Alternative data (in finance) refers to data used to obtain insight into the investment process. These data sets are often used by hedge fund managers and other institutional investment professionals within an investment company. Alternative data sets are information about a particular company that is published by sources outside of the company, which can provide unique and timely insights into investment opportunities.
Alternative data sets are often categorized as big data, which means that they may be very large and complex and often cannot be handled by software traditionally used for storing or handling data, such as Microsoft Excel. An alternative data set can be compiled from various sources such as financial transactions, sensors, mobile devices, satellites, public records, and the internet. Alternative data can be compared with data that is traditionally used by investment companies such as investor presentations, SEC filings, and press releases. These examples of "traditional data" are produced directly by the company itself.
Since alternative data sets originate as a product of a company's operations, these data sets are often less readily accessible and less structured than traditional sources of data. Alternative data is also known as "data exhaust". The company that produces alternative data generally overlooks the value of the data to institutional investors. During the last decade, many data brokers, aggregators, and other intermediaries began specializing in providing alternative data to investors and analysts.
Types
Examples of alternative data include: Geolocation (foot traffic) Credit card transactions Email receipts Point-of-sale transactions Web site usage Mobile App or App Store analytics Crowdsourcing Obscure city hall records Satellite images Social media posts Online browsing activity Shipping container receipts Product reviews Price trackers Shipping trackers Internet activity and quality data
Uses
Alternative data is being used by fundamental and quantitative institutional investors to create innovative sources of alpha. The field is still in the early phases of development, yet depending on the resources and risk tolerance of a fund, multiple approaches abound to participate in this new paradigm.
The process to extract benefits from alternative data can be extremely challenging. The analytics, systems, and technologies for processing such data are relatively new and most institutional investors do not have capabilities to integrate alternative data into their investment decision process. However, with the right tools and strategy, a fund can mitigate costs while creating an enduring competitive advantage.
Most alternative data research projects are lengthy and resource intensive; therefore, due-diligence is required before working with a data set. The due-diligence should include an approval from the compliance team, validation of processes that create and deliver this data set, and identification of investment insights that can be additive to the investment process.
However, the usage of the alternative data is not restricted by investment sphere, it is successfully used in economics and politics as well as retail and e-commerce spheres. It is possible to predict geopolitical risk through a profound alternative data analysis, while social media sites reveal a host of data for consumer sentiment analysis.
Methodology
Alternative data can be accessed via:
Web scraping (or web Harvesting, performed by computer programmers that design an algorithm that searches websites for specific data on a desired topic) Acquisition of Raw data Third-party Licensing
Analysis
In finance, Alternative data is often analyzed in the following ways:
Scarcity: the data Information overload within financial markets
Granularity: the level of detail and aggregation of data (including time) History: the trajectory of data Structure: the form of the data (csv, json etc.) Coverage: the stocks or geographical locations that data can be linked with