There are several purposes of this article series on investment strategies
- The strategies by themselves provide investment insight.
- It serves to add some color to my (new at Seeking Alpha) profile and general investment approach.
- My various strategies will be referred to from time to time in weekly commentaries.
For the last 15 years or so, I have written weekly investment commentary for my own use, guiding me for the coming week. These weekly commentaries I now intend to bring to Seeking Alpha. Without proper background and accompanying deeper analysis, these weekly commentaries could be interpreted as too weak by themselves. Therefore, the Strategy articles should serve as background material; explain that there is more analysis behind the commentaries than first meets the eye.
After having read this article you hopefully see that there is a quantitative aspect to my analysis. Still, my own final investment strategy has not been possible to fully quantify. I mix many inputs; classic technical analysis, sentiment analysis, volatility measures, calendar strategies, business model innovations to the outcome of U.S. elections...
We are approaching month end. You will likely hear someone, somewhere throwing out the "month end effect" to support some general thesis. What about the month end effect? I started looking at the month end effect 13 years ago. During this time, the performance has weakened somewhat. Nowadays I mostly use another monthly calendar effect, that I will cover in another article. I will not fill this space with what you can already find in other articles, so simply google "month end effect" or "turn of the month effect" as some academicals refer to.
Several academical research papers have studied the phenomena of MEE:
Ariel (1987) showed that for the period 1963 to 1981 the average returns for common stocks on the NYSE and AMEX are positive only for the last trading day of the month and for the trading days during the first half of the month.
Lakonishok (1988) studied 90-years of data and concluded:
This study uses 90 years of daily data on the Dow Jones Industrial Average to test for the existence of persistent seasonal patterns in the rates of return. Methodological issues regarding seasonality tests are considered. We find evidence of persistently anomalous returns around the turn of the week, around the turn of the month, around the turn of the year, and around holidays.
Ziemba (1991) shows that Nikkei 225 has significantly higher returns between day -5 and day +2.
John J. McConnell and Wei Xu (2008) conclude that
The turn-of-the-month effect in U.S. equities is found to be so powerful in the 1926-2005 period that, on average, investors received no reward for bearing market risk except at turns of the month. The effect is not confined to small-capitalization or low-price stocks, to calendar year-ends or quarter-ends, or to the United States: This study finds that it occurs in 31 of the 35 countries examined.
They all conclude that the effect is not limited to only some months of the year, i.e. that it would be incorrectly identified as a year-end effect or quarterly end effect. It has also been concluded that the effect is not limited to a certain type of stocks (i.e. small cap vs large cap or growth vs value).
The primary reason presented to explain the month end effect has been the flow of money in to the market as a function of most of the western world paying salaries around the 25th to last day of month, which then goes in to monthly saving schemes of different kinds, including pensions (Ogden, 1990). This conclusion is enhanced by the fact that the study of Nikkei 225 had a stronger effect from already day -5, which is then explained by the fact that most Japanese salaries are paid on days 20 to 25. In the United States, most salaries tend to be paid on day -1 (Handbooks in Operations Research and Management Science, vol 9, R.A. Jarrow et al.)
John J. McConnell and Wei Xu argue in Equity Returns at the Turn of the Month that the effect is not caused by month-end buying pressure as measured by trading volume or net flows to equity funds. The author of this article (me) believes that overall volume is a too blunt measurement, it depends to a large degree who is contributing volume; long only vs a day trading robot for example.
It is worth noting that the research conducted in this article is effectively an out of sample test of these earlier studies.
Using data from year 2000, I decided to run some tests on two major indices; S&P 500 and German DAX Index. All returns are calculated using closing prices for the day. Example: The return for day 5 is calculated using the closing price of day 5 divided by the closing price for day 4. [(Day5/Day4)-1].
Disclosure: I/we have no positions in any stocks mentioned, but may initiate a long position in SPY over the next 72 hours.
I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.