Alternative Data: The Moat Around Hedge Funds

Introduction

What connects images of China’s megacities, smartphone location signals from Tesla’s factory in California, and the amount of time people are ready to wait to get their hands on a new iPhone?

Alternative Data

These are examples of different scenarios in which alternative data has been used in the investment research process. Satellite captures images of China’s megacities every five minutes, and a computer in California, thousands of miles away, analyzes the shadows of the buildings from the images to determine the state of China’s real estate sector, which is then used to drive investment decisions on the sector.

When Elon Musk promised that Tesla would work round the clock to increase the production levels of its Model 3 Sedan, Thasos group decided to verify his claims. They set a digital perimeter around Tesla’s Fremont, California factory to isolate the smartphone geolocation signals originating from that factory. Upon analyzing the data, they found that Musk’s claims were true and that the overnight shift had swelled by 30% between June and October; Thasos then sold this data to their hedge fund clients who made a fortune betting on Tesla. Similarly, smartphone geolocation data was used to calculate the amount of time that iPhone users waited outside Apple stores to buy new iPhones.

With the growing threat of passive funds and investors increasingly scrutinizing the fees charged for active management, active funds- especially the hedge funds have to find a way to justify their exorbitant fees and chase that elusive alpha.

Enter the hedge funds trump card- Alternative data. Until recently, alternative data was largely used by quantitative funds, which used numerical methods and computer models for investing, but in the past few years, alternative data has grown in prominence with industry behemoths such as JP Morgan, Goldman Sachs, Bloomberg, all wanting a piece of the pie.

What is Alternative Data?

Simply put, alternative data can be defined as data that is collected from non-traditional sources. There are three main sources through which alternative data is generated- individuals, business processes, and sensors.

• Alternative data generated by individuals: These are mostly unstructured data in the form of social media postings on sites such as Instagram, Facebook, and Twitter, product reviews posted on sites like Flipkart or movie reviews posted on IMDB, news relating to that specific company/ topic and web searches on search engines like Google or Yahoo.

• Alternative data generated by businesses: The alternative data produced as a by-product of business processes is known as the exhaust, and it is structured data that is comparatively more reliable. Examples include the aggregate credit card transaction data, which provides insights on product level profitability and price information, supply chain information, invoices and marketing material used.

• Alternative data generated by sensors: Sensors gather data from satellites, CCTVs, mobile phones, and IoT (Internet of Things) devices. The data that is gathered through these means is then used to analyze the movements of ships in some of the busiest ports in the world and plane movements to reveal the true state of economic activity. It can also be used to analyze the movement of people through the geolocation data from mobile phones, precipitation, and temperature data from IoTs.

In its report Casting the Net: How hedge funds are using alternative data, AIMA found that among the hundred hedge funds it surveyed, 53 % reported that they are already using alternative data, and the hedge funds reported aggregate spending of $250 million (USD) on alternative data. With the increased usage of alternative data, the number of alternative data providers has exploded from around 20 in 1990 to 445 in 2019, according to Alternativedata.org. The alternative data sets have risen in tandem with the data providers with Eagle Alpha estimating that the total number of alt data sets can rise to a whopping five thousand by 2024.

Clash of Clans

The hedge funds, in order to adapt to the changing landscapes, are modifying their recruitment practices; candidates who are familiar with technology and data analysis are given more preference. Case in point, Capital Fund Management, with its 160 employees, is composed of 75 employees who are focused on information technology, 20 of which are in data management, and 40 are scientists with PhDs in Physics.

Hedge funds which were traditionally the fiefdom of people with finance degrees (I like to call them the traditionalists), now have to deal with new-age data scientists and techies with an information technology background (the modernists?). There may be a tussle for power in the near future as the modernists will want more power in the investment process while the traditionalists will try to maintain the status quo. Hedge funds will also have to compete with Silicon Valley to retain the modernists who may prefer working with the likes of Google and Microsoft, where they can work on more exciting projects.

An interesting analogy can be applied to this scenario. A few months back, while I was switching channels on TV, I ended up watching a showdown between a material scientist and a seasoned chef baking a fluffy cake. The material scientist who had never baked a cake in his life was trying to bake a cake through unconventional and unproven scientific methods while the chef had mastered the art of baking the cake for decades. The scientist ends up making an edible cake which was fluffy but is far from being called a cake in a traditional sense, whereas the chef baked a fluffy cake with absolute perfection.

This can be applied to the clash between modernists and traditionalists, the modernists being the material scientist and the traditionalists being the chef; though the material scientist currently lacks the expertise in this field, he can quickly learn and match the skill set of the chef in the near future by using his unconventional methods. So two or three decades from now, the heads of the largest hedge funds can potentially be from the information technology sector.

Democratizing the Access to Alternative Data

Even though data is abundant, internet costs are low, and the computing power of our devices are at an all-time high, an average investor still cannot lay his hands on useful alternative data and derive insights from it as data preparation, and data analysis is still a costly exercise, even hedge funds struggle with it.  Alternative data has to jump through a lot of hoops in order to be useful; they have to first be sourced from a legitimate provider who has to first scrub the data off personally identifiable information and then hedge funds should clean and remove the noise, then proceed to apply machine learning algorithms to make sense of the data and see if they provide any actionable signals. So all this costs a lot of money which poses as a barrier to an average investor.

Quiver Quantitative (QQ) aims to solve this problem. Quiver Quantitative is an initiative of two college students who wanted to bridge the gap between Wall Street and retail investors by scrapping alternative stock data from across the internet and displaying it in a neat, easy to use web dashboard.

QQ has an interesting dashboard of data ranging from corporate flights, US Senator trading performance, corporate violations and fine, to Wikipedia page views, Political Beta and product recalls.

The above picture is a treemapping based data visualization with the size of the tiles based on market capitalization. The colour signifies the intensity of the fines, with red indicating larger fines and green indicating comparatively smaller fines. Hovering over the tiles gives the information of the stock ticker, company name, amount of fine charged and market capitalization.

This chart illustrates the relationship between weekly Wikipedia pages views and the stock price of Microsoft; these are interesting relationships that were hitherto inaccessible to an average investor. 

Conclusion

Alternative data is slowly changing the investment research landscape, and as an increasing number of firms adopt the usage of alternative data, more number of companies will start providing it, the cost of data preparation and cleansing will go down and eventually, alternative data will not remain alternative anymore, their use will become common practice, but it is important to note that alternative data cannot substitute thorough fundamental investment research, which will always reign supreme. 

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