In pursuit of dominance with alternative data

H

ere in the UK, the London Stock Exchange and Nasdaq have seen stock prices and indices continue to climb. Not only have they undergone growth, but they also suffered little inertia during this period.

It is evident that the markets do not seem to care about what’s happening to the economy, while analysts all over the world are speculating on many reasons for why this is taking place.

It might be safe to say, that non-market factors like quantitative easing and increased speculation are among many of the factors for irrational growth in the markets.

An illogical market removed from the actual economy is hard to comprehend and difficult to thrash. Investors look for insights that go beyond the tried and trusted model.

By using alternative data, investors have the much-needed insights that can give them that much needed edge to create higher returns when playing the market.

This tool is not new to the likes of hedge funds or investment banks, as they have been using analytics-based processes and alternative data for some time.

Alternative data is economic, financial, and political information that can be found outside of traditional sources such as social media networks, public websites, and applications.

Specific sources include raw scraped data, data sets and third-party services providing unique data types that can include:

• CEO compensation
• Corporate flights
• Entertainment event listings
• Geolocation & satellite images
• Government contracts
• Hotel/commercial flight bookings
• IoT data
• Mortgage data
• Politician trading activity
• Product recalls
• Reddit forums (like WallStreetBets)
• Retail inventory data
• Twitter activity
• Work visas

Alternative data is challenging to collect and expensive to purchase. However, for those with the tools and resources, it gives investors THE advantage. In simple terms, alternative data provides unique acumen which enables professional investors to outwit passive investors, day traders, and algorithms.

Today, market insights do not carry the same power. The Efficient Market Hypothesis asserts that all public information is almost immediately incorporated into financial asset prices, making attempts to outperform the market futile, in the long term. Whether traders agree on the theory or not, what seems evident is that data diminishes in value with age.

The fresher the data, the more relevant the insights. Alternative data is not only current, but also mostly hidden – and that is a powerful combination.

Investing today is less based on intuition and more on mechanics. It is no longer about 'investing' in a promising business. Advanced financial analysis instead uses analytics and scenario modeling to find hidden correlations that predict market movements.

Two examples of alternative data could include hirings of high-level executives, signaling a change in the approach to business, and changes in customer sentiment, signaling a change in the perception of a brand.

Such company related data reveals behavioural patterns that can serve as predictive indicators of public company performance.

Insights can be derived from the activities of public companies servicing large global corporations. One example is merchant payment history – derived from credit inquiries – that can reveal insights into the health of a company’s business.

This can work two ways. A positive scenario can be discovering inquiries by potential trading partners looking to establish a relationship with a growing business. Conversely, increased credit inquiries may be a sign that the company has issues paying its bills. Either case can provide share price direction when combined with other data sets.

Classified company data is imperative for financiers wanting to gain historical acumen into the market. Classified companies that have over 500 employees have been constantly growing. Accordingly, of about 32.5 million US businesses in 2020, less than 0.01% were publicly owned. As many companies are privately held, their data is particularly valuable in predicting the overall market’s direction.

This can be achieved through the implementation of benchmarking or indexes that use the underlying private company data sets. It can be argued that smaller privately-owned businesses feel economic changes earlier and more strongly than their large public counterparts.

All these factors indicate strengthening or weakening economic conditions. When combined with advanced analytics, these data sets can predict market or sector moves for public companies.

Alternative data can be acquired from companies that extract data from websites through a process called web scraping. Web scraping uses 'bots' that crawl and extract data from public websites and publicly available social media forums.

Some companies choose to extract the data with in-house scraping solutions or through outsourced ready-to-use tools. Since alternative data is specialised, the sources must be equally unique. That is why leading investment firms typically scrape data according to their own customised strategy.

Web scraping is the primary way to access insight-generating real-time publicly available data from the web. Due to the widespread use of the internet, tremendous amounts of valuable data are available on the web. Information related to business intelligence, customer behaviours and actions, news sentiment and much more is within arm’s reach.

Unlike other public data types, alternative data needs creative analytical skills to obtain insights into what market signals are at play.

Data like housing permits, hiring activity, or corporate flights to a specific location may give insights into a corporation’s activity, but that data may not be as straightforward as other information such as increased sales, successful drug trials, or technological innovation.

It can even be argued that alternative data can produce indicators that predict more obvious traditional market signals, like early supply chain issues that may affect prices that then affect sales. To produce those insights typically requires extensive industry experience and inside knowledge into the factors that can move asset prices.

Beating the market has never been more difficult for the professional investor, especially with the proliferation of algorithmic trading and passive investing.

Alternative data has emerged as a necessary part of the investment analysis process by providing insights not commonly found or understood by the average investor or algorithmic trader.

Whether you choose to buy your own data sets or scrape it yourself depends on factors like time, resources, and expertise. Whatever your choice, the insights you obtain are arguably more important than the money spent

In such a competitive environment where news travels at lightning speed and the value of data rapidly degrades with time, the speed at which these insights are gained can make or break a strategy.