Name
Abstract | ||
|---|---|---|
Trillions of dollars are traded daily on the foreign exchange (forex) market, making it the largest financial market in the world. Accurate forecasting of forex rates is a necessary element in any effective hedging or speculation strategy in the forex market. Time series models and shallow neural networks provide acceptable point estimates for future rates but are poor at predicting the direction of change and, hence, are not very useful for supporting profitable trading strategies. Machine learning classifiers trained on input features crafted based on domain knowledge produce marginally better results. The recent success of deep networks is partially attributable to their ability to learn abstract features from raw data. This motivates us to investigate the ability of deep convolution neural networks to predict the direction of change in forex rates. Exchange rates for the currency pairs EUR/USD, GBP/USD and JPY/USD are used in experiments. Results demonstrate that trained deep networks achieve satisfactory out-of-sample prediction accuracy. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1002/isaf.1404 | INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT |
Keywords | Field | DocType |
deep network, feature engineering, financial prediction, foreign exchange | Speculation,Econometrics,Trading strategy,Foreign exchange market,Computer science,Raw data,Artificial intelligence,Hedge (finance),Artificial neural network,Financial market,Machine learning,Currency | Journal |
Volume | Issue | ISSN |
24 | 4 | 1055-615X |
Citations | PageRank | References |
6 | 0.48 | 12 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Svitlana Galeshchuk | 1 | 6 | 0.48 |
| Sumitra Mukherjee | 2 | 311 | 31.75 |