Bouman and Jacobsen called it the Halloween Indicator, attempting to drill down into the psychological and logistical complexities behind the ‘Sell in May and Go Away’ adage in their paper on the subject. They described the May-October period as seasonally down in some 36 out of 37 developed/emerging markets studied, with European markets showing the strongest adherence.

Indeed, by DJIA measure stocks have on the average climbed 7.4% in the Nov. 1- Apr. 30 period since the 1950’s, mapping only 0.4% for the following May-Oct. period. Moreover, some of the worst six-month intervals have occurred during this period and the May 2008 period (off by 27.3%) is still fresh in investors’ minds, with the 2001-2002 periods (down some 16%) close behind as examples of good calls to sell in May and go away.

While the 2011 S&P 500 data looks like its tracking the 2004 curve, which was a good season to sell in May and go away, indices for US markets are bullish and historically speaking, the seasonally negative effects are generally neutralized by easing from the Fed. With the Fed pushing QE2 into June 2011 there is ample room for an agile portfolio to grab some under-realized dividends (largely because as Bouman and Jacobsen observed “in most countries dividend payments occur mainly during the May through October period”) in sectors and individual companies that have solid momentum.

Looking at the global economy and the pace of markets, it is clear that there is huge upside in key areas like emerging health sciences and pharmaceuticals, commodity developers (with precious metals, energy and other raw agricultural or material inputs leading the way) and new technology. A variety of service areas also have great potential due to stable year-round revenue models, meaning that even if the next several months are down months, a shrewd investor can find footholds.

A particularly intriguing example of modern IT ubiquity driving new trend lines that may also disrupt the dominance of the sell in May adage is the growing prevalence of data-mining Twitter to read the market in real-time. This is a natural extension of the same push behind algorithmic trading and other implementations of predictive theory like the Web Bot Project, which leverages massive amounts of high fidelity user generated data to pull trend lines right out of the infospace.

As usage of Facebook and Twitter, in addition to other, similar technologies increases, the strength of linguistics analysis (like the Asymmetric Language Trend Analysis technique employed in Web Bot) combines with other analysis techniques to produce a real-time model of the market’s psychological dynamics.

Professor of Informatics at Indiana University, Johan Bollen, recently called the emergence of such technologies an exciting time for information science and described the availability of this massive pool of high-value, high fidelity data as a veritable “gold rush”.

Bollen published a paper on his studies of using Twitter data for evaluating DJIA stock prices wherein he claims an astonishing 87% accuracy rate.