For Global Financial Advisors, Fuzzy Logix Analytics is a Smart Investment

One of our customers, a global investment firm, manages more than $2.4 trillion (US) in assets. In other words, there’s a lot riding on their decisions. The investment firm has long used data analytics to give its investors a competitive advantage in the market. Over the years, they’ve amassed a huge volume of data, including years’ worth of data on every stock in the NASDAQ 100, S&P 500, S&P 1500 and Russell 3000 indexes.

As even casual market watchers know, stocks are a volatile commodity. Prices change quickly, trends emerge and disappear, market sentiment fluctuates—all of it driven by business news that feeds in from around the globe, 24 hours a day. In such an environment, real-time data analytics can provide actionable insight that significantly impacts revenue and reduces risk.

Too Much Data and Not Enough Insight

Financial calculations are complex by nature, particularly when dealing with concepts like risk and volatility. While the investment firm was able to use their existing analytics platform to drive many of their decisions, there remained certain calculations that were too complex to perform in real time with their existing solution, such as value-at-risk and equity analysis. Part of the problem stemmed from the size of their data; in order to analyze terabytes of market data, the company needed to split the analytics problem across multiple analytics servers and re-compile the results. This took hours and, sometimes, days, by which time the data was no longer valid. What they needed was an analytics tool that could analyze their data quickly without requiring the extra step of moving it and re-assembling it.

In looking for an analytics solution to augment their existing environment, the investment firm needed a tool that would work seamlessly with their Teradata database management system, support SQL queries and integrate with visualization tools like Tableau. That criteria led them to Fin Lytix from Fuzzy Logix.

Moving at the Speed of the Market

Fin Lytix is an in-database analytics solution that features hundreds of analytic algorithms for financial markets, which are specially coded for massively parallel environments so that all analytics can be performed right in the database. The firm, working together with Fuzzy Logix, quickly identified several business scenarios where Fin Lytix could dramatically accelerate their analytics:

  • Calculate the Value at Risk (VaR) for a wide range of portfolios by quickly answering questions like “What is the maximum loss of risk for a given portfolio at a point in time?”
  • Measure the risk associated with taking various positions on a stock option or other derivative, known as Greeks because of their Greek alphabet denomination (Delta, Gamma, Theta, etc.)
  • Analyze customer portfolios to provide recommended stocks to their customers based on customer segment
  • Improve the accuracy of their data through text matching in order to remove inconsistent or erroneous data (e.g., misspelled names, impartial entries, etc.)

Equity analysis is another important area where Fin Lytix is being applied. Using standard SQL queries, the firm’s quantitative analysts are now able to measure and predict the performance of individual stocks in less than a second, down to a stock’s individual characteristics including volume weighted average price, liquidity, volatility, momentum, risk versus return and more.

Reducing Risk and Increasing Market Agility

Today, the investment firm is better equipped to anticipate and adapt to market changes and opportunities. That’s good news for its customers and the trillions of dollars it protects. Looking ahead, it plans to use Fin Lytix for its credit portfolio management as well. Credit portfolios are inherently risky, and the firm aims to minimize that risk through real-time analytics that provide better insight into each loan’s investment worthiness. Clearly, their investment in Fin Lytix is one they expect to pay dividends well into the future.

Credit risk analytics