Why Investment Professionals Need Both Quantitative & Fundamental Skills
August 2026
Searching Smarter Podcast: Why Investment Professionals Need Both Quantitative & Fundamental Skills
Could AI help investors make better decisions without taking the decision out of their hands?
According to Ying Hua, founder and CEO of Implied, the future of investing may sit somewhere between fundamental and quantitative approaches. AI can accelerate research and help investors process more information, while human judgment remains critical.
In this episode of Searching Smarter, Ying joins host Jesse Skaff to discuss how fundamental and quantitative investing can work together, where automation adds the most value, and why professionals who can combine investment expertise with technical skills may be better positioned for what comes next.
About the guest
Ying Hua is the founder and CEO of Implied, an AI-native equity investment research firm. Having previously worked in investment roles at Goldman Sachs and Balyasny, she brings experience across both fundamental and quantitative investing.
That combination shapes a central idea in this episode: investors do not have to choose between data and judgment. The opportunity is understanding where each is most useful.
Three ideas worth taking from this episode:
1. The future may sit between fundamental and quantitative investing
Fundamental and quantitative investors approach markets differently, but both are ultimately looking for alpha. Ying believes the future sits somewhere between the two, combining forward-looking investment judgment with data, historical patterns and quantitative testing.
2. Human judgment still has a role
Ying separates investment decisions into two parts: the direction to take and how much capital to commit. She believes direction still requires human judgment because the future may not always follow historical patterns.
How much to invest, however, can be more data-driven. Rather than letting conviction or emotion determine position size, Ying points to factors such as volatility and what else is in the portfolio as important considerations.
3. AI should accelerate the work, not replace the thinking
AI can help investors automate repetitive research, process more information and reach higher-value analysis faster.
Ying saw this firsthand when she asked fundamental equity research analysts on her team to learn Python. The aim was not to turn them into expert programmers. It was to help them read and write code, identify where research processes could be automated and think more systematically about how the work could be done.
AI is purely an aid that gives you better information to make a decision.
What does this mean for you?
Technical literacy does not have to mean becoming a quantitative researcher or developer.
Understanding how data, coding and AI can support your existing expertise can make you more versatile. That could mean learning enough Python to read code and think about automation, using AI to work through large amounts of information, or identifying which repetitive parts of your workflow can be accelerated.
The differentiator is not using technology for its own sake. It is understanding where it can improve the process and where your judgment still matters.
Building a quanta-mental team may require more than putting a fundamental investor and a quantitative specialist next to each other. Ying argues that both sides need enough understanding of the other to communicate effectively.
Investment context can be lost when a technical specialist does not fully understand the hypothesis they are being asked to test. Equally, an investment professional who cannot read or understand the technical process may struggle to identify what has been missed.
The stronger approach may therefore be to build teams with overlapping skills, where investment and technical professionals understand enough of each other’s work to communicate, test ideas and improve the process together.
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FAQ
Fundamental and quantitative investors use different methods, but both are ultimately trying to find alpha. Ying believes the future of investing may combine the two, using quantitative data and historical patterns alongside forward-looking fundamental judgment.
Ying sees AI as a tool that can accelerate research, process information and help investors reach decisions faster. It should provide better information rather than make every decision itself, with human judgment remaining important when markets or companies behave differently from historical patterns.
Technical skills can help investment professionals test ideas, automate repetitive work and make better use of large datasets. Ying asked fundamental analysts on her own team to learn Python so they could read and write code and think more systematically about which parts of their research process could be automated.
AI can help early-career professionals spend less time on repetitive work and reach more valuable parts of the job sooner. Ying encourages professionals to look at their day-to-day processes and ask what can be accelerated, using AI as a tool to learn faster and move towards work that requires greater analysis and judgment.
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