What Is Alternative Data in Finance?
Alternative data is data generated that provides insights into the real-time behaviour of consumers, businesses, and economies but is not typically found in conventional data sources such as government statistics or financial reports.
It consists of any information that can be used to measure economic activity in a non-traditional way; common examples include web scraping data, satellite imagery, and social media data.
The use of alternative data has grown in popularity in recent years as data sources have become more accessible, and analysts have become more skilled at extracting insights from it.
Alternative Data in Finance
Alternative data is not typically used in traditional financial research. However, traditional financial datasets, such as government statistics and company reports which are published quarterly, are lagging datasets. As a result, they do not offer real-time insights, which are important for investment decision-making.
By combining traditional financial and economic data, financial analysts and traders can get a near or real-time picture of specific companies and commodities and demands for goods and services.
In this way, they can gain an edge over the rest of the market regarding where prices of stocks or commodities are headed.
Examples of Alternative Data in Finance
While alternative data can be extremely useful, it is important to remember that it is still data and should be treated as such. Furthermore, alternative data should be subjected to the same rigorous testing and analysis as any other data source to ensure that it is accurate and reliable.
Web Traffic And App Usage
Web traffic and app usage data are examples of alternative data that can be used to gauge consumer demand and sentiment.
Web traffic data can be used to track how much traffic a company's website is getting and where that traffic is coming from. This data can be used to identify customer behaviour trends and assess marketing campaigns' effectiveness.
App usage data can be used to track how often people are using a company's app and what features they are using. This data can be used to understand how customers interact with the app and identify improvement areas.
Example: Tracking Product Sales on Amazon
By tracking product sales data on Amazon, investors could try to predict if a specialised company selling popular products is likely to have a good or bad quarter before the quarterly report is made public.
Suppose there is a big discrepancy between real-time sales and expected quarterly turnover. In that case, an investor could bury or sell the stock short to benefit from a surprise compared to analyst estimates.
Satellite Imagery
Near-real-time satellite imagery can provide detailed information about various topics, including crop yields, retail foot traffic, and even traffic congestion.
One of the key benefits of satellite imagery is its timeliness, as it can be used to get a real-time view of what is happening on the ground. This is particularly useful for tracking changes in the environment, such as deforestation, or for monitoring the progress of construction projects.
Another benefit of satellite imagery is its global coverage. Satellites can provide detailed information about anywhere in the world, regardless of whether ground-based data is available. This makes it an ideal tool for tracking global trends and consignments, such as the spread of new crop diseases or global cargo movements.
The use of satellite imagery is not without its challenges, however. The high cost of collecting and processing this data can be a barrier for many organisations. In addition, satellite data can be difficult to interpret and requires specialist software and expertise.
Example: Tracking Crude Oil Cargo
Oil tankers transport a significant amount of crude oil worldwide before it is refined into various products, such as gasoline and diesel. Hedge funds often seek to profit from the movements of these tankers, and they do so by tracking them via satellite imagery.
Satellite images can give hedge funds insight into where the tankers are, where they're going, and how much oil they carry. This information can then be used to make predictions about the future movements of oil prices.
Social Sentiment and Product Reviews
Social Sentiment data and Product reviews reveal customers' attitudes about a specific brand or company. By tracking social media posts, traders can get an early read on which products are gaining or losing popularity. This information can then be used to make investment decisions.
Example: Social Media Hype
A Hedge Fund can track likes and tweets referring to companies and products using software or an external provider such as Stockgeist.ai.
If they spot a product that is suddenly getting a lot of positive social media attention, they may decide to buy shares of the company that makes that product.
If the product's popularity grows, the investment will likely pay off. On the other hand, if the product starts to receive negative social media attention, the hedge fund may decide to sell its shares.
Free Alternative Data Sets
The following websites offer some free datasets to experiment with alternative data.
Alternativedata.org
Alternativedata.org is a website dedicated to sharing alternative data. This type of data is not typically found in traditional data sets but can be found in sources like social media, web traffic, and user behaviour data. This website allows users to share their alternative data sets so that others can use them for research.
Kaggle
Kaggle.com is a website that provides data sets for use in data science competitions. These competitions challenge data scientists to create creative solutions to real-world problems. Kaggle provides a platform for users to share their data sets and compete against other data scientists.
Datahub
Datahub.io is a website that provides access to data sets from various sources. This website allows users to search for data sets by keyword, browse data sets by category, or explore data sets by location. The website also offers tools for data visualisation and data analysis.
