In this article you will learn:
- What is alternative data?
- Case study #1: How Bloomberg leveraged geospatial alt data to help investors in 2020
- Case study #2: How alternative social media data influenced financial markets in 2020
What is alternative data?
Alternative data, External Data, or ‘Alt data’ goes beyond the ‘typical’ type of data that companies collect. ‘Typical data’ may consist of company filings or earnings, for example. Usually, alt data is used by the financial sector though it can be extremely valuable for a variety of industries, and use-cases from eCommerce to SEO, and travel to app performance.
Here are some examples of ‘alt data’:
- Credit card transactions
- Geolocation data from cell phones
- Satellite images
- Weather forecasts
- Consumer sentiment data
This type of data is extremely valuable in finding predictive signals regarding recessions or future commercial intentions which can be translated into concrete investment, and business decisions.
Case study #1: How Bloomberg leveraged geospatial alt data to help investors in 2020
Question: What bearing does the weather, hurricane, and global pandemic data have on business and stock market trading decisions?
According to Bloomberg, a great deal! The financial media company has developed a map called ‘Map Go’ which is fed alternative geospatial data which includes, for the most part, weather and climate-related data points. This tool is used by investors as well as companies looking to mitigate and quantify their risk portfolios.
Specifically, over the course of 2020 this geospatial ‘alt data’ machine proved to be extremely useful when Bloomberg decided to incorporate coronavirus case data into its geospatial feeds. This empowered clients, investors, and institutions to follow the virus, infection rates, heavily affected areas, and the like in order to make better trading decisions based on where manufacturing or consumption may have been most likely to be affected due to virus exposures.
Supply chain irregularities were more easily identified by looking at a specific geolocation or corporation’s exposure to risk based on time-stamped Covid-19 caseload density analysis.
The bottom line: Geospatial alt data is becoming an integral part of quantitative and qualitative analyses at hedge funds and other financial institutions looking for a market advantage.
Case study #2: How alternative social media data influenced financial markets in 2020
Companies that collected alt data over the course of 2020 benefited by being able to make smarter decisions. Here is a case study of the crossroads between politics, social media data, and financial market trends.
Question: How did Trumps’ tweets influence financial markets?
Image source: Bright Data
According to a study carried out by Barron’s, Trump’s social media activity had a direct impact on financial markets and they can prove it. Trump is known as a prolific ‘Tweeter’, thumbing out an astounding 14,000 tweets from 2016 when he was a presidential candidate all the way up to the summer of 2019. On average Trump tweets a whopping 10 times per day!
The correlation between Trumps’ tweets and the S & P 500 is pretty astounding. According to Barron’s analysis, the less Trump tweeted the better the market performed. They found that on the days the president addressed the American people fewer than 5 times on Twitter, the S & P showed an above-average return of 0.13% compared with an average return of 0.02% for that same period. That means markets and investors ‘felt insecure’ or had ‘negative outlooks on the economy’ on days their leader chose to tweet. Hedge funds, institutional investors, and investment houses that collected this type of data were able to analyze and capitalize on this information in real-time.
Here are some more data points to back up this correlation:
- Following ‘quiet Trump days’ the S & P rose 0.46% over the following ten trading days.
- When Trump was feeling especially ‘communicative’ (20+ tweets in one day) the S & P showed a loss of 0.03% on average.
The power of words
Besides for the ‘quantity’ of tweets, one also must examine the content itself which may have had a bearing on markets. In this instance the Barrons study looked at two main issues:
1. The Chinese-American trade war – In this respect, Mr. Trump tweeted the word ‘tariff’ 165 times resulting in a decline of 0.12 % on average of the S & P 500.
2. Federal reserve interest rates – In this instance, Trump tweeted 100 times about ‘Powell’ (the Fed’s chairman Jerome Powell), the ‘federal reserve’, and ‘the fed’ resulting in a drop of 0.05% of the S & P on those same days.
The bottom line: Data sets which at first glance may seem odd to invest in collecting and analyzing as they deviate from ‘classical approaches’ are actually where a lot of opportunities are to be found.
As the old adage goes:
If you want different results than what you’re getting, you have to try different approaches.
