Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators

Forex(foreign exchange)is a special financial market that entails both high risks and high profit opportunities for traders.It is also a very simple market since traders can profit by just predicting the direction of the exchange rate between two currencies.However,incorrect predictions in Forex may cause much higher losses than in other typical financial markets.The direction prediction requirement makes the problem quite different from other typical time-series forecasting problems.In this work,we used a popular deep learning tool called“long short-term memory”(LSTM),which has been shown to be very effective in many time-series forecasting problems,to make direction predictions in Forex.We utilized two different data sets—namely,macroeconomic data and technical indicator data—since in the financial world,fundamental and technical analysis are two main techniques,and they use those two data sets,respectively.Our proposed hybrid model,which combines two separate LSTMs corresponding to these two data sets,was found to be quite successful in experiments using real data.

Preface:Special Issue on Modeling,Simulation,and Optimization in Operational Research

“Operational Research”(OR)is“the discipline of applying advanced analytical methods to help make better decisions”.By using techniques such as problem structuring methods and mathematical modeling to analyze nontrivial situations,OR gives executives the power to make more effective decisions and build more productive systems.