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AB IQ Equities Blog

Combining Big Data and a Human Touch for Equity Insights

28 June 2019

4 min read

Nelson Yu| Head—Equities

Chris Hogbin| Global Head—Investments

Figuring out how to use big data is the next frontier for the asset-management industry. Equity investors must have the right culture—and ask the right questions—to successfully integrate data science into research and investment processes.

Why Is Big Data So Important?

There’s a colossal amount of data available to investors today. For example, more than 8,000 US-listed companies produce quarterly 10-Q and annual 10-K reports, each hundreds of pages long. We’ve collected 675,000 of these reports that were filed over the past 26 years. Globally, companies also conduct about 20,000 earnings calls a year in English, each yielding detailed transcripts. And if you include non-English corporate documents from around the world, the data mountain mushrooms.

In theory, portfolio managers must pore over thousands of pages of data to fully gauge the risks and opportunities that a company faces. Practically speaking? It isn’t humanly feasible.

Data science offers a solution by applying machine learning and artificial intelligence (AI) techniques to process information. Yet even the smartest software requires human direction and expertise to translate data into investing conclusions.

Bringing Together Research Skills

Generating insights requires a broad set of investment skills. Large data sets must be crunched and combined with complex statistical and economic models. Investment organizations rooted in quantitative research may seem more attuned to data science but might not be equipped to make sense of the information.

Fundamental analysts can apply research intuition by asking the right questions needed to extract useful information from huge pools of data, but they may lack the technical skills to process it efficiently.

We’ve been introducing advanced big data techniques to tackle equity investing conundrums that couldn’t be solved by human researchers alone. In the following case studies, we aim to show how a hybrid approach drawing upon diverse analytical skill sets can help investment teams rise to the data challenge.

Case Study 1: Big Data to Study Airline Capacity Utilization

Question:How does additional capacity impact the pricing power of an airline?

Airfare pricing is extremely complex and opaque, which makes it very difficult for a transportation analyst to draw conclusions about an airline’s capacity, its pricing and ultimately its profitability. In 2018, we set out to mine big data in order to learn more about how airline capacity affects pricing power (Display).

Solving the Airplane Capacity Conundrum

Source: AllianceBernstein (AB)

We set our data scientists to overlay more 1 million rows of data from the Department of Transportation with a six-month lag with airline-reported data by route. We then matched quarterly fare and capacity data, consolidated the airlines and removed monopoly markets from the data set to focus on the effects of competition.

The study revealed two clear conclusions. First, airlines typically add capacity to routes with higher-than-average growth in passenger revenue per available seat mile (or PRASM, the industry measure of the profitability of a given route). Second, within four quarters, the increase in capacity tends to slow. This triggers a recovery in PRASM.

These conclusions were not academic. By gaining a better understanding of pricing dynamics, we developed a clearer view of the earnings potential of one of our holdings, which underpinned our conviction and allowed us to increase our position. What’s more, the research has enabled us to monitor real-time airline pricing more accurately, and we use these data to engage management in discussions about their plans for future route expansions.

Case Study 2: Identifying Risks in Corporate Filings

Question:Can changes in the MD&A section of 10-K statements help identify risk?

Corporate filings provide investors with a treasure trove of information. But with thousands of pages to decipher, how can you find what’s really important? We set out to identify potential risks in corporate filings by using NLP and AI to scour annual 10-K reports.

The logic is simple. In the management discussion and analysis (MD&A) section of a 10-K, most of the text doesn’t change much from year to year. By systematically and comprehensively finding sections that have changed, we may be able to discover potential risks earlier than others.

First, we scraped the website of the US Securities and Exchange Commission for corporate transcripts of S&P 500 companies since 1996. Then we parsed the MD&A sections and applied NLP to compare filings from year to year. Our system created a score to show how similar or different a report was from one year to the next. Fundamental analysts then reviewed the 10-Ks of companies flagged to validate the risks detected.

Natural Language Processing and Artificial Intelligence Identify Text Changes

Source: Company reports and AllianceBernstein (AB)

The conclusion? Shares of companies that scored low tended to underperform within the first three months after the report was released. Based on this analysis, a broader data-driven tool could be developed to help equity analysts gain an advantage in anticipating corporate changes that are likely to affect share prices.

Don’t Dismiss the Human Touch

These cases show the importance of closely integrating the data analysis function with investment teams and industry analysts. Given the costs involved, it’s important to choose the right projects, where data analysis can make the biggest impact.

Investment firms that address these challenges will do better at using data science to improve portfolio returns, in our view. There are no easy answers. But one guiding principle is clear: even with the most sophisticated AI, applying a human touch to the equity research process is the best way to turn big data into big investment insights.

The views expressed herein do not constitute research, investment advice or trade recommendations and do not necessarily represent the views of all AB portfolio-management teams. Views are subject to revision over time.

About the Authors

Nelson Yu

Nelson Yu is a Senior Vice President, Head of Equities and a member of the firm’s Operating Committee. As Head of Equities, he is responsible for the management and strategic growth of AB’s equities business and investment decisions across the department. Since 1993, Yu has experience generating investment success in global equity markets by joining fundamental research with rigorous quantitative methods. He joined AB in 1997 as a programmer and analyst, and served as head of Quantitative Equity Research from 2014–2021. Since 2017, Yu also served as head of Multi-Style Core Equity strategies, with over $10 billion in assets. Most recently, he was CIO of Equities Investment Sciences and Insights, which brings together resources across Data Science, Quantitative Research, Advisory Services, Risk and Global Execution to deliver differentiated capabilities and insights to AB’s equities investment platform. Prior to joining AB, Yu was a supervising consultant at Grant Thornton. He holds a BSE in systems engineering from the University of Pennsylvania and a BS in Economics from the Wharton School at the University of Pennsylvania. Yu is a CFA charterholder. Location: New York

Chris Hogbin

Chris Hogbin is the Global Head of Investments for AB. In this broad leadership role, he oversees all the firm’s investment activities. Hogbin is responsible for driving investment success across asset classes, fostering collaboration and sharing best practices across investment teams, as well as leveraging a common infrastructure and evaluating opportunities to invest in capabilities that deliver better outcomes for clients. He is also a member of the firm’s Leadership team and Operating Committee. Hogbin joined AB’s institutional research business in 2005 as a senior analyst covering the European food retail sector. In 2010, he was named to Institutional Investor’s All-Europe Research Team and was ranked as the #1 analyst in his sector in both 2011 and 2012. Hogbin became European director of research for the Sell Side in 2012 and was given additional responsibility for Asian research in 2016. In 2018, he was appointed COO of Equities for AB. In 2019 Hogbin was promoted to co-head of Equities, becoming head of Equities in 2020. Prior to joining the firm, he worked as a strategy consultant for the Boston Consulting Group in London, San Francisco and Shanghai, where he was responsible for the execution of critical business-improvement initiatives for clients in the financial-services and consumer sectors. He holds an MA in economics from the University of Cambridge and an MBA with distinction from Harvard Business School. He is a trustee of the Public Theater in New York.

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