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The purpose of this paper is to provide a plausible explanation for the “sell in May” anomaly observed in US stock markets. A heretofore unexplained strategy of selling stock in May and not returning to the market until November has been shown to outperform a simple strategy of buying and holding stock all year long.

The authors compare the seasonal performance of three US size-based portfolios for the May–October and November–April periods considering whether or not they were in years with US congressional elections, which occur every two years.

While the sell-in-May effect appears to persist in the long run, the authors find that the anomaly is not present in non-election years. There is no significant difference between the May–October and November–April stock returns in non-election years. The observed sell-in-May effect is driven by poor stock returns in the May–October periods leading up to US presidential or congressional elections and subsequent strong performance in the November–April periods immediately following elections.

The paper offers an election-year effect as an explanation of the sell-in-May anomaly that has been observed in the US stock market. Other possible explanations of the effect, such as seasonal affective disorder, the weather, and daylight savings time, have not gained widespread acceptance.

The purpose of this paper is to develop an algorithm to harvest user specified information on finance portals and compile it into machine‐readable datasets for quantitative analysis.

The Visual Basic macro language in Microsoft Excel is applied to develop code that is not constrained by the single‐query function of Excel. The core of the algorithm is built around the splitting of the URL connector line and the placement of a continuously updating variable into which are looped as many tickers as there are in the input list. The output is then written to non‐overlapping cells.

Numerical information placed on major finance websites can be harvested into structured machine‐readable datasets by applying this algorithm.

One significant change in Microsoft Excel 2007 is that the worksheet is expanded from 224 to 234 cells, or to be more specific, from 256 (IV) columns × 65,536 rows (28 × 216) to 16,384 (XFD) × 1,048,576 (214 × 220). These new limits while allowing for a larger number of tickers, still constrain a single worksheet to 16,384 columns. For five fields per ticker that translates into roughly 3,200 ticker symbols.

The algorithm extends user accessibility to websites that do not provide the facility of simultaneous downloading of information on multiple stock tickers. Furthermore, the procedure automates the downloading of multiple pieces of information (fields) and entire tables per ticker (record).

An exhaustive literature search did not find any paper that discusses a multiple ticker algorithm for web harvesting.