“Alternative Data” in Investment Research – Is it really “Alternative”?

Published on August 6, 2018 by Mohit Mittal

Active funds have been struggling to generate sustainable Alpha and justify their higher fees in the recent years. With Passive funds marching ahead of Active investments, both in terms of fund inflows and returns, Active fund managers are turning to alternative data to find new sources of Alpha.

While the use of alternative data in research has been prevalent for some time now, it gained strong traction only in the last few years as digitization of the economy opened new data sources that provide realistic predictive value to company performance insights. Based on a recent survey of buy side chief investment officers, portfolio managers, and investment analysts conducted by Greenwich Associates, 70% respondents agreed that they were either already using alternate data or planning to use it in the next 12 months.

Quant analysts have been using alternative data, and, now, sell-side fundamental analysts have also started seeing analytical value in the same. Sell side may have started using alternative data primarily to validate research analyst’s assumptions, but, slowly, its relevance is increasing. Currently, alternative data is making room directly to analysts’ valuation models, providing industry and competitive insights, acting as a lead indicator, and being utilized as a separate data product to track and act upon. The post-MiFID II era is likely to further drive more sell-side analysts into using alternative data in research, in order to provide the required value to their clients. This increasing adoption of alternative data by the sell-side fraternity comes with several challenges, which we will discuss in this blog.

Firstly, let’s look at what is alternative data. There are several definitions available. But, to keep it extensive, we can consider all non-traditional data as alternative data. Traditional data primarily includes company filings, press releases, management commentary, and investor presentations, which are generally provided by the company itself. Alternative data includes not only credit card transactions, job listings, web traffic, flight and shipping statistics, but also weather data, social media posts, product reviews and even satellite imagery and geospatial information. These are extracted primarily through web harvesting tools that include several software, algorithms, and programs.

Sell-side firms face several challenges as they look to introduce/enhance alternative data usage in their existing research process:

 

Data-related Risks

 

Alternative data is taken from different sources, which may not be available otherwise. Data risks include:

• Accuracy and validity of data

• Unauthorized access to Personally Identifiable Information (PII) and Material Non-Public Information (MNPI)

• Data provider not following the required controls and procedures

 

Availability and Cost of Data

 

This type of data is not readily available and may require high investment, at least in the initial stage. It is also a challenge to select the right vendor to get relevant data access.

 

Data Analytics/Science Talent

 

Most firms are hiring Data Science heads to lead their strategy and implementation of alternative data. However, there is an overall scarcity of such talent in developed economies. They come with a high price tag, usually higher than research associates

 

Research Integration

 

It is difficult to separate the Data and Research functions. Research experts are required to interpret alternative data and integrate actionable insights in their research process.

For this, research teams require more bandwidth and close coordination with data experts, not just in interpreting the extracted data, but also in developing new data fields that can facilitate a more in-depth analysis of a sector/company.

 

Acuity’s View

 

With increased use of alternative data, its availability and cost are likely to get rationalized. In the meantime, it is also important to have a control on the data to avoid any regulatory risks. It is suggested to partner with data providers for specialized data, such as satellite imagery, social media sentiments. However, for other data requirements (e.g. web scraped data), firms should aim to build internal teams to be cost effective and dynamic, to be able to adjust to evolving sectoral trends.

Impacted by the scarcity of talent and related higher cost, Investment Banks are looking to offshore their work to low-cost locations with abundant talent, such as India. Given the substantial time and effort that go into bringing up actionable insights for sell-side analysts, the next wave of efficiency will arrive through collaborating with partners who provide both data and research expertise.

At Acuity Knowledge Partners, we set up a team of fundamental research experts and data experts, who work as an extension of client’s research teams, to extract and interpret data and design differentiated data research insights.

Alternative data analysis is likely to follow the traditional data path, wherein all firms use the same/similar data in the longer term. Success will depend on how quickly you are able to embrace changes, how effective your analysis and insights are, and how operationally effective you are.

Sources: Seismic Shifts The Future of Investment Research – Greenwich Associates, Thomson Reuters

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