3 APR 2009, Friday

1. Classic cartoon from 1930's seems eerily similar to current times.
2. Economic downturn speeding Social Security's demise. HEY!!! CON-gress and O-Bomb-A, want to tackle a crisis that's provable (ie, I can clearly prove that people get older), ...TACKLE THE TICKING TIME BOMBS CALLED SOCIAL SECURITY AND MEDI-SCARE NOW! And quit diverting limited resources (money, time and effort) toward a theory such as global warming.

Monthly Seasonality

First, if you look at the data from this millennium (since 2000), April is the leading average % return for the S&P 500 Index at +1.46% average gain (see the below table). This is far stronger than any other month in that time frame -- and the second strongest month is May at +0.88%. So the Spring has been an outperforming season in general. The months that had the highest chance of being positive were August, followed by a May/October/November/December tie, so April has not been the highest in terms of a positive return (it is 55%).




That is a fairly small sample size, so we looked back all the way to 1950 on the S&P 500 Index data. Note on the chart below that April is the 3rd strongest month, with an average return of 1.37% and a 67.8% chance of being positive. November and December, which are commonly discussed as historically "strong" months for the market, are the biggest gainers on average. Data compiled from Yahoo Finance and excerpts taken from BigTrends.com.

There are many market axioms concerning seasonality and months, such as "Sell in May and go away", "Up January equals Up Year", "Crashes occur in September/October/November", "Summers are slow and bad for technology stocks" etc. Some of these expressions are proven true while others may be violated in any given year. The data above indicates that "April Showers brings Bullish Flowers" may become a future cliche, albeit a tongue-in-cheek one. Of course nothing is guaranteed (for example none of the months are up more than 75% of the time since 1950), but it's always good to have some historical statistical data in your favor when analyzing the market and risk/reward ratios.