Consider a scenario. You have invested in a food chain restaurant in New Delhi. As it is difficult for you to keep a tab on performances of all of their stores, you hire a human fund manager to ease out this process for you. A fund manager will be watching the investment on at least a quarterly basis, checking the profit margins, return on invested capital, same store sales and other key performance indicators that are disclosed by the company to its investors. If the manager sees a trend, say slipping same-store sales and an erosion in the profit margins compared to last quarter, she may decide to sell the stock. If the opposite is true, she may decide to buy more.
Now equip the Human Fund Manager with a predictive model pulling in data from all over. Instead of waiting for the quarterly reports, she can see models approximating the changes in same-store sales based on social media posts by customer’s cross referenced with the transaction data and GPS data from opt-in Smartphone users for all the locations. Analytical software aids her in mining the data and recommends an action, allowing for her to unload or add to the position long before the change in sales appears in an official document. In other words, there is no longer any time lag in seeing the company’s results so investment decisions can be made on up-to-the-minute information that approximates the company’s actual situation.
In today’s digital age, there is an explosion of data. Businesses have beginning to see a lot of value in analysing this data to derive actionable insights. Industries like healthcare, telecom are at the forefront of this revolution. Simply by analysing the patient streaming data, the healthcare industry experienced a 20 percent reduction in patient mortality. The telecom industry has seen a 92% increase in processing time through the analysis of call data and network data.
According to a recent industry report, companies that invest in analytics have 33 percent higher revenue growth, 12 times more profit growth, and 32 percent higher return on invested capital than their peers. To further this point, a recent examination of Nucleus Research ROI case studies found organizations earn an average of $10.66 for every dollar spent on deployment of analytics applications such as business intelligence (BI), performance management (PM) and predictive analytics.
Similarly, there is a tremendous amount of data about each company which directly or indirectly affects its stock prices and other investment opportunities. However, many investment managers and asset owners are not able to tap this resource because of lack of the right analytics tools. With powerful data analytics, investment firms can make smarter decisions, create value, and deliver results. Instead of relying on just the past experience, investment managers can leverage predictive analytics to make smarter investment decisions for their clients. While financial
services firms have been a little slower in adopting big data, there is an increased awareness and recognition of the value from mining loads of data in finance. A survey of 400 investment firms, conducted by the Economist Intelligence Unit and State Street, saw that for 91% of the respondents, data and analytics was a strategic priority.
Investors can very well use data analytics and data interpretation tools to identify the right investment opportunities and also the risks. It can help them effectively manage risks across multi-asset portfolios and allow them to take faster and smarter investment decisions. The timely insight into the right portfolio data can allow them to leverage the “high ticket items” and readjust the portfolio for better returns and less exposure. It helps them with manager assessment, manager strategy overlap, and factor analysis.
Using big data analytics, investment firms can quickly test complex scenarios. It can help them in understanding their portfolio exposures. Using easy-to-comprehend dashboards and visualization, the firms can explore ways to optimize gains and minimize risks across their portfolios. Using advanced analytics, the portfolio managers can fine-tune their strategy in real-time by reducing the barrier between trade origination and trade execution. It can help them in enhancing the overall portfolio performance.
The three V’s—variety, velocity and volume—are often used to describe and define big data. You need all three to do any meaningful analysis. Variety refers to the channels of data that are being tapped. This can be everything from social media mentions to weather reports and bulk transaction data. Volume is the amount of data coming in and, like all the V’s, more is better. The volume and the variety of data allow for outliers to be either verified or eliminated and lead to more accurate data overall. Velocity is simply the rate at which the data flows in. For predictive analytics to be valuable in terms of driving profitable trading, the data has to be available quickly for analysis, meaning a constant stream of up-to-the-minute information.
Now the question arises which data can be mined. Well data, today, is being captured from a variety of sources. The non-traditional sources of data include social media conversations, satellite images, quarterly results of companies, economic reports, minutes of executive meetings, industry insights, information about mergers and acquisitions, government contracts and so on.
In order to leverage Big Data and Analytics, Investment firms need to clearly identify their requirements and base a solid foundation to support big data analytics at the operational level. It has the ability to empower the investment managers by offering them a much more granular data in real-time and facilitate quick decision making.
Now investment in analytics seems to provide promising returns on our investments and help us in our strategic decisions. Well, they do deliver their promises but there is a catch. With every technology comes their limitations and analytical investments are no exception. Lack of alignment with strategic goals, Poor integration with business as usual, limited frontline
adoption and poor data quality and accessibility may lead to failure in reaching a firm’s objectives. There have been numerous cases where right data was not fed into the algorithms and as a result, all the analytics investment languished without adding the business value it was intended to achieve.
There are some guidelines which if followed diligently will help in getting the most out of the data mining technology. The first step toward scaling analytics is creating a clear road map based on use cases that support priorities across the value chain. Each analytics initiative should be ranked objectively based on its potential value to the business. Build analytics results into performance management. Leaders must embed analytics into their organization’s DNA by making it an enterprise priority and managing to it. Define clear governance across the organization. Organizational transformation requires clearly defined governance, roles and responsibilities, and escalation and decision-making processes. Launch a change campaign. Like any transformation, a data and analytics transformation requires changing the culture and the daily operating model. As one frontline employee put it, “I can’t execute it if I don’t understand it.” The partnership between business, analytics, and technology functions throughout the transformation—from selecting use cases and understanding the modelling output to executing on insights-is crucial to any analytics program.
It is easy to say that in investing, as in conversation, too much information can be a bad thing, but this may just be a case of holding on to the world we are used to. Time will tell whether predictive analytics is a valuable source of insight or another source of short-term market noise .
Member – Alumni Relations Team