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GSAM Perspectives

Interview with Daniel Nadler


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Daniel Nadler, Founder and CEO of Kensho, provides his perspective on the power of big data analytics for Wall Street and beyond

What was the catalyst for starting Kensho Technologies and what does the company do?

I came up with the idea for Kensho while serving as a visiting scholar at the Boston Federal Reserve in 2013. I was stunned to learn that as important developments were occurring around the world—central bank announcements, elections in Europe, the European sovereign debt crisis, turmoil in the Middle East, etc.—there was no existing mechanism to track similar historical events and analyze the implications so as to glean insights. Neither regulators nor bankers had an efficient and effective method for assessing the impact of similar events on financial markets beyond digging up old news clips and manually creating spreadsheets. I began working on it with some friends, and within weeks we had put together a small team and received early funding for the idea from Google’s venture capital arm. Kensho Technologies Inc. was founded in May 2013.

Like Google, Kensho answers questions such as “How do defense or oil or airline stocks react to ballistic missile tests by North Korea?” But while a search engine can only find pages with existing analyses, Kensho can generate original answers by analyzing relationships between events, including natural disasters, political developments, corporate earnings announcements, product launches and FDA drug approvals. Answers that might have previously taken 40 man-hours of research can be generated by Kensho in seconds, complete with graphs and charts.

What is your firm’s philosophy and approach?

We believe Kensho is in the vanguard of the Fourth Industrial Revolution. The First Industrial Revolution used water and steam to power production. The Second used electric power for mass production. The Third used electronics and information technology to automate production. This Fourth revolution is building on the third to create a “digital revolution.” In this vein, our goal is to bring advanced technologies, like machine learning, to bear on aspects of the capital markets in ways that, until now, have been the provenance of a very select set of elite hedge funds. By making this technology more accessible, market participants are able to gain a more efficient and transparent understanding of capital markets. Through our media partnerships, we are also bringing greater market insight and transparency to everyday investors, revolutionizing their access to information that helps them achieve their goals.

How has your business model evolved?

Over the next two to three years, Kensho will continue to expand its footprint within the financial services industry, both horizontally, by adding new clients, and vertically, by expanding coverage and functionality for existing clients. We are also creating new business lines, such as the application of machine learning technology to financial transaction flows, giving our clients unique predictive insights into the behavior of market participants, and creating indexes to help investors make sense of the New Economies that are transforming our world—emerging sectors like the commercialization of space, autonomous vehicles, or wearable technologies. There are significant opportunities to apply our technology beyond financial services, with applications in government, retail, pharmaceuticals and healthcare, among others in which we are actively engaged.

As we adopt new technologies, how do you see financial intermediaries evolving? How do you see the roles of investment professionals changing as a result of big data innovations?

From the invention of the ticker tape to high-speed trading, technology has constantly changed the financial industry. Today, start-ups are taking aim at nearly every business line of traditional financial institutions. Decisions about loans are now being made by software that can take into account a variety of finely parsed data about a borrower, rather than just a credit score and a background check. Banks are trying to fend off the newcomers by making their own investments in big data innovations. And investment professionals will need to adapt as they always have, making use of new technologies where they help them to serve their clients more effectively.

How has Fintech evolved and what opportunities and challenges do you see?

Stock trading, one of the earliest areas to go electronic, provides an interesting precedent for how automation can play out in various financial institutions. On the company’s trading desks, stocks are now bought and sold by computers instead of people. Some traditional traders were replaced by programmers who design and monitor the new trading algorithms, and there are now new jobs in the data centers where the high-speed trading takes place.

What is your outlook for big data?

We’re at an interesting inflection point. Never before have we had so much data available to us to instrument the world and draw inferences about where to deploy capital. And that is coincident with the availability of computing power, analytics and machine learning to help us make sense of it. The smartest people asking the best questions will be able to use that to generate alpha, but the decay-time over which new signals become beta will shrink.

Outside of finance, what industries do you think will be most impacted by advancements in big data and machine learning technologies?

According to an Oxford paper1 and subsequent research, advancements in machine learning technologies vary significantly by industry. In healthcare, for example, where human interaction is vital, automation threatens fewer jobs than it does in the labor market as a whole. Taxi and truck drivers, on the other hand, may face a bleak future given recent advances in self-driving cars. Oxford researchers also took into account software that can analyze and sort legal documents, doing the work that even well-paid lawyers often spend hours managing. Journalists will have to compete with start-ups like Automated Insights, which is already writing up summaries of basketball games and financial reports.

8. You’ve spoken about the ability of machine learning technology to perform traditionally “human” tasks more efficiently and accurately. What are your thoughts on the broader impact of machine learning on society?

I anticipate some form of strong artificial intelligence, whereby computers in the far future would be smart enough to anticipate our needs and usher in an era of abundance. For the next few decades, though, I predict a more complicated time—an interregnum in which the computers are not as smart as people, but smart enough to do many of the tasks that make us money.

Daniel Nadler

Daniel Nadler

Founder, CEO, Kensho Technologies

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