Alternative Data in Finance

What is structured data? 

Structured (or conventional) data refers to any data that resides in a fixed field within a record or file. For example, macroeconomic indicators or financial statement items would be examples of structured data.

What is unstructured data? 

Unstructured data refers to any data that does not reside in a traditional row-column database or file. For example, unstructured data in finance is the data generated via social media in the form of reviews, news reactions, videos and charts. 

How is alternative data used in the finance industry?

It is estimated that more than two thirds of hedge fund managers are currently using, or are at least actively testing, alternative data sources. The insurance industry has also been proactive at using alternative data such as medical records, professional licence registries and social media activity analysis, among other unconventional sources. This has allowed the industry to more accurately classify all customers based on risk, to improve the client experience and to enhance risk management, liquidity and solvency policies. In the investment management space, we have seen that asset managers have been using alternative datasets more intensively over the past five years in order to screen investment ideas, blending both quantitative and fundamental methods that has resulted in the creation of the so-called “quantamental” investment style. The percentage of investment funds that use or expect to use alternative data sat at approximately 78% in 2020 compared to 52% in 2016, with a budget of over 1.7 billion US dollars per annum and an average investment fund spending of $158 thousand per annum.

What are some examples of the use of alternative data? 

- The use of credit card transactions to measure a supermarket chain performance

- The utilisation of geo-location data to measure the organic growth of a fast-food chain brand

- The gathering of email receipts to understand the activity of food-delivery companies

- The number of app downloads to rate the penetration of a new video game release

- Use of social media data and search engine data to predict quarterly earnings surprises

- Analysis of satellite data such as tanker tracking and crop conditions to identify the impact on commodity prices

- Use of weekly box office data to enhance momentum signals

What are the challenges of alternative data?

Budget constraints. Dataset integration comes with hidden costs with regard to the degree of internal effort.

Technological risks. Events such as vendor blackout risk, IT reputational risk and alternative data delivery format risks should all be factored into decisions.

Legal and/or compliance breaches. Organisations should maintain the highest standards ensuring that all legal and compliance requirements are satisfied.