# An Intelligent Event-Sentiment-Based Daily Foreign Exchange Rate Forecasting System
## 2 Related Work
### Artificial Neural Network  
ANN was not widely used for currency exchange forecasting mainly because the subject was related to economics and majority of economists are not well versed with the working of ANNs. Another reason is the popularity of statistical models for currency exchange forecast. However, over the years, ANN has shown its strength in solving nonlinear problems. The strength of ANN lies in its ability to learn from the data. It does not suffer from noisy and inconsistent data and, thus, is an excellent tool to be used for nonlinear problems. An important feature of ANNs is that they are numeric in nature [19], which makes them an ideal candidate to be used for currency exchange forecast and other financial related matters where we are dealing with numbers. Moreover, ANN accepts input data in the numeric form directly for data mining. One example of the use of ANN for economic matters is the intelligent trading tool described in [20], which shows that with a more complex ANN the resulting system becomes more specialized, which results in increased profits on the training sets. However, in the real-world scenario the less specialized and less complex ones, i.e., the generalized ones, provided better results and more profit. Another example of the use of ANN for economic issues is discussed in [21]. The study in [21] compares the performance of an ANN with other time-series-based techniques. A quadratic neural network (QNN) created for the prediction of financial time series data from the Gabor-Kolmogorov polynomial function is described in [22]. The QNN evaluates the data collected on a day-to-day basis of a certain stock and it calculates a hypothetical price series of the stock. The use of ANN for financial forecasting is advocated in [23; 24], where ANN is applied to an economic time series. A similar kind of study was carried out for the stock market in [25], and in [26] the ANN is applied to predict currency exchange. Another study was carried out in [27] to determinethe Brazilian currency exchange rate with the US dollar and it showed the strength of ANN over other linear models. A similar kind of study was carried out to predict the exchange rate of the Turkish lira with the euro and the US dollar [28], which showed high accuracy in comparison to other models. Another study in [29] predicted the euro to US dollar exchange rate by using a multilevel perceptron (MLP), a specialized ANN, applied to one-day-ahead forecasting of the currency exchange rate collected from European Central Bank (ECB) time series data. The study showed that MLP outperformed other techniques for the said problem. A similar kind of study was carried out in [30], which predicted the exchange rate of the Indian rupee to the US dollar. It created a knowledge guided neural network with integrated features of least mean square as an input. A study of prediction of the Australian dollar against the currency of six other currencies was carried out in [31]. Historical data from over 500 weeks were fed to the ANN and a prediction accuracy of about 87% was recorded. A general regression NN is proposed in [32], which predicts the currency exchange rate on a month-by-month basis. [PERSON] [33] utilized an ANN and compared it with three different models including an autoregressive model, moving average technical frameworks, and at the end a logistic regressive model. The artificial neural network outperformed all the standard models. [PERSON] and [PERSON] [20] discovered that the greater the performance on the training dataset, the more complex the ANN. Although these models lead to unsatisfactorily high generality errors and are predisposed to overfitting on out-of-sample forecasts. To resolve the outdated artificial neural network model's limitation, [PERSON] and [PERSON] [34] proposed an alternative method utilizing artificial neural network and genetic algorithms for constructing predicting models for the rate of exchange.