I originally wrote a piece on my sun/oracle blog called “Are “Quants” suitable for grid infrastructure?” I used a finance dictionary to create a dichotomy between Quants & Chartists and suggested that new IT architectures were needed to deal with trends and forecasting, especially if applied to portfolios and baskets. I reproduce the words here, as I was clearly struggling towards a forecast about big data and new application’s and implementation architectures. The original is not tagged “big data” as it wasn’t a current term at that time.
At investorwords, a finance dictionary site, they define a quant as “One who performs quantitative analyses”. Not so useful; while I have known people bet on horses because they like the name, I have not really heard of people investing in the stock market using this strategy, so evaluating the value of stock using numbers seems pretty basic, and in their eyes a Quant is a person. They define quantitative analyses as “The process of determining the value of a security by examining its numerical, measurable characteristics such as revenues, earnings, margins, and market share.” If measuring the value of one stock, it is unlikely that, parallel algorithms are necessary. The data points are too few to warrant applying grid or parallel programming techniques. If analysing a portfolio of many stocks using these techniques, or a whole bank portfolio, then grids become more useful. It really depends upon the size of the portfolio, and the required response time.
A chartist on the other hand, performs “Technical Analyses“, which investorwords, defines as “A method of evaluating securities by relying on the assumption that market data, such as charts of price, volume, and open interest, can help predict future (usually short-term) market trends.” Presumably the name chartist, comes from the fact that they use graph representations of the data series they analyse. However, the analysis of history increases the number of data points involved, and when one tries to apply technical analysis to portfolios, the problem of scale and the attributes amenable to parallelism come into the frame. The use of Grid software and hardware architectures probably becomes more useful earlier.
Neither of these techniques however are truly applications; they can be used to support trading decisions, risk evaluation, capital adequacy calculations, and pretty much any decision which involves evaluating today’s and tomorrow’s value of a financial instrument, be it treasury, equity or derivative.