The Sell Side of Alternative Data

Research is considered a very important part of the value-added by portfolio managers. Huge costs of structuring a research department require most firms to buy research from the sell side (investment banks and brokers). In-house research teams also like to supplement their research with alternative views from the sell side.

Research can be fundamental or quantitative and highlights macro themes such as the U.S. economy or specific sectors or individual companies. The clients of sell-side firms are typically asset managers, hedge funds and institutional investors.

Demand has prompted firms in the past few years to increase the big data and alternative data research offerings. In particular, new novel digital datasets and signals are most requested. Candace Browning, head of global research at Bank of America Merrill Lynch, noted in Institutional Magazine: “We continue to look for datasets that can provide external insights to our clients.”

The quality of research gives prestige to sell-side firms. A few of the leaders in sell-side alternative data research are Jefferies Global Research & Strategy, UBS Evidence Lab, Goldman Sachs Data Works, Morgan Stanley Alpha Wise, JP Morgan and Bank of America Merrill Lynch.

JPMorgan has similarly focused on big data and new technologies like artificial intelligence. The bank has expanded its use of tools like natural language generation, which is used to produce machine-written reports, and is working on automating tasks like scraping data from the web.

There is a trend to brand quantitative research differently than traditional research using words like: lab, data works and alpha wise. How millennial, with a Fintech startup feel.

One of the challenges in 2018 will be the unbundling of research costs under MiFID II, and whether costs of research will be passed through to clients or absorbed by banks. It remains largely a wait-and-see situation, according to industry watchers. 

Jefferies Global Research & Strategy provides thought leadership across the financial markets, offering valuable insights and actionable ideas to buy-side clients on market strategy, economics, equity, fixed income and foreign exchange.

In October 2017, Jefferies released the research piece “Quantifying Strategy: Building Data Strategies for The Next Decade.” The study surveyed more than 50 investment managers and service providers to give an inside view of the rapid changes in quantitative research and alternative data (see “Key takeaways from Jeffries survey,” below).
 

Top Predictions from Jefferies

With 90% of the available data in the world being created in the last two years, investment managers will increasingly have to dedicate resources to source, digest and disseminate this available information. We wanted to highlight what we believe will be some fundamental shifts on the horizon for investment managers to properly address the explosion of available information.

• Overlaying multiple datasets will be standard, creating infinite data combinations to assist investment processes. 

• The percentage of firms who have a “Dedicated Data Lead” (data specialist) will double. 

• Mutual funds adoption will scale significantly. 

• Investment managers will increasingly leverage third parties to source, analyze and catalog data. 

• At least half of firms incorporate “data and analytics” into their broker vote. 

• Artificial intelligence will become widely adopted to efficiently process continuous data explosion. 

• The amount of historical data desired before evaluating a dataset will shrink. 

• There will be considerable consolidation among data providers. 

• Well-resourced firms will build centralized internal “data control centers.”  
 

About the Author

Chris Randle previously was a proprietary global interest rate trader. He now focuses his time on Alternative Data & AI research thought leadership and Fintech investments.  @crandl