What does it take to keep an index up to date? Until recently, the only way to manage this was through spreadsheets with analysts whose daily duty was to look at and update the spreadsheets on a rolling schedule of whichever indices needed to be rebalanced next. Not only did this exhaust a tremendous number of expert man-hours, it was also poorly scalable and prone to errors. When this happens, it not only corrupts the piece of data in question but has a domino effect on the data that follows, creating more work, under more pressure. 

With the emergence of Index Management software, Exchanges, Finance Agencies, and Financial Analytical Firms can unify the technology stack and use Java and Python to allow them to forget about those tedious manual rebalancing and focus only on delivering their fleshed-out methodology. Doing this not only frees up working hours but reduces risk.

Currently, many indices have old legacies that are still maintained through this method of manual rebalancing, and the cost of this process grows exponentially. Rather than spend even more time, money, and manpower trying to implement old legacies into new software, it is the best practice to build the solution on top of an existing data stream for uniform access. Accessed by a set of real-time and historical APIs, the data set will always be complete. This offers a set of tools and libraries that are flexible, meaning that if you want to run analysis today or tomorrow, it doesn’t matter. 

The right software frees indices from inefficient legacy systems.

Not all index management software is created equal. The benefits of a solution that has its APIs to existing data feeds cannot be stressed enough. Without this feature, you have to be extra sure at all stages when re-uploading data and produce results before it goes to someone else for review. If there is a correction needed, it invalidates the data and needs to be recalibrated again. When current index management software solves a problem, it will not happen again in the future.

When there is a uniform stack of technologies, there are no problems in combining different pieces of outdated or even incompatible technology. It provides full flexibility in the data retrieval process so that any inconsistencies can be rectified without the exhaustive man-hours. 

Further still, if the technology stack is connected to historical data feeds, you’ll have an incredible tool that shows all previously distributed indices updated in real-time for anyone looking to build a custom index.

It is always important to have scalable solutions when working with large amounts of data. Once these solutions are in place, monitoring becomes much simpler. Sets of monitoring components can be set up and configured for each index. When a trigger is activated (e.g. a stock gets delisted), the analytic team will get a notification. This saves further manpower that would otherwise be spent on this constant monitoring. 

For exchanges, this type of software can cover sets of indices in a scalable way. For example, if you’d like to build one index, you could create ten instead with slight variations and without ten times the work. The storage, real-time updates, and backtesting all become scalable solutions at a fraction of the effort. Spreadsheets scale poorly, without the software you’d need ten spreadsheets and exponentially more manpower.

Supporting an index is a difficult task, but the solution to doing it efficiently and effectively lies in having the correct software.