Asset Allocation: Seasonal Trading Patterns

This strategy is designed to exploit short-term trading patterns, to assist long-term investors in fine-tuning entry and exit points. It uses end-month data to illustrate the average seasonal trading patterns in the past. A rise represents an increase in value. So that all asset classes tare compatible, this means total returns.
 
Each series shows the cumulative gain or loss as the year progresses. Thus, starting from zero at the beginning of the year, a 1% gain in January followed by a 2% gain in February show up as +3% for February. A fall of 1% during the next month would show up as +2% for March.
 
In order to identify possible changes in behaviour patterns over time, separate series are shown for short, medium and long-term averages. The time-frames selected are 8, 16 and 32 years to reflect respectively two, four and eight electoral cycles, as discussed in Investor Education > Conceptual Framework > Investment Cycles. The shorter the time-scale, the stronger is the line. This recognises that more recent experience has greater significance. Thus the white line is the short-term series, the light grey line is the medium-term series and the dark grey line is the long-term series.
 
By changing the Time Scale on the drop-down menu, one can see the difference between cumulative performance over successively longer time periods (Long Term option) and discrete performance in succeeding time periods (Medium Term option).
 
To indicate of the odds of making profits or losses in any month of the year, white percentage figures are attached to the longest time-scale. Thus, for example, 67% against a rising month shows that the changes of a profit in that month are 2 in 3. Equally 67% against a fall shows that the chances of a loss in that month are 2 in 3.
 
Please note that both bond and share charts show an upward bias throughout the year. This is because of the secular uptrend in global markets through most of the period, but will cease if markets enter a trading range in future.
 
While the database is as comprehensive as possible, it does not cover all situations. In some cases the longer-term series in individual charts may be missing and in other cases there may be no data at all. That may be because suitable long run data is not available, or because it has been excluded while countries experienced hyperinflation, as in some emerging markets. Markets for long term bonds have only been a recent innovation in many emerging markets.
 
Back-tested past performance of this strategy shows that this simple and predictable technique can be remarkably effective. That is confirmed by live performance since 2000. This timing indicator also performed well during the 2008 financial crisis.