Big data: What it’s doing right now for investor relations
More data gives us more information — but data by itself is meaningless. The translation of data, in a language that people can understand, is crucial to supercharging our use of the massive datasets created every day. More resources are being allocated to big data analytics, which are the application of mathematics to large datasets that filter out the noise and bring clarity to the meaning behind the data itself.
Big data analytics applications have reached investor relations teams. Predictive results have improved exponentially, and we are now capable of providing real-time solutions that historically would have taken months to calculate, if at all.
Here are three ways IR professionals are leveraging big data in their day-to-day.
Big data analytics have opened up our ability to identify activists. Real-time calculations of the largest datasets at our disposal have triggered the ability to monitor activist situations as they develop and before they develop.
Specifically, our quant team spent some time analyzing campaigns initiated by the largest 50 activists as the most significant events to predict. These activists account for nearly half of all dissident campaigns and, because their buying is a repeatable pattern, it has the potential to be predicted. For the purposes of our analysis, we focused our attention on determining and creating predictive indicators based on this subset of activists.
Our scoring system uses sophisticated models and historical analysis to create a baseline, or assumptive “normalcy”, for each stock. Through machine learning, algorithms closely monitor equity and options order flow, including liquidity analysis, volume velocity, and trade pattern recognition, to identify when current trading activity veers from these preset norms. IR teams are then at an advantage: they can help their management teams address problems before they become outright crises.
Our latest whitepaper on activism provides more details on how big data can help IR teams anticipate activism within their shareholder base.
No longer is the activity on the NYSE the only source for trading information — information on public companies has never been more abundant. Social media outlets, news sources, and a multitude of data suppliers have now made it possible to gain a deeper view of a company’s trading environment. Vendors like Bloomberg and FactSet provide a wealth of knowledge available at the click of a button, which historically would have taken months to compile.
Big data analytics allow us a bird’s eye view of data living on all available channels. When we combine multiple data sources, we’re granted a holistic picture of all the driving forces of a company. Longer term inputs like company fundamentals can now be combined with shorter term measures like daily trading patterns, through high-speed computing that generates new datasets. Those datasets are then evaluated from a multitude of perspectives, to determine whether they are predictive qualities for an IRO, such as stock trends, accumulation patterns, or even activist activity as previously discussed.
The days of targeting based solely off a peer list are behind us. High-speed computing gives us the ability to examine which factors are important for specific funds. Big data analytics peel back a deeper layer of insight as to why funds are buying certain companies and not others. Data analysis provides an additional understanding into a fund before an IRO takes a roadshow meeting. This insight now facilitates more productive, targeted meetings and roadshows.
An IRO is now able to craft a directed message for a specific fund, based on those measures valued by the targeted fund. Big data analytics can now tell an IRO which funds are more likely than others to purchase their stock, meaning that roadshows become more productive and measurable in their success.
When we apply the same process of targeting, but in reverse, we gain a better understanding about which current shareholders could be more at risk of reducing or closing their long position. When you combine this insight with the knowledge of a surveillance analyst, IR teams are granted a full view of who is trading one’s common stock.
Big data will continue to grow
As big data become a more integrated part of our lives — the Internet of Things will soon cause big data to be generated from the objects of your own home! — the translation of big data will continue to become more sophisticated. Artificial intelligence and machine learning applications to data analysis are on the horizon for all industries, IR included.
As big data continue to grow in importance and output, it will be more crucial than ever to translate that data into valuable, useful applications. Through thoughtful examination and translation, we will continue to develop new tools that will provide deeper insights into the everyday tasks of an IRO.