Data is considered vital in understanding the exact nature of a problem by providing a holistic view. As a resource, data points out exact areas that can create the most impact on your investment decisions. For instance, when used to generate models, the information now becomes an integral part of the process of benchmarking and grading your investments in a way that ensures they are better targeted. Further, with the help of public data, you can prop up future investment decisions and create experiments.
But all these methods are only possible after you gather the needed public data. This fact underscores the role of web scraping, which, by definition, refers to the science of automatically collecting publicly available data from websites. Given that the internet is home to a trove of information, most of which is unstructured, web scraping has proven an impactful process in data-driven investing. This article takes a deep dive into what this role is.
The rise of data-driven investing
The internet gave rise to the information age that enabled individuals and independent investment advisors to undertake significant analysis of the markets, a process that previously could only be undertaken by big institutions.
The internet also provided a convenient way to collect information on stocks and monitor breaking news, thus enabling investors to make more informed investment decisions. It also enabled them to come up with useful and actionable investment strategies. Since then, the concept of data-driven investing has expanded to include other elements that were initially not present, e.g., alternative data (also known as external data), thanks to the increased prevalence and accessibility of public business-related data from platforms such as social media sites.
Moreover, data-driven investing has evolved from being primarily applied in the financial markets to its adoption by multiple companies across various industries.
What is data-driven investing?
Data-driven investing refers to using and analyzing traditional information and alternative data to obtain more precise, accurate, and clearer insights about investors, identify risks, and more.
Notably, alternative data is any type of data that has not traditionally been used in the financial services sector that provides investors and businesses with an advantage over their competitors. Other examples of alternative data, besides public web data, include satellite images and internet web traffic.
Top trends in data-driven investing
As technological developments have made data-driven investing more ubiquitous over time, they also helped make it more sophisticated and effective. This has led to top trends, which include:
- Artificial intelligence
- Machine learning
- Social media sentiment analysis
- Using alternative data
- Using unstructured data
AI investing relies on big data to automatically improve your active return on investment or alpha. Machine learning uses algorithms that can learn from existing data and subsequently adapt the results based on newly generated data. ML offers an analysis of data in real-time and comes in handy, especially in today’s fast-paced investing environment.
Social media sentiment analysis, as one example, provides information about how customers perceive a brand. It entails the use of software programs that not only analyze reviews, online feedback, and mentions but also show the general emotions customers hold towards a business.
As a process that, by definition, entails using alternative data, data-driven investing departs from the traditional reliance on conventional data such as pricing information, news stories, and financial statements.
Technological advancements have made it possible to use data such as satellite images and internet traffic to create a holistic picture that perfectly describes a situation, in effect informing the investment decision. Technology has also facilitated the use of unstructured data or data that cannot be quantified. These include images, audio, videos, webpages, transcripts, documents, social media posts, and more.
The driving force behind data-driven investing
With the use of unstructured data being on the rise in the context of data-driven investing, combined with the internet being home to large volumes of unstructured data, it’s easy to see how web scraping is a driving force behind research-driven investing. It is, in fact, a fundamental building block if you need to analyze and use data successfully.
The role of web scraping
An automated way of collecting publicly available data from websites, web scraping provides an infrastructure-based foundation that allows you to leverage data and make better investment decisions as a result. But that’s not all.
Web scraping offers capabilities not available elsewhere. For instance, it can transform unstructured data collected from different websites into structured data that you can then use in analysis.
Additionally, advanced web scraping solutions sift through the noise on websites. They are, for instance, designed to avoid useless links and therefore collect public data faster. They are also scalable because you can use them for both large-scale and small-scale data extraction applications. As a result, the solutions also save on costs that companies would have otherwise incurred while investing in scraping hardware and software that could lie unused when the demand drops.
What’s more, these solutions usually have in-built tools that ensure data collection proceeds unencumbered. Some rely on the service provider’s large IP address pool, while others use advanced capabilities to avoid CAPTCHAs, honeypot traps, and more. They, therefore, ease data collection in ways that were previously impossible to achieve.
Benefits of web scraping in data-driven investing
When it comes to data-driven investing, the capabilities mentioned above offer immeasurable advantages. Usually, in order to come up with actionable data that can be relied upon when coming up with investment strategies as well as making investment decisions, you have to follow a process described by the investment data funnel. First, you have to collect data (upstream), derive insights from the data (midstream), and convert the insights into investment strategies (downstream).
While necessary, the various stages could potentially lead to an information overload, especially if you deal with a lot of data daily. This is why companies, including asset management firms, are embracing new ways of gathering and analyzing data at various points across the asset management value chain. According to a McKinsey study, this has resulted in greater productivity, better investment decisions, and sophisticated distribution.
Companies are applying advanced research processes that automatically collect and analyze public filings and flag changes in sentiments. Such technologies rely on technologies, such as natural language processing (NLP), that aids in processing vast amounts of information quickly.
Furthermore, asset managers are currently looking at various sources of investment research. These include social media sites; online platforms connecting investors to corporates; analyst-consensus platforms that crowdsource estimates of equity and fundamental economic data; structured data from financial statements; and data stored by independent research providers. With web scraping, you, as well as these asset managers, can easily access data from each of these sources.
Challenges of web scraping for data-driven investing
Although web scraping for data-driven investment is beneficial in many ways, it’s still plagued by a few challenges, including:
- Geo-restricted content
- Anti-scraping techniques
- Complexly structured web pages
- Vast amounts of data and unstructured data
The interconnectivity that the internet offers has made investment opportunities available across borders. However, this has also resulted in a secondary problem associated with geo-restrictions. Some websites containing vital data integral to formulating investment strategies could restrict their content to visitors within given territories. This makes data extraction a challenge.
Solution: web scraping providers often have vast IP address pools with IPs drawn from multiple countries globally. They are, therefore, able to circumvent such restrictions.
Companies and their web developers often deploy anti-scraping measures, including IP blocking, CAPTCHAs, honeypot traps, and more, to safeguard the content stored in their websites.
Solution: as with geo-restricted content, web scraping solutions use vast IP pools and capabilities that mimic human behavior to avoid anti-scraping measures.
Complex structure of web pages
Given that there is no universal standard governing web design, web designers can use their own styles and even experiment. This is regardless of the fact that virtually all websites make use of HTML and CSS. The variations result in complex structures that complicate web scraping. Combining this with dynamically changing content compounds the problem even further.
Vast amounts of data and unstructured data
Companies and news outlets constantly update their websites with new information, not to mention social media users. This generates vast amounts of data that is mostly unstructured. Combined, these aspects could curtail smooth web scraping.
Solution: advancements in data extraction technologies, such as AI-enabled data parsing, have dealt with this challenge.
Web scraping is indeed the driving force of data-driven investment. It forms the foundation of creating actionable investment strategies and is a crucial upstream stage of the investment funnel. This is because it provides the data needed in the analysis stage of coming up with insights. However, despite its importance, it’s still plagued by a few challenges, all of which are no match for advanced web scraping technologies that effectively deal with them, smoothing the process.
Learn more about how finance companies pay the price for overlooking alternative data or what financial data is by checking our other blog posts.