Article originally published in “Market Technician” No 84 – March 2018
William Delbert Gann (1878-1955) was a successful stocks and commodities trader. He also wrote seven books and numerous short courses on how to trade. Gann’s success as a trader was based on his forecasting method which he discovered from his research between 1902 and 1908.
This paper examines the application of Gann’s forecasting method to the currency markets. The evidence suggests that Gann never traded currencies. I examine the reason for this and why his forecasting method can nonetheless be applied to the currency markets today, and also look at the potential practical problems.
Why Did Gann Not Trade The Currency Markets?
Gann does not mention currency trading in his books and courses, nor in his various advisory services or in his personal trading records. Thus it is reasonable to assume that he never traded them. To identify the reason for this, it is first necessary to examine his forecasting method.
Cycles of time form the basis of Gann’s forecasting method (Smithson 2016) and hence he referred to cycles throughout his writings, including:
“Time is the most important factor in determining market movements because the future is a repetition of the past and each market movement is working out time in relation to some previous time cycle” (Gann 1946, p4) and
“My experience has taught me that nothing can stop the trend as long as the time cycle shows up-trend. Nothing can stop its decline as long as the time cycle shows down. Stocks can and do go up on bad news and go down on good news” (Gann 1949, p3).
He summarised his forecasting method in the following statement:
“In making my calculations on the stock market, or any future event, I get the past history and find out what cycle we are in and then predict the curve for the future, which is a repetition of past market movements” (Gann 1927, p76).
Thus Gann’s forecasting method essentially has three stages:
1) Acquire a detailed price history of the financial instrument;
2) Analyse that price history to identify the underlying cycles driving the financial instrument; and
3) Forecast the future prices of that financial instrument from the future progression of those underlying cycles.
Therefore a necessary condition for the application of Gann’s forecasting method is that a financial instrument’s prices should fluctuate freely in accordance with their underlying cycles; both historically (i.e. during the period of the price history) and into the future.
However, the evidence suggests that between 1908 (when Gann completed the discovery of his forecasting method) and 1955 (when he died) this condition was not generally present in the currency markets. To be specific, the major currencies typically did not fluctuate freely.
A detailed history of currencies from 1908 to 1955 is beyond the scope of this paper; but here is a summary of the exchange-rate regimes of the leading industrial countries during that period which indicates the constraints and controls on currency prices:
1908-14: Fixed-exchange-rate gold standard
1914-18: First World War: gold standard suspended and capital controls
1919-27: Interwar managed-floating-exchange-rate period
1928-31: The major currencies returned to gold standard by 1928 and from 1931 started leaving gold standard in response to the Great Depression
1931-39: Interwar managed-floating-exchange-rate period
1939-45: Second World War
1945-55: Bretton Woods system of fixed exchange-rates
In summary, it would appear that Gann never traded currencies because, from 1908-55, the major currencies were typically prevented from fluctuating freely; which nullified his forecasting method.
Application Of Gann’s Forecasting Method To The Currency Markets Today
It is more than 60 years since Gann died and the Bretton Woods system of fixed exchange-rates that was in force for the last decade of his life eventually collapsed in 1971-73. Today the major currencies (viz. Australian dollar, British pound, Canadian dollar, euro, Japanese yen, Swiss franc and U.S. dollar) ostensibly float freely. They are monitored by their respective central banks, all of which are mandated to maintain economic stability and hence can intervene in currency markets when they deem necessary. For example, the Swiss central bank pegged the Swiss franc to the euro between September 2011 and January 2015.
The key question, which I shall now investigate, is: Can Gann’s forecasting method be applied to the currency markets today?
As stated above, the first stage is to acquire a detailed price history of the financial instrument. I decided to examine first the U.S. dollar and therefore obtained a series of daily price charts (open/high/low/close bar charts with arithmetic price scale) of the U.S. dollar index (i.e. measured against a trade-weighted basket of currencies) from 1988 to 2017. I selected a 30-year period because this is long enough to include a range of market conditions. Finally, I selected a starting-date of January 1 1988 because that was 15 years after the end of the Bretton Woods system of fixed exchange-rates. I hoped that by this time the U.S. dollar and the other major currencies would have been fluctuating freely in accordance with their underlying cycles.
