How Smart Contracts Can Ensure Data Integrity for Trillions in ETF Assets

This article was originally published by Traders Magazine,


Index funds have exceeded their actively managed counterparts. With this concentration of assets in ETFs comes operational risks that bear some weight.


By Nathan Wells, Chief Product Officer at Symbiont

According to the Investment Company Institute, passive funds accounted for 16% of US stock market capitalization at the end of 2021, exceeding 14% for active funds and marking the first time that index funds have exceeded their actively managed counterparts.


That trend is expected to continue, led by some of the largest investment managers globally, a desire for lower fee solutions, and the rise of exchange-traded funds (ETFs). With this concentration of assets in ETFs comes operational risks that bear some weight.


One problem is managing the distribution of data that underlies ETFs: Index data. Using smart contracts to automate the distribution of this data can maintain the integrity of gigabytes worth of data that are transmitted quarterly.


Why is that a crucial problem to address? Because every basis point counts. Harnessing big data equates to efficiencies across the board and rings true for all those involved in the ETF ecosystem, including index providers, and the asset managers that benchmark their funds against their data.


Smart contracts automate and ensure the delivery of high-quality data to ensure the flawless rebalancing of index-linked ETF data. The magnitude of potential errors that result in portfolio losses, lawsuits, and missed licensing revenues to index providers is significant, yet could be avoided.


As an example, Vanguard currently bases $2.3 trillion of its ETF assets on data distributed via a blockchain-based platform and when other asset managers objectively assess their operations, we believe they will look to the benefits that distributed ledger technology offers.


The ABCs of ETF rebalancing

When it comes to quarterly rebalancing, portfolio managers are under enormous stress to ensure that they don’t lose hundreds of millions of dollars. They need to ensure that the ETF is rebalanced appropriately based on data shared with the industry. With trillions of assets under management, small mistakes yield potentially disastrous outcomes.


ETFs are based on underlying market indices and the managers of those ETFs spend substantial time and resources to ensure that their ETF properly tracks those market indices.


There are several ways of composing indices. One way involves algorithms that calculate the weighting any given security in the index should have. As prices change, the algorithm to generate weightings changes dynamically based on pricing and new constituents. This critical process, modified and updated on a quarterly process, is known as rebalancing.


Accounting for this shift in the index data results in a time-consuming endeavor with an embedded risk for asset managers, as these rebalances can reveal swings in asset allocations worth billions of dollars. Missteps can reveal a faulty process and more importantly, can be costly. Invesco, for example, had to refund $105 million because of a data-driven error. This will not be the last such incident given the antiquated technology in use, dating back to the 1980s and in some instances the 1970s!


A single source of truth

The magnitude of data across the securities market is gargantuan and is magnified during rebalancing. When we onboard data it involves an automatic ‘pre-conciliation’ process: eliminating multiple copies of the data to provide a single copy shared amongst all participants. This highlights just one blockchain-enhanced advantage; and further cements the importance of uncompromised market data for investors in a world of information overload and the need for a single source of truth.


We ingest 225 megabytes of data daily from an FTP site that we then process into a standardized format, relaying those on the blockchain so it can be acquired and then incorporated into other systems. The time saved between data being published and trade orders executed results in a reduction of risk and increases efficiencies. By providing a single source of immutable truth, blockchain technology has the potential to eliminate errors, saving time and money.


In the case of intercepting bad data, we can alert the provider of the error so that they can efficiently correct it. In addition, this approach allows asset managers to inquire about potential data quality issues and for data providers to improve quality, via “restatements”. When an index provider fixes a data quality issue, it gets broadcast to everybody participating on a blockchain in real-time. Other approaches are not actually solving for data quality, but merely redistributing (possibly faulty) data.


Active asset managers also need to be on top of this process as many of them benchmark against market indices. The data integrity aspect of performance, especially when one is solving for a universe that houses trillions in assets, is paramount. Index providers also benefit as data is licensed for a fee, and the transparency of the blockchain gives them more control and visibility of redistribution.


Every basis point counts

Fund managers are looking for cost efficiencies and transparency and any cost savings resulting in lower management fees get passed along to the end user, which equates to greater potential returns. In addition, the power of blockchain technology provides built-in security mechanisms and safeguards, such as historical and real-time auditing and selective and permissioned sharing of data.


Risk managers and other teams involved in this rebalancing function should see where they stand and analyze how this process can be fine-tuned. Running an exhaustive analysis exposing current exposure and investigating how your firm can standardize and increase data quality will reveal that significant efficiencies and risk mitigations are possible.


Smart contract technology is a driving force in the search for a single source of truth and the next iteration involves harnessing big data to develop impactful insights and analytics on top of data streams.