We’ve been hearing about the importance of Big Data for what seems like decades. But often, investment banks approach data projects the wrong way. The focus is more on getting ultra-fast market data than new data types like transactional, referential and social data. Budgets can also be a stumbling block, with discretionary funds going toward the overwhelming demands of complying with regulations such as Dodd-Frank and Volcker Rule.
But not prioritizing data projects is a strategic mistake. Here are three reasons why.
- Data volumes continue to grow exponentially.
After sifting through near-unmanageable volumes of data, investment banks and asset managers are now challenged to discover how to use all this data and, more importantly, how to find meaningful patterns in that data that deliver insights others don’t have.
- Competitive insight can yield giant returns.
In the trading business—possibly the most competitive of all—it’s all about a firm’s ability to differentiate itself using data. We all know that the trading business has evolved (some say devolved, but I’ll refrain from that debate for now) into one where technology trumps human intelligence and quantitative process overrules fundamentals. In some ways, technology levels the playing field among the large firms, each investing hundreds of millions in trading technology a year simply to keep pace.
So if trading firms have similar technology and use the same data, the tide will rise and all firms will rise with it at the same rate. In trading—a zero sum game—that doesn’t satisfy. It’s relative growth that matters, leading one firm to win more frequently than its competitors. The winners are those that have the best inputs into their technology. Finding unique data sets—either raw or derived—is the winning formula.
- Access to unique data enables well-informed investing.
What am I talking about here? Reference data that offers insightful information about companies that form the core of our investments—equities or fixed income securities—is moving to the front of the line for investment models.
Reference data can often identify a leading indicator for an investment position. For instance, having insight into the payment history for a private issuer of bonds can enable a trader to assess its price more precisely than other bidders. It’s about getting an early alert about glitches in a manufacturer’s supply chain that can dampen future earnings projections and trigger a ‘sell’ signal on its stock—before the rest of the market figures it out.
We know the data used by most trading firms is indeed quite similar. However, all traders remain on the lookout for data that they can deploy uniquely to separate from the pack. Right now, the differentiating factor for many of these firms is in the models used by each to derive new data. That is the secret sauce they count on to achieve alpha returns.
Image credit: Anthony Quintano