Predict financial movements with web data

by: SESAMm , 3 minute read , February 12 2020

Discover our whitepaper highlighting the power of web data on predictive analytics for the financial industry: how alternative data strengthen the market, the challenges of collecting web data and case studies presenting different approaches, such as ESG, showcasing TextReveal features and capacities for investment purposes.

In complement, please find here our Recorded WebinarPredict financial movements with web data”.


In the past, investment management institutions relied mostly on traditional data to gain an edge in investing. Traditional data ranges from SEC filings to earnings reports and pricing information any type of data produced by the company itself. The rise of the digital age, however, has opened up new sources of data for investors beyond the scope of traditional data. The seemingly infinite scope of alternative data includes data produced from credit cards, satellites, social media and perhaps most importantly the web.

With the additional integration of alternative data, investment management institutions and hedge funds in particular that once relied only on traditional data now have an edge in predicting the rise and fall of the markets. As increasing numbers of financial institutions jump on the bandwagon of alternative data, spending on alternative data by trading and asset management firms is set to exceed $7 billion by 2020.[1]

What was only a few years ago a question of when institutions should start using data has shifted to the question of how they can organize and structure these mostly unstructured datasets. And with 4 billion webpages and 1.2 million terabytes of data on the internet estimated to be generated globally by 2025, there is no shortage of web data to sort through. As increasing numbers of investment management institutions incorporate alternative web data into their predictive algorithms, it will change the face of investment as we know it.

This white paper is intended to be a guide for investment management (IMs) institutions to better understand how alternative web data is quickly becoming an essential component for generating alpha and mitigating investment risk. In addition, it explores different models of web data crawlers and what IMs need to look for as they incorporate alternative web data into their predictive analytics models.

Section 1: Beating the Market with Alternative Web Data

“Your company’s biggest database isn’t your transaction, CRM, ERP or other internal database. Rather it’s the Web itself…Treat the Internet itself as your organization’s largest data source.”

As previously mentioned, alternative data includes any type of data that is beyond the scope of traditional data: satellite imagery, social media data, and web data (which includes news sites, blogs, discussions and forums) along with credit card data. Alternative web data, which falls under the broader category of big data, is typically unstructured and demands a process for structuring it in order to deliver insights.

[1] Alternative data for investment decisions: Today’s innovation could be tomorrow’s requirement. Deloitte Center for Financial Services. 2017.

Access the full whitepaper