The Process of Generating Alternative Data.
Alternative data refers to non-traditional data sets that are culled from various sources and used by investors to guide their investment strategies and gain a market edge. So instead of only focusing on such traditional financial data as pricing data, company filings, broker forecasts, etc. investors seek out additional data to help improve their performance.
Examples of alternative data sets include:
- Credit card transaction data
- Mobile device data
- IoT sensor data
- Satellite imagery
- Social media sentiment
- Product reviews
- Weather data
- Web traffic
- App usage
- ESG (environmental, social, and corporate governance) data
- Jet tracking
- Government contracts
- Congressional trading
Most of these data sets are generated in one of three ways:
- Individuals: Social/Sentiment, Web Traffic, App Usage, Survey
- Business Processes: Credit/Debit Card, Web Data, Public Data, Email/Consumer Receipts
- Sensors: Geo-location, Satellite, Weather
Note that data from business processes are typically more structured than data from individuals or sensors.
How Alternative Data Complements Traditional Data.
Investors use such data to generate alpha and manage risk. It is important to note that alternative data is usually one of several inputs to an investment process — rarely is it used as a stand-alone data set. For example, investors with a fundamental approach may use alternative data to help interrogate their existing investment hypotheses.
In addition to alpha generation, investors also use alt data as an input to their risk management processes. Often the data can help limit losses by alerting investors to a brewing problem. Again, risk managers do not solely rely upon alt data in their analysis. They use it as an additional layer on top of more traditional financial data to manage risk.