Every day, each of these services generates thousands of pieces of data. As a result, data management is regarded as the most critical aspect of these facilities. Any data loss can trigger significant issues for that particular financial sector. Financial analysts nowadays rely on external and alternative data that helps them make crucial investment decisions. Big data has a major impact on economic analysis and modelling. 71% of firms in the BFSI sector use big data analytics to gain a competitive advantage.
How equity research companies are using big data
Businesses today have to deal with massive amounts of data, and they often face a variety of challenges in extracting and producing useful information from large datasets in a timely and effective manner. Data collection for equity research reports usually takes enormous amount of time and consumes several resources. Fortunately, the right software can help coordinate and speed up the process, making it easier to complete time-consuming analysis and data entry activities.
Data discovery
For producing quality reports and generating useful analytics, having data available in silos isn’t going to work. The ability to scan through all of the material in total that too with contextual meaning is essential for any successful business system to succeed.
Adding context to data and/or identifying relationships between data to determine a graph is becoming increasingly important as big data evolves. The ability to derive information from data increases the value of the data that is available. Leading equity research companies can use semantics to describe and index information which can be used to deliver a contextual search tool, thanks to modern technology frameworks.
Semantics capabilities
Big data may play a role in assessing and leveraging relationships about recent news and events. Equity research companies must use data discovery capabilities as well as data provenance through semantic technologies that allows the equity research analyst to reach out to the companies that may be directly affected by any recent event or news.
While event/news information could be available to equity research teams, they are likely to come from different sources, and combining them can be a project in itself due to diverse nature of information. The massive data from different sources would be unstructured, and modelling unstructured data would be counterproductive in the vast majority of cases.
Data available in silos
In today’s corporate world, having a holistic view of data from a single application is often a challenge. Data is often dispersed through various systems and environments. Data relevant to equity analysis should be accessible to analysts through a single application/platform for searching and discovery.
The application that runs on big data, artificial intelligence and machine learning could allow the analyst to browse through both internal and external data sources, uncover information, and get a glimpse of the underlying stock’s technical analysis. This can give the equity research analyst a 360-degree view of the data related to the underlying stock.
Real-time content delivery
Asset managers pay a fee to their prime brokers in exchange for access to a flood of macroeconomic, policy, business, and stock reports. There is a huge amount of content generated, and there isn’t enough time to digest it all or quickly find relevant intelligence in the haystack of PDF files. Analysts should be able to customise custom reports for targeted customers using small but highly applicable and contextual data through research portals that work on big data.
Fundamental analysis
When working with an investor, an equity analyst wants to assess a company’s long-term value and potential return on investment. This method entails a number of phases such as analysing individual businesses and assessing marketplace investments. With the help of big data, equity research companies can bring all of the data together for a more streamlined and accessible review in a fraction of the time it takes to hand-compile data, rather than sampling hundreds to thousands of pieces of data and searching for trends to decide the company’s importance.
Earnings forecast
To assist analysts and investors in putting together valuation models, equity researchers look at a company’s or sector’s growth and profitability. Multiple quarterly earnings reports are also used by equity analysts to predict how a business or investment will do over the course of a year. However, as more data becomes available, those estimates can change. That’s why it’s important to use scheduled data updates, with the help of automation and big data, to stay up with the latest data and keep projects as current as possible. This will aid in making crucial investment decisions.
Otherwise, equity analysts may end up with a slew of out-of-date data that skews analytics and reports before the team has had a chance to thoroughly examine the valuation models.
