# A Review on Recent Advancements in FOREX Currency Prediction
## 6 Brief Review of FOREX Price Prediction Techniques
### SVM Method  
[PERSON], [PERSON], and [PERSON] [58] proposed a model for FOREX market trading that is based on the combination of SVM and the genetic algorithm (GA). SVM classifies FOREX in three different stages while a dynamic genetic algorithm is applied for the trading rule optimization. They trained three variation GA for three different FOREX types using hyper-mutation and adaptability approaches. Then they classified the movements using a rate pattern and optimized the investment strategies using the suitable GA. They used the EUR/USD currency pair as training and evaluation data. They ran the SVM model 50 times; it was trained using 4000 samples. Their model classifies the market into three types: precision, recall, and accuracy. The technical indicator approach showed a higher standard deviation, which may not be reliable for this model. The GA showed a better result compared with other models. They observed that the developed model with leverage performed better than the developed model without leverage. After analyzing their model, they found 83% return on investment.  
[PERSON] and [PERSON] [59] proposed a model for FOREX forecasting using SVM. They used the EUR/USD currency pair for their model's implementation. They used the cross validation technique for their data and split the results into two categories: positive results and negative results. They used accuracy rate, positive, negative, macro averaging and micro averaging for comparing the performance. Their results show a big difference (29.5%) between training sets and testing sets in the Gaussian RBF method, but only a little difference was found in polynomial model. According to the result, the kernel function taken from polynomial theory provides high performance. They compared the normal transaction method with SVM transaction and found that the profit rate was tripled when using an SVM model.  
[PERSON], [PERSON], and [PERSON] [60] proposed a model for FOREX prediction called short term movement forecasting in FOREX. Their provided model does not need any human interaction. They found that to locate motif along with learning models such as clustering algorithms and support vector machines to forecast price changes in financial market such as the FOREX market, technical indicator data can be used. The accuracy of the forecasting system was analyzed using different currency pairs. Finally, their result suggests that by using the zig-zag technical indicators to identify motifs which express short-terms trends in FOREX, the result will be higher predicted accuracy along with trade profits.  
[PERSON] et al. [61] proposed a model for forecasting the direction and movement of currency rates in the FOREX market. They present a novel approach based on SVM and genetic algorithms for creating a FOREX rate forecasting model. In their system they used different trading ideas and input compositions and obtained different results based on their experiment. Their model provided a variety of accuracies for different pairs. The success of their system highly depends on the selection of learning model, inputs, and decision support mechanisms.  
SVM method was used in four papers for predicting the FOREX currencies. The genetic algorithm and SVM hybrid model were the most used techniques. SVMs combined with neural networks have never been explored, so leave a future path open for the researchers. The best feature of the SVM is that it can be used as a classifier [58] and a regressor for forecasting [59; 61]. The literature review suggested that when a SVM is incorporated with the genetic algorithm the model can yield a greater ROI [58]. However, sometimes choosing the wrong kernel may provide a huge difference in the result [59]. Furthermore, some systems highly depend on the selection of learning model, inputs, and decision support mechanisms [61].