Opening Cross: Forza Monza! Forza Market Data!

Most pople compare market data to Formula One because of speed. But that's just one similarity between the two.

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Taking place this weekend—or having already taken place, depending on when you read this—is the Italian Formula One Grand Prix at the fabled Autodromo Nazionale Monza circuit, one of the highest-speed tracks on the F1 calendar. F1 fans worldwide—but especially local favorite Ferrari’s rabid fan base, known as Tifosi—love the circuit not only for the current races it hosts, but also for its history. Now a series of long straights, high-speed curves and 90-degree bends, the track was originally an oval circuit, which might have been boring but for the enormous banked sections of track (featured in the 1966 movie Grand Prix) that still stand, unused beside the present-day circuit.

Among the F1 community, there’s a huge sense of nostalgia for that old, beloved—albeit outdated and dangerous—circuit, with many who remember those times harking back to the “golden age” of F1.

And likewise, as the market data industry plows ahead at F1-like speeds, there are some who talk whimsically about “the old days” before direct exchange feeds, non-display fees and ever-increasing compliance burdens around data usage—back when the small print was short and the profits (and parties) were big.

However, just like the old Monza track, those days are gone. And while the basic tenets of market data remain—just as the racecars still have four wheels (excluding the 1976 Tyrrell P34 six-wheeler), an engine, a gearbox, and a steering wheel, yet the body and engine designs and manufacturing materials used have changed radically—the ways in which data can be derived from new sources and in new formats is also dramatically different from just a decade ago.

Vendors like Mosaic Smart Data, which predicts client trading behavior, and Social Alpha, which provides analytics of social media activity that can be used to predict stock price movements, increasingly have a role to play in adding another dimension to existing datasets.

In some cases—especially for compliance and risk purposes—that extra dimension is to be found not only in the content, but in the delivery and communication channels that carry it, such as instant messaging, which San Francisco-based Recommind now supports in its Axcelerate visual analytics platform, allowing users to monitor traders’ communications for unusual activity or patterns that might suggest any nefarious behavior.

And others are expanding these new tools into new areas. Thomson Reuters is adding support for Japanese-language news to the latest version of its News Analytics (TRNA) service. Since plenty of vendors now perform sentiment scoring on the plethora of English-language news services commonly used by trading and investment professionals, large portions of this have become commoditized, and that there is hidden value to be found in local-language news that has the potential to move market but doesn’t immediately make it into the mainstream English news feeds. Thus, in countries or regions where English is not the primary language of capital markets, tools that might be subject to more competition elsewhere may still deliver a big edge for traders.

The other factor beyond language is access to free and transparent market news. So the Chinese market may also be a good candidate for services like TRNA—though the availability of uncensored local news may prove a challenge to obtaining genuine market-moving information. However, there is still demand for access to Chinese data, in part because of the country’s impact on other markets, such as commodities data provider Platts’ new China Oil Analytics service, which helps traders, energy producers and analysts to predict changes in domestic Chinese supply and demand, which influences the global oil and petroleum markets.

And speaking of oil and petrol, I’m off to get ready for (hopefully) an exciting Italian Grand Prix. If you’re not already a fan, I encourage you to give it a try: the level of in-race data analysis around F1 cars is truly impressive, and any fan of data should be a fan of F1. 

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