hero bg

Lord Abbett

Reporting Process

About The Client

Since its founding in 1929, Lord Abbett has maintained a singular focus on the management of money. As an investment-led, investor-focused firm, Lord Abbett evaluates every decision from an investment perspective, in an effort to achieve a superior, long-term investment performance on behalf of its clients. Currently, Lord Abbett has more than $140 billion in assets under management.

Work Carried Out

We helped to develop a monthly process, to summarize and disseminate portfolio statistics, such as sector and credit quality breakdowns, average durations, and holdings concentrations. The system handles mutual funds, separately managed accounts, institutional client accounts, and third party indices and blends. As a part of the client’s team, we created a consistent process to:
  • load and normalize position and reference data from a myriad of sources,
  • assign each security a unique ID,
  • run analytics through Yield Book,
  • classify securities according to an asset className, industry, geographic region, and more.
For all this, the system supports different values in different contexts, such as when comparing a fund to different benchmarks. It handles funds-of-funds, differing policies for split credit ratings, and offers various approaches for handling derivatives and defining cash.
Each month, the system processes more than 20,000 instruments, and generates more than 5,000 reports for use on lordabbett.com, in the client materials, and in the reports for Lord Abbett’s board of directors.
The system uses metadata to define how to extract the holdings of various portfolio data sources, convert them to a standard form, normalize their values, and match their positions to securities that may already exist in the database. It provides for human override and complete history tracking of all values.
In the course of this engagement we introduced additional agile development practices. We set up an automated testing environment on TeamCity, and configured it to run the entire process with a limited data after each code change, and full overnight run on a clone of production data. Also, we helped introduce automated testing, continuous delivery, frequent deployments, and shared code ownership. These practices are now spreading throughout the firm.

Applied Technologies

Java

Java

Python

Python

Azure SQL Server

Azure SQL Server

Batch Script

Batch Script

Perl

Perl

Hibernate

Hibernate

TeamCity

TeamCity

Project diagram

Data
Providers
Data

Ratings Retrieval Process

Preparation to data processing
View the case study

Reporting Process

Moulding data into required format
Go to the top

Reporting UI

Presenting reports for the user
View the case study

Get in touch

Contact us