Opening Cross: You Can Teach an Old Dog New Tricks... But New Tricks Still Need Old Dogs


It takes a certain blend of skills to combine the experience that comes with age and the dynamism of youth. For eternity, mankind has tried both to put old heads on young shoulders, and find ways to teach old dogs new tricks and recapture the energy and alertness of years gone by. Just as this is true for everyone from the vainest Hollywood celebrity to the scientist searching for the secrets of life with just one lifetime in which to find it, it is also true for the financial markets.

That's the reason why relatively young companies like fund flows data vendor EPFR Global (now celebrating 15 years) and data search and discovery engine provider AlphaSense (now six years old) find value in hiring staff with a longer track record in the business. For example, EPFR recently hired Larry Joyce─a veteran of Thomson Reuters, AOL Relegence and Dow Jones─as sales director, while AlphaSense has appointed Steven Carroll (also a veteran of Thomson Reuters and Starmine, among others) as vice president of sales for its EMEA business as the young company continues a growth spurt.

Of course, even 15 years is a short time compared to Thomson Reuters, which has its roots in the company Paul Julius Reuter founded in 1851. Even Nasdaq OMX's Mutual Fund Quotation Service─which Nasdaq officials remind us is now celebrating 30 years, during which time it has grown to tracking 31,000 instruments─is young by comparison, while Inside Market Data itself won't even reach 30 until next year, though our parent company, Incisive Media, also owns insurance industry magazine Post, which was first published in 1840.

Similarly, Chicago-based data and analytics vendor Barchart was originally founded in 1980 under the moniker Logical Systems, though its heritage extends back 80 years as a result of its purchase of the Commodity Research Bureau, which was set up in 1934. In fact, Barchart has just made its CRB Commodity Yearbook─a 75-year record of reports on the commodities and futures markets─available via Mergent's archive of corporate and industry documents for analysis and forecasting by academics and researchers.

Analyzing historical data is the next best thing to actually having experienced the period in question, and some might even say better, since the data itself is without bias, whereas individuals always─however unintentionally─impart some level of subjectivity. Hence, trading algorithms attempt to distil the years of experience of multiple trading experts into a computer program with boundless prowess, while technical analysis programs help users spot myriad trends and predict entry and exit points without actually having to have experienced those trends first-hand before to know what to look for.

Meanwhile, the perfect storm created by the "institutionalization" of consumer technologies and the trickle-down of institutional content and tools to retail investors has led to a mixture of crowdsourcing and "game-ification." While we've heard the term "game-ification" bandied about in recent years to mean new interfaces that more closely resemble gaming environments, one of the most intriguing ways that some providers are creating datasets that put a wealth of experience at anyone's fingertips is by capturing data from trading games and using that as a measure of investor sentiment or an indicator of potential price movements.

The most recent case in point is Invstr, a finance-focused social network created by a former senior trading exec at Deutsche Bank that encourages users to pit their wits against the markets in specific strategies (or "games"), from which the vendor creates an overall signal for crowd-sourced sentiment. Other "players" in the trading game-turned-data-source field include London-based StockViews and Singapore-based Nous, which have both already rolled out broadly similar products targeting this space.

So while it's hard to put an old head on young shoulders, new datasets and technologies are actually making it easier. I'm not yet sure whether this trend is a good or bad thing, but of one thing I am certain: it will place greater emphasis on the value and quality of your data, which will increasingly become the differentiating factor.

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