Here’s the system:
At the end of every month,
And that’s it.
So, if we take the FTSE 100 Index as an example, if at the end of a month the FTSE 100 is above its 10-month simple moving average then either,
Conversely, if at the end of a month the FTSE 100 is below its 10-month simple moving average then the portfolio sells the ETFs and moves 100% to cash; if it is already in cash then nothing is done.[NB. OK, it’s possible that this isn’t absolutely the simplest trading system imaginable, but apart from buy and hold it is unlikely there are many systems much simpler than this one!]
The following chart illustrates such a portfolio for the FTSE 100 Index since 1995. The diamond markers indicate the decisions made at the end of each month whether to be in the market (green diamond) or in cash (red diamond).
Roughly, one can see that the system kept the portfolio in the market in uptrends and out of the market (in cash) when the market fell.
This trading system is well-known in the US, what we will look at here is:
Terminology: we will use SMATS(10) to refer to the 10-month simple moving average trading system. And SMATS(5) for the trading system using the 5-month simple moving average etc. Below we will analyse the trading system for 14 different parameters of the simple moving average, i.e. from SMATS(4) to SMATS(16).
First, let’s look at the overall profitability of SMATS.
The following chart plots the values of the SMATS portfolios for the 14 different simple moving averages (i.e. 4-month to 16-month). As a benchmark the FTSE 100 is added (i.e. this is the value of a buy and hold FTSE 100 portfolio). All values were re-based to start at 100.
The following chart summarizes the final portfolio values in 2016 after running the trading system from 1995.
By 2016 the STATS(10) portfolio had the highest value of all portfolios at 269; the FTSE 100 buy and hold portfolio a value of 1999.
We’ve looked at profitability, let’s now consider the risk incurred by each portfolio. We’ll use volatility as a (fairly standard) proxy for risk.
The following chart shows the volatility of the portfolios over the 20-year period.
Not surprisingly the FTSE 100 had the highest volatility. The volatility of the SMATS portfolios was less due to the fact they were in cash for part of the time; broadly their volatility increased as the moving average month parameter increased.
The Sharpe Ratio combines returns with volatility to provide a comparative measure of profitability per unit of risk incurred. The ratio’s purpose is to answer questions of the form: is the profitability of a strategy justified by the risk incurred, compared to another strategy?
The following chart plots the Sharpe Ratio for the 14 portfolios. (The benchmark for the Sharpe Ratio calculation was the FTSE 100 Index.)
SMATS(10) had the highest (i.e. the best) Sharpe Ratio, although close behind were SMATS(14) and SMATS(15).
Maximum Drawdown decribes the maximum loss a portfolio suffered from a previous high value. For example, in this test SMATS(10) had a max drawdown value of 22.8%. This means that over the 20-year test period the portfolio was at most 22.8% under water (from a previous high).
Frankly, max drawdown has more significance for strategies that employ leveraged products (e.g. futures), as drawdowns incur realised losses as margins have to be paid. By contrast in the case of unleveraged equities or ETFs, drawdowns incur unrealised losses. Having said that, unrealised losses can still be uncomfortable and can have a major adverse psychological impact on the investor or trader.
The following chart shows the max drawdown values for the 14 SMATS portfolios and the FTSE 100 Index.
Here the SMATS(10) portfolio only had a middling relative score. The best portfolios (i.e. those with the lowest max drawdowns) were: SMATS(7), SMATS(14), SMATS(15), and SMATS(16).
The following chart shows the average number of trades for the year for each portfolio. For example, over the 20-year test period SMATS(10) portfolio traded 36 times, which is an average of 1.7 times a year.
As would be expected the number of trades decreases as the length of the moving average month parameter increases. In other words, systems get whipsawed less with longer moving averages.
The profitability figures above did not include transaction costs, but with the systems averaging under 2 trades per year the transaction costs would not be significant.
The following table summarises the above analysis. The values are colour-coded with green being the best value through to red being the worst for each respective analysis.
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