As the world changes beneath our feet, we need more than traditional sources of information to make sense of the investing world.
When markets are moving at a furious pace, as they have been since the start of the coronavirus earlier last year, it has seemed impossible to get a clear, up to date picture on what is happening.
During such times, official data is often slow and noisy - making it difficult to develop a full picture of what is really going on.
Against this landscape, alternative data takes on new importance in identifying emerging, economic and market trends that traditionally can be slow or impossible to efficiently contextualize.
What is alternative data ?
Alternative (or big) data is physical, unstructured (text) or non-financial data generated by the technologies of our everyday lives ― review sites, news and media, satellite imagery, sensors, to name just a few. When aggregated and analyzed in the right way, alternative data can provide valuable insights as part of a micro or macro analysis for companies, countries and pretty much anything you can think of.
ESG Analytics specializes in using alternative data for company ESG analysis, and these data sources have especially been valuable in the rise of increased scrutiny and shareholder activism, when analyzed with the right context. Here we look at a few sources of alternative data, to show how powerful alternative data can be for both ESG and broader financial analysis.
1. Restaurant Bookings
One of our favourite data sets is the restaurant booking data that comes from one of the worlds largest restaurant booking sites - OpenTable. During periods of volatility and large scale changes, it can be difficult to rely on traditional data such as consumer sentiment surveys, PMI surveys or official employment numbers. During the beginning of Covid, some of the volatility we experienced was unprecedented, with several exchanges halting trading repeatedly due to the large price changes.
Where there is volatility like this, it is sometimes a result of not understanding what the future holds - in this case, the economic impact of Covid and what that meant for businesses.
The restaurant booking data, from OpenTable was able to capture this in real time, showing how many bookings were being made on a daily basis, both at the country and city level.
This can show clear trends when compared to things such as stock price movements and other data.
2. Natural language processing
Natural language processing (NLP) of text has proved especially useful in gleaning insights from the massive amounts of text that are released on a daily basis. This can be applied to social media, news opinions and reports. In the case of news and public media, it can be near impossible to keep track of everything that is happening at all times, and understand the key insights.
One of the primary tools that we use is our NLP engine, which has been taught how to understand the relevant ESG related terms in text, and who it applies to (in this case public and private companies). This can then be further processed to run sentiment analysis to determine if that particular phrase or headline is good or bad.
The power of this is being able to get detailed analysis of key issues affecting a company, and understanding where risks may be. In the case of ESG Analytics, we categorize this to the Sustainability Accounting Standards Board (SASB) 26 issue categories.
Other uses of NLP include identifying events such as acquisition activity, understanding the frequency of phrases or running aggregated sentiment on social media (for example "what is the public sentiment by ticker on each stock on the Reddit subreddit r/wallstreetbets)
The end result can be powerful categorizations and trends for a company, for example:
3. Employee Reviews
As more web applications become available for everything from restaurant bookings, employee reviews, matchmaking and endless other verticals, there is a extremely rich source of alternative data that can give us insights that would just not have been available from traditional data.
Take Diversity and Inclusion for example. Typically you would rely on non standardized reports from a company, talking about their male/female ratio - or perhaps there would be some news about specific instances where they might have won an award, or on the other hand had allegations of misconduct.
One data source that can be used is employee reviews from sites such as Glassdoor. In this case a combination of the employees ratings and NLP can be used to paint a picture of how this might be seen at a company.
From big data to meaningful insights
We have seen from our adventures using alternative data ― that different insights tend to add value, sometimes in surprising ways. Some are more effective when making high level judgment about a topic, whereas others can offer you a faster data point than you might get with traditional data.
Ultimately, all alternative data sources have flaws and the goal is to get better, not perfect. This is why its important to have diverse and differentiated insights to inform our investment decision-making. Drawing from these datasets can reveal inconsistencies and anomalies just as well as potential trends. All of this together helps us to develop a better picture, manage risk and stay ahead of the curve. This is the type of information edge than can make a difference when the world and the opportunities and risks in it are changing fast.