It seems that most financial products have their own tickers: equities, options, and even corporate bonds have a data repository that turns out the latest price and quantity traded. On the surface, it would also seem that new intraday securities lending data products could be another ticker, providing the latest volume and rate information for the most recent loans. Under the surface however lies a range of differences that make it difficult to draw a meaningful parallel between the tickers of publicly traded products and the world of securities lending. If these challenges are met however, the results may be surprisingly positive.
The main challenge to creating a securities lending ticker lies in the standardization of loans themselves. While some securities loans may be considered standardized, the majority are individual arrangements between two parties that depend on volume, duration, relationship and cost. A loan by a pension plan that spans a dividend period will look different than a soon-to-be-recalled loan by a mutual fund at the same time. Likewise, a loan for a general collateral stock with a high yield collateral strategy behind it will look very different than a general collateral loan investing in repo for cash collateral. A ticker could potentially distinguish between different types of loans, and data vendors already pull out exclusives, but the more nuances and caveats in the data stream, the more its value is diminished.
In equities and other public trading markets, product standardization has led to the growth of data analysis tools, including transaction cost analysis, pre-trade analysis and the universe of algorithmic trading. These tools are so widely accepted that institutional investors use them to compare asset managers, asset managers use them to compare brokers, and brokers use them to compare exchanges and even clearing firms. A stream of data from standardized trading products has been proven to be very helpful.
The possibility of creating such a ticker for securities loans depends on how data can be standardized. Without standardization, securities lending data looks similar to mortgage data, where loans might be commercial or residential, across multiple regions and countries, and made to borrowers with varying credit scores. Some form of standardized securities lending data might be close enough to equity or options data to make it reasonably useful for analysis. This would not be the same as analyzing the full securities lending market; it would however give a head start to market participants and regulators looking for consistent patterns in at least a subset of lending activity.
The next challenge is to clean up a standardized data feed. This process has a long history in the equities markets; there is a large industry that does nothing but clean the New York Stock Exchange’s Trade and Quote (TAQ) database. Some traders decide to use the dirty data with bad ticks to capture all market noise while others want the filtered version. Likewise, when real-time data comes through, it may also be unfiltered or cleaned. Securities lending data may go through the same transformation. The end result may or may not be as useful as a cleaned TAQ database however; it all depends on the volume of loans that can be considered standardized in any one trading set.
The potential for intraday securities lending data to be transformed into a ticker is enticing to regulators, particularly legislators who are new to the securities lending arena. This is an interesting path and one that is almost certain to lead to errors and confusion without product standardization. With a standardized and cleaned up feed however, a new opportunity emerges for a wide range of market participants.