As stated above, the second stage is to analyse the price history to identify the underlying cycles. I found it quite straightforward to identify the set of cycles driving the U.S. dollar and therefore I concluded that Gann’s forecasting method can be applied to the currency markets today.
Unfortunately my conclusion was premature. More specifically, when I then applied this same methodology to the other major currencies (as stated above, the Australian dollar, British pound, Canadian dollar, euro, Japanese yen and Swiss franc) I was unable to identify clearly any underlying cycles; whether I analysed a currency index or the various currency pairs.
My initial thought was that I may have been mistaken in my analysis of the U.S. dollar, that proponents of the Efficient Markets Hypothesis were correct after all and that market prices are essentially random, making looking for underlying cycles in a price history a naïve and futile activity. However, I rapidly came to my senses, remembering that I had already successfully applied Gann’s forecasting method to a range of stocks and commodities.
I therefore hypothesised that currencies present a specific problem: individual stocks and commodities are essentially single financial instruments that are measured in a currency that is relatively stable compared with the stock or commodity. In contrast, a currency pair is essentially two financial instruments fluctuating simultaneously in accordance with their underlying cycles and therefore it is difficult to identify those cycles.
I then hypothesised that I had been able to identify the underlying cycles of the U.S. dollar from its index price history because its cycles dominate those of the basket of currencies it is measured against. In contrast, the underlying cycles of the other major currencies are relatively evenly-balanced. Therefore the cycles driving a particular currency are obscured, whether one is examining a currency index or currency pair.
I therefore concluded that in order to identify the underlying cycles driving a particular currency it is necessary to measure that currency in terms of a second currency, one that is relatively inert. Consequently, I set out to find a currency issued by a country both economically and politically very stable and with few natural resources (e.g. mineral or energy assets, whose cycles may influence its currency). The New Zealand dollar appeared to be a suitable currency.
I then re-examined the major currencies, employing the New Zealand dollar as the reference currency. In each case the underlying cycles driving that currency could finally be identified. I then re-examined the U.S. dollar, but measured in New Zealand dollars, and confirmed my earlier findings regarding its underlying cycles.
I then examined a selection of minor currencies, the Brazilian real, Indian rupee, Mexican peso, Norwegian krone, Russian rouble, South African rand, South Korean won, Swedish krona and Turkish lira. Once again, I employed the New Zealand dollar as the reference currency and, in each case, the underlying cycles driving that currency could be identified.
I excluded some currencies from this analysis because they are linked to others, once again nullifying Gann’s forecasting method. These were the Chinese yuan (partially-pegged to a basket of currencies), Danish krone (pegged to the euro), Hong Kong dollar (allowed to trade in a range linked to the U.S. dollar) and Singapore dollar (stabilised against a concealed basket of currencies).
In summary, my research encompassed the 21 most actively-traded currencies by value, which constitute approximately 97% of foreign exchange turnover (BIS 2016). More specifically, the underlying cycles of 16 currencies were identified. Four were excluded because they are linked to other currencies and it was not possible to identify the underlying cycles of the New Zealand dollar due to the apparent absence of a more stable reference currency.
I then concluded that Gann’s forecasting method can be applied to the currency markets today. However, in order to identify the underlying cycles driving a particular currency it is necessary to analyse the price history of that currency measured in New Zealand dollars.
At this point I should apologise for not providing a detailed exposition on how to identify the underlying cycles driving a currency from a price history of that currency. I am however continuing a practice started by Gann himself: “Mr. Gann has refused to disclose his method at any price” (Wyckoff 1909, p55) and “It is not my aim to explain the cause of cycles” (Gann 1927, p78).
Potential Problems In the Practical Application of Gann’s Forecasting Method To The Currency Markets
i) Identifying the underlying cycles that drive a currency.
As discussed above, currencies present a particular problem because a currency pair is essentially two financial instruments fluctuating simultaneously in accordance with their underlying cycles and therefore it is difficult to identify the cycles driving each component.
