Exchange Rates: 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. For currencies
this means capital appreciation only.
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.
Owing to the conversion of legacy currencies
into Euros, analysis is provided on the common
currency, rather than for individual countries.
Historical data is provided by creating
synthetic GDP-weighted time-series for the
component currencies, expressed in the European
Currency Unit.
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. 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