Posted on : March 24th 2022
Author : Sudhakaran Jampala
Investment management firms are desperate to boost their returns or alphas. In the search for alpha, data analytics is the weapon of choice for capturing competitive advantage. Investment managers can extract useful insights from various internal and external data sources, including what is dubbed as alternative data sets. Often associated with financial analysis, alternative data is sourced externally regarding a particular company, and typically refers to information that’s used to gain additional business insights. For example, consumer sentiment and credit card data mixed with traditional data can provide better predictions on the business performance of certain companies.¹
Typical data types in alternative data are customer credit and debit card transactions, emails, geo-location (or foot traffic) metrics, mobile application usage, satellite or weather data, social and sentiment data, web scraped data, generic web traffic, etc. However, alternative data analytics is only in its early adoption stage now. Some specialized data vendors sell alternative data to various enterprise stakeholders, though many companies currently only explore alternative data for potential value. The more prevalent use now is using alternative data as supplementary information to test market hypotheses derived from more conventional sources.
Going beyond the confines of conventional data sources (e.g., financial statements), alternative data provides deeper and more timely insights for supporting investment decisions. The growing use cases for alternative data is a trend that’s expected to see the global alternative data market size grow at a 44% compound annual growth rate (CAGR) to reach US$11.1 billion by 2026 from US$1.25 billion in 2020 (Research and Markets).²
Traditional financial and company data like company earnings reports are released at particular times. It lacks a real-time connect. But alternative data offers a quasi-real-time view of market sentiments through various routes (like social media chatter). Alternative data sets encompass things like measuring traffic in stores, assessing how successful cellphone applications are, evaluating web traffic, etc.³
According to Gartner
Alternative data can also be acquired from individual or aggregate data by advanced algorithms or machine learning on traditional data sources, so that results can be used as inputs for further analyses. These sources can come from news articles, government publications, company reports, or licensing or purchasing data from third-party specialist aggregators. ⁴
Any data apart from static or conventional market data is typically classed as alternative data. By 2025, over 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics, according to Gartner, Inc. In this context, alternative data’s real-time updates is bolstered by the objectivity and accuracy of machine learning (ML) algorithms.
According to the Alternative Investment Management Association (AIMA), alternative data is not a new practice. Today, investors are getting access to much more alternative data as more and more information, company events, trends, etc., are digitized. Numerous alternative data sources, including satellite imagery, analysis of weather patterns, social media analytics, etc., are at the call of analysts. Since a lot of alternative data is unstructured data, getting it into a manageable form is key. Regarding that, AI- and machine learning-based technologies can optimize data automation and transform the processes surrounding data analytics and governance.
Technologies like Internet of Things (IoT) like smart TVs, vehicle sensors, etc., constantly provide data on user behavior. AI/ML-led analytics enable firms to analyze such unstructured data, producing a competitive edge over peers, through unconventional and nuanced data sources. The latest in AI/ML platform models allow firms to elevate subtle signals and latent trends that support insights to streamline business processes.
³ https://advanresearch.com/downloads/Advan Location White Paper.pdf
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
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