Finance

Financial economics

Market data

Technical analysis

Machine learning

Trading strategy

Econometrics

Time series

Artificial intelligence

Economics

Microeconomics

Profit (economics)

Financial market

Exchange rate

Foreign exchange

Computer science

Foreign exchange market

Monetary economics

Financial Innovation

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.

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