BI from Big Data – Role of Analytics in Investment Management

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In today’s digital age, there is an explosion of data. Businesses have beginning to see a lot of value in analyzing this data to derive actionable insights. Industries like healthcare, telecom are at the forefront of this revolution. Simply by analyzing 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.

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.

In this blog, let us have a look at various areas where investment managers and asset managers can leverage the power of big data and data analytics.

Identify Investment Opportunities and Risks

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.

Consumer Behavioral Understanding

The wealth management space is now able to leverage behavioral science with the help of analytics technologies. Since financial planning is highly emotional, wealth management firms across the world have started using data analytics to better understand the emotional behaviors of their customers. They have started tracking the customers’ decisions, social media activities, and spending patterns to get a better understanding of the customers’ attitudes and personalities. Armed with this knowledge, the investment managers are able to better design their investment strategies. Big data analytics can also help in preventing losses which occur due to panic selling. Wealth management firms are using these technologies to effectively match advisors to clients based on their mutual personalities so as to create a conducive work relationship.

Predictive Analytics

Predictive Analytics, the new modeling capability, has the ability to change the investment nature. Based on the statistical analysis of the advisor action, it can provide a likelihood of future purchases. Using predictive analytics, the investment firms can take better trading decisions and enhance the outcomes. It can also help them in managing risks and forecasting wider market trends; thereby driving new business opportunities. In the near future, by identifying repeatable, projectable events, predictive analytics will influence risk/return ratios and move the markets based on those.

Portfolio Optimization

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.

Which data can be mined?

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.

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