By Tim Smith, Managing Director Business Development, Hazeltree
It wasn’t long ago that fund managers operated in the dark about trading costs; nor could they capitalize on the intrinsic strategic value of the metrics gleaned from the multiple data points.
Hedge funds would borrow securities based on past relationships or the size of available credit lines that they could secure with little consideration about securities lending rates, including what their peers were paying. It wasn’t that managers weren’t interested in this information, but there wasn’t exactly a LendingTree type of service that allowed for easy comparisons. Although imprecise data around rates was superficially available, the metrics from changing positions held, both long and short, and both physical and synthetic, were simply not attainable.
Today, however, the enhanced securities-data ecosystem is quickly taking shape, and those who know where to look are at a distinct advantage over those who don’t. Data that had previously been unavailable is increasingly seeing light for the first time and influencing both the investment and operational strategies of traders, treasury professionals and portfolio managers. More transparency has been driven in part by a series of trading regulations, but demand for data also has played a role, as investors increasingly focus on gross returns while managers themselves contend with fee compression amid the intensifying scrutiny. And an increasing desire for what’s new and unique has accelerated the analytic focus and, together with more sophisticated obfuscation logic protection, this has started to overcome the traditional asset manager reticence towards sharing data in order to obtain data.
At its simplest, but most obvious and immediately financially rewarding level, this type of data can help exposeoutliers in terms of lending rates and other formerly overlooked factors. But it also yields an array of portfolio insights that goes far beyond the costs involved to execute a given strategy. The most advanced users of securities finance data are leveraging all available intel to gain insights into potential impacts on their current positions.
As many discovered during the GameStop saga last year, it can behoove investors to understand the make-up of the investor base, be it long or short, to avoid getting blindsided. And data, such as borrowing fees, provide invaluable insights around the percentage of shorts that have covered and at which point they closed out their short positions. The data can reveal market movements and help guide strategies, particularly during periods of volatility. Furthermore, given the growing history of positional movements among the buyside community and the relative crowdedness of particular securities and the variations in the number of holders of short and long positions, numerous additional metrics are becoming available for the monitoring of not just the “hottest” of securities but also the “general collateral” or easy-to-borrow securities on a global basis.
Finding value in trading transparency
There are many variables that affect securities lending rates, including the size of the fund, the type of security, and the other services the prime broker or another lender may provide. These variables account for the fact that the rate dispersion is very wide in the buyside space, making it exceedingly difficult to provide a definitive rate. A large multi-prime hedge fund can command close to the rate being charged by the institutional lender to the broker — indeed, in some circumstance they are able to borrow directly themselves. However, the small funds have to pay the rate at the top end given their lack of buying power. Hindered by their credit risk profile, they are at the mercy of their prime brokers, although having data can somewhat level the playing field and allow for educated pushback arguments.
A new level of data intel — a robust set of securities lending data — allows firms to effectively shop around, avoiding overcharging, creating more economical relationships and enabling more profitable trades. For a larger hedge fund, which should enjoy economies of scale, this data can help them realize the advantages that should come with size. But for even smaller managers, relatively small basis-point improvements can add up to significantly enhanced profits. One firm’s operation group said the depth of enhanced securities data has turned his team into a “quasi-profit center.”
Securities data can be just as powerful when it helps to surface mispricing opportunities and latent risks. Consider, for instance, the extent to which long-short data can serve as an early warning system for funds, allowing fund managers to pick up important signals that otherwise might come too late.
A general trend among traders as it relates to securities data is a historical overreliance on prime brokers, with data often once or twice removed from its source, making it dated. Portfolio managers and quant analysts now can have access to a whole host of available unique metrics:
- Broker internalization: Insight into the captured long positions usage by the prime brokers is a key indicator of securities finance flow as yet untapped.
- Crowdedness on both a long and short basis: This can be broken down by market value and the number and proportional fund holder “buckets.”
- Conviction: Aggregate allocations to specific names across all funds.
- Physical versus synthetically held positions: This is particularly relevant in many global markets.
- Capital flow: Buy and sell activity and flow around all securities.
While this data may not seem as essential in a low-volatility market environment, as interest rates climb and the geopolitical backdrop becomes more uncertain, it will become much more critical to understand all the drivers that will influence the underlying market movements.
Long-short data has a way of anticipating the actual news, capturing traders’ intel and hunches ahead of announcements and providing statistical tipping points to take action. If some of the earlier indicators of movements between long and short holders had been noted, then many positions may have been closed, expanded or even re-negotiated throughout the tumultuous period and revenue earned or conserved.
Asset managers naturally are hungry for the fullest data to make the best decisions and to be able to react nimbly to changing conditions. A fundamental challenge is how to best cut through the noise; with enhanced lending and long-short data, important messages in securities finance come through clearly enabling consumers to predict, protect and profit.