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Alt Data on the March with Machine Learning

Traders Magazine Online News, January 29, 2018

Ivy Schmerken

The explosion of alternative data sources, such as satellite images, sentiment analysis, and geolocation data, is having a profound impact on the field of quantitative investing.

Analyzing torrents of unstructured data requires sophisticated tools and technology, and this leads to opportunities as well as challenges.

Alongside the boom in alternative data, there is increasing demand for data scientists and machine-learning professionals who can work with petabytes of unstructured data sets.  In January, Point72’s Aperio unit advertised for a “machine learning-data scientist” on, a public website that covers the industry.

And on Dec. 12, The Financial Times reported that AQR, a $208 billion hedge fund manager run by Clifford Asness, planned to explore big data. Despite previous doubts, AQR reportedly plans to experiment with machine learning “to parse through novel data sets such as satellite pictures of shadows cast by oil wells and tankers,” Asness told the FT.

Why is the growth in alternative data fueling the demand for data scientists with machine learning skills?

“It’s hard to make money on simple trades or macro-trades on basic relationships. So, everybody on the asset management side is chasing how to get that next edge,” said Mansi Singhal, cofounder at qplum, who spoke at TABB Forum’s Fintech Festival in November.

Based in Jersey City, NJ, qplum is a registered investment advisor that operates an online wealth management platform offering A.I. and machine learning-based portfolios of ETFs in stocks, bonds and real estate. The firm uses big data, algorithmic executions and risk parameters to run an automated end-to-end process. “Instead of asking people to write signals or strategies, the edge is to use a machine learning framework on it to extract features from data,” said Singhal in a follow-up interview.

Amidst the buzz surrounding alternative data, there are still concerns about the amount of resources, time and energy it takes to collect, process and reformat the data for use in algorithms and machine learning tools.

“Clearly, it’s not that simple. There are a lot of challenges in terms of collecting that data, coding that idea, and running the correct models,” said Patrick Pinschmidt, partner at Middlegame Ventures in Washington, D.C.

To extract value from alternative data, it requires an investment in infrastructure and talent, said qplum’s Singhal on the panel.

Data Scientists & Infrastructure

While traditional hedge funds will spend a lot of money on relationships and consultants, perhaps to obtain shipping or trucking data, qplum chose a different route.  Firms can choose whether they want to spend money to get that data from a relationship or invest resources in developing their own data pipeline, continued Singhal.  Investing in relationships or consultants is an ongoing expense, whereas once a data pipeline is built it can be utilized over and over again, she emphasized.

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