than a decade, with the data offering growing from equity market data to more sophisticated information, including Nasdaq TotalView. More recently, Nasdaq began supplying alternative datasets through Robinhood’s Agents ecosystem. Users can interact with AI agents that draw on market intelligence to answer investing-related questions.

“We’re not just streaming live data, we’re helping investors utilize it in a more agentic way,” Tepper said. He sees the potential for individuals to set up algorithmic strategies around predefined market conditions. That would make access to data only one part of the change: users could also have tools that help translate conditions they specify into an investing process.

The interest reflects a more sophisticated retail audience, including traders Tepper describes as “pro-tail,” who seek more advanced market information. Yet greater capability comes with added responsibility. An automated process can act when specified conditions are met, so users need to understand those conditions and the possible limits of the tool before relying on it.

Tepper pointed to Robinhood’s safeguards for agent-based functionality: users must establish and separately fund an account, and access to information outside the platform is restricted. He said the data is not used to train the underlying AI models. These controls speak to questions that become more important as applications gain the ability to support automated trading activity.

“If you have the right rails and the right security and risk profile, and understand what it can and can’t do, it’s going to just get better and easier to use,” Tepper said. The practical test will be whether users can both get useful answers and see the boundaries around the tools’ access and actions.

Data delivery adapts to longer trading hours

Nasdaq has been preparing its datasets for AI-driven applications for several years, developing AI-ready data delivery capabilities and support for Model Context Protocol (MCP). Tepper said AI now features in conversations with existing and prospective clients, startups and individual investors. That demand is arriving alongside another change: the market’s push toward longer trading hours.

Nasdaq is expanding across regions and product categories, with more than 250 products available through Nasdaq Data Link. Tepper said the company is preparing for greater demand for market information beyond traditional trading hours as the industry moves toward 23-hour, five-day trading and possibly continuous markets. Nasdaq has been testing new data feeds ahead of a planned December 6 launch of 23/5 trading.

Longer sessions change when investors may want to access information, and they increase the need for data services that operate outside familiar market hours. Tepper connects that demand with consumers’ expectations of being able to access services at almost any time. “Why not have the ability to invest in your favorite company at all hours of the day?” he asked.

He sees opportunities for both retail and institutional investors as trading hours expand, while emphasizing the need for appropriate risk controls. His broader view is that AI will support increasingly sophisticated agent-based investing as access to information improves. The near-term measure of progress will be whether cleaner data, usable tools and safeguards develop together as investors gain access to more information and longer windows in which to act.