Foreign Exchange Market Inter-city Information-based Neural Network Prediction Model

Since the major Western industrial countries put the floating exchange rate system into practice in 1973, exchange rate on the foreign exchange market fluctuates frequently. Everyone who participates in foreign exchange transactions are facing with a huge foreign exchange risk,no matter individuals or banks, businesses, and even sovereign states. The risk has been reflected by frequent occurrence of currency crises in the two or three decades ,which led to negative impact on the economics.Meanwhile, the scale of the foreign exchange market trading has been far more than the stock,futures and other financial products markets, become the largest financial market. The foreign exchange market participants, such as importers, exporters, portfolio managers and central banks, etc., just like the food chain at all levels in the ecosystem.As the rapid increase of market size, the food chain relationships become more complex.This makes the forecasting of exchange rate through various methods has become extremely urgent. This will not only help the speculators on the foreign exchange market make trading strategies and reduce risk, but also can make the sovereign state carry early warning for possible financial crisis ,and keep the financial risk away, safeguard national financial security.This paper summarized the short-term and long-term factors which impact the exchange rate fluctuations at first. Prediction of the current exchange rate is mainly based on basic variables and technical point of view, this paper give a simple evaluation on such work. However, due to the complexity of the foreign exchange market transactions, the traditional macro exchange rate determination models have been difficult to predict short-term exchange rate fluctuations.On this basis, the research chose the technology method to predict the short-term exchange rate, select the hourly closing price of the EURUSD as forecast object.The focus of this paper is it proposed the EURUSD non-linear prediction model based on intermarket information. We use a multilayer feedforward neural network(MFNN) prediction model for the calculation of non-linear form. This paper elaborate the principle of MFNN in detail, especially the BP training algorithm calculation model MFNN the BP training algorithm.In the second part of this article, the theory of artificial neural networks is elaborated, especially the content of BP neural network, and also,the exchange rate forecasts based on neural network methods were generally reviewed and evaluated. Next, by constructing a single variable and multivariate time series model, predict the hourly closing price of EURUSD, And test the actual prediction effect by correct direction hit rate.In the single variable model, make the historical hourly closing price of EURUSD as input data, the research shows that the predicted effect is not very satisfactory. In the multivariable model, the historical hourly closing price of relevant currency like EURUSD,GBPUSD,USDCHF,USDJPY are selected as input data, the correct direction hit rate can reach 62.63%. In the research,The USD index and other converted currencies index such as EUR index,GBP index,CHF index,JPY index are also selected as input data to forecast the target exchange rate, there is no significant improve compared with single variable model.