Abstract
The objective of this research is to examine the efficiency of EUR/USD market through the application of a trading system. The system uses a genetic algorithm based on technical analysis indicators such as Exponential Moving Average (EMA), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI) and Filter that gives buying and selling recommendations to investors. The algorithm optimizes the strategies by dynamically searching for parameters that improve profitability in the training period. The best sets of rules are then applied on the testing period. The results show inconsistency in finding a set of trading rules that performs well in both periods. Strategies that achieve very good returns in the training period show difficulty in returning positive results in the testing period, this being consistent with the efficient market hypothesis (EMH).
Highlights
This paper describes a genetic algorithm used to create a trading system, consisting of several rules for opening and closing trading positions in the FX market
Our paper shows that the distribution of the outcome in the out-ofsample period is uniformly distributed around an average close to 0. This provides evidence that all the information available in the EUR/USD market is reflected in the price and no arbitrage can be made by trading this currency pair based on historical information
We firstly discuss the evolution of EUR/ USD in the training and testing period (Fig. 1)
Summary
This paper describes a genetic algorithm used to create a trading system, consisting of several rules for opening and closing trading positions in the FX market. The aim of this study is to assess the weak form efficiency of the EUR/USD market. Our paper shows that the distribution of the outcome in the out-ofsample period is uniformly distributed around an average close to 0. This provides evidence that all the information available in the EUR/USD market is reflected in the price and no arbitrage can be made by trading this currency pair based on historical information. Our findings should capture the attention of investors in the FX market that base their decisions on technical analysis signals. The results are in the support of previous academic literature that in general provides evidence for the impossibility of forecasting financial market movements by only analyzing historical prices
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