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

Date

2021-01-01

Author

Yildirim, Deniz Can
Toroslu, İsmail Hakkı
Fiore, Ugo

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

URI

https://hdl.handle.net/11511/88640

Journal

FINANCIAL INNOVATION

DOI

https://doi.org/10.1186/s40854-020-00220-2

Collections

Department of Computer Engineering, Article

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Citation Formats

D. C. Yildirim, İ. H. Toroslu, and U. Fiore, “Forecasting directional movement of Forex data using LSTM with technical and macroeconomic indicators,” FINANCIAL INNOVATION, pp. 0–0, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/88640.