The solution, when analysing a particular currency, is to employ the New Zealand dollar as the reference currency. This is an effective solution because the currency is relatively inert, apparently reflecting the country’s stability and small economy (New Zealand’s gross domestic product was ranked fifty-third in the world by the IMF in 2016).
ii) A currency ceases to act in accordance with its underlying cycles.
Gann’s forecasting method is nullified when a financial instrument ceases to act in accordance with its underlying cycles. The major cause of this problem in currencies is central bank intervention. The solution is to avoid such currencies, in the same way that Gann recommended we avoid stocks acting similarly:
“The kind of stocks to trade in are those that are active and those that follow the rules and a definite trend. There are always queer-acting stocks and some stocks that don’t follow the rules. These stocks should be left alone” (Gann 1936, p34).
iii) A currency’s low sensitivity to its underlying cycles.
Under Gann’s forecasting method, the price of a financial instrument is driven by cycles. One ramification is that the larger the amount of the financial instrument that has been issued, the stronger the cycles must be to drive it:
“Do not expect General Motors to have a big advance because Studebaker has already advanced…Also consider that it has a capital stock of fifty million shares, while Studebaker has only 750,000 shares…It requires a much larger buying power to move a stock with several million shares than it does one with only 750,000” (Gann 1923, p104).
The currency markets are some of the largest financial markets in the world. Therefore they are particularly susceptible, displaying a low sensitivity to their underlying cycles and only the strongest cycles produce major moves.
One solution is to trade the so-called cryptocurrencies (e.g. Bitcoin or Ethereum) as these usually have a price history from which their underlying cycles may be identified and used to forecast future price movements, and the amount of issued currency is apparently strictly controlled. Unfortunately there would also appear to be a grave risk that in future these currencies will not act in accordance with their underlying cycles due to such factors as fraud, hacking, network and infrastructure failure and regulatory intervention.
iv) A breakdown in the geometry of currency markets.
Although cycles are the basis of Gann’s forecasting method, he also discovered that market prices typically unfold in a coherent way in response to these cycles and hence there is a geometry to the stock and commodity markets. For example, changes in the rate of vibration (as measured by the slope of the trend line in prices) are not continuous but conform to a series of principal energy levels and subshells (Smithson op. cit.).
Consequently, in his forecasting of the stock and commodity markets, Gann analysed the underlying cycles and the resultant market geometry. However, in the currency markets this geometry typically breaks down because of the simultaneous price fluctuations of each component of a currency pair.
Although the New Zealand dollar is sufficiently stable as a reference currency to enable the identification of the underlying cycles driving a particular currency, it is usually not sufficiently stable to enable the use of market geometry as a supplementary forecasting method. Therefore, unlike stock and commodity markets, when analyzing currency markets, we can only rely on the underlying cycles.
The Efficient Markets Hypothesis is currently the dominant paradigm in the field of investment. In summary, it postulates that price movements are essentially random in highly-developed or “efficient” markets.
W. D. Gann’s forecasting method is the direct antithesis of the Efficient Markets Hypothesis. In summary, it postulates that the price history of a financial instrument can be analysed to identify the underlying cycles driving the financial instrument; these can then be used to forecast future prices.
A necessary condition for the application of Gann’s forecasting method is that a financial instrument should fluctuate freely in accordance with its underlying cycles; both historically (i.e. during the period of the price history) and into the future.
This condition was not generally present in the currency markets during Gann’s lifetime and therefore he never traded currencies. However, it is generally present in the currency markets today and therefore his forecasting method can now be applied. The exception are pegged currencies, which do not fluctuate freely.
A key problem in application is identifying the cycles driving a particular currency. The solution is to employ the New Zealand dollar as the reference currency. Another problem is when a currency ceases for a period of time to act in accordance with its underlying cycles, the usual cause being central bank intervention. A third problem is a currency’s low sensitivity to its underlying cycles, the usual cause of which is the very large amount of currency that has been issued. A final problem is the breakdown in market geometry, the usual causes of which are the simultaneous price fluctuations of each component of a currency pair.
Since Gann’s forecasting method can be applied to any financial instrument that fluctuates freely in accordance with its underlying cycles, and where a detailed price history is available to enable identification of these cycles, the investment universe today essentially includes all stocks, all commodities and all currencies that are not pegged to other currencies.