Case Study

Hedge Fund Technology Strategy for Long Term Growth

The Challenge

The hedge fund grew from 800 million in AUM to over $4Billion in AUM over a three-year period. To generate returns and hedge the exposure, the hedge fund went from trading on one major strategy, which involved utilizing mostly equities and U.S. only market exposure, to trading on five major quantitative strategies, diversifying the risk by trading both equities and commodities on the global markets with exposure in Europe and Asia, in multiple currencies.

This required major updates to the data collection, data analytics requirements, as well as the applications and services running to support these hedging strategies. Due to the fast-paced nature of the industry, the firm kept making incremental updates to the current system over the three-year period.

The hedge fund’s technology architecture, infrastructure, and platform were not keeping pace with the dynamic changes in their hedging strategies. In addition, the operations department was busy with the day-to-day requirements of running the fund.

Our Solution

The Chief Operating Officer (COO) at the hedge fund explained the scenario in detail to the Cognivo team over multiple discovery calls and on-site meetings with operations and management. The Cognivo team decided to take a twofold approach to the challenge at hand.

The first step was to understand the architecture requirements looking 3-5 years into future growth prospects of the hedge fund, as well as high-level insights into the trading strategies that might be utilized.

To immediately alleviate the major pain points, Cognivo decided to refactor the current system and implement incremental updates to keep the system viable for the next 3-6 months.

During this period, Cognivo re-engineered the current data requirements, data dictionaries, application architecture, and other core requirements that supported multiple asset classes and currencies.

With extensive knowledge and expertise in the hedge fund industry, the Cognivo team was able to recommend two new vendors to support basket trading and other highly-specific trading requirements, as well as post trade allocation and settlement in multiple currencies and asset classes.

Based on Cognivo’s recommendation, the hedge fund also started the process of slowly moving the application and services to a highly-secure private cloud, hosted under Microsoft Azure web services.

The Result

The new system designed met the client’s goals, as described in the Challenge. Cognivo’s extensive knowledge and experience in the hedge fund industry allowed for understanding of the current systems without any major documentation. The legacy applications and services were successfully reverse engineered in a relatively short period of time.

Prior understanding of various types of hedging strategies and knowledge of numerous asset classes, allowed the Cognivo team to start small and perform rigorous testing and validation of the new reports against independently-built Excel spreadsheets, as well as current reports being used in production.

Cognivo provided constant updates to the COO on a weekly basis, as well as when needed. The project was successfully completed in less time than initially anticipated, and under budget.

As a result, the hedge fund decided to retain Cognivo for technology and software advice, as well as provide vendor recommendations on an ongoing basis.

Major Benefits

New Business Opportunities

Confident in their technology infrastructure, the hedge fund felt like they had a solid foundation to continue to accept new business, increase their AUM, and achieve their business goals.

Reliable Trading Desk & Operations

According to the COO, a big advantage of this new solution is that this allowed them to not worry about the technology infrastructure on a day-to-day basis. It also allowed the COO to be more confident in supporting future changes to the trading strategies.

Efficient Workload Management

According to the COO, another big advantage of this new solution is that this allowed them to better utilize their small team and get more work done, while keeping the team small. The middle and back office operations team no longer needed to gather data manually from various sources, and then reconcile the data after generating the reports.