Currency Trading in the FX market : Will spectral analysis improve technical forecasting?

University essay from Högskolan i Jönköping/Högskolan i Jönköping/IHH, Redovisning och finansieringIHH, Företagsekonomi; Högskolan i Jönköping/Högskolan i Jönköping/IHH, Redovisning och finansieringIHH, Företagsekonomi; Högskolan i Jönköping/Högskolan i Jönköping/IHH, Redovisn



The efficient market hypothesis asserts that one cannot consistently achieve returns in excess of market returns by trading on publicly available information. Since there is no collective market return in the foreign exchange (FX) market, it has generally been perceived as impossible to consistently generate a profit. There is now empirical evidence which seriously call into question the efficiency of the FX market and opens up the possibility to turn a profit on the FX market by ways of analysis.Technical analysis is a method of analysis which by using historical price data tries to deduce future price changes. Technical analysis assumes that financial markets move in sine waves. There are stronger and weaker sine waves simultaneously. An accurate identification of the dominant sine wave gives the investor a good idea about future movement. Most technical trading tools approximate the length of the sine wave by default. This static approach does not consider the specific market or the recent lengths of the dominant sine wave. Spectral analysis will help to identify the dominant cycle, and thus determine the frequency of that cycle making the applied trading rules adaptive to the market.


The purpose is to investigate whether adding spectral analysis to existing technical analysis tools can create a higher and more stable return on investment on the FX market.


An experiment involving four different sets of trading rules was conducted to answer the purpose. In the first test, trades were performed based on a static approach commonly used by technical traders today. In the other three tests different transforms of spectral analysis were applied, thus making the input not static, but adaptive to the market. The four sets of trading rules where coded as an automatic trading algorithm and backtested on data collected for the currency-pair EURGBP during an 11-month period. All four tests were analysed in three different areas; performance, stability of return and crash risk.


The study shows that the application of spectral analysis to technical analysis methods on the FX market results in higher return on investment and better stability of returns. The win/lose ratio is significantly higher and the adaptive approach increases profit as well as decreases losses.

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