Paper Info
Title
Deep Learning for Predictions in Emerging Currency Markets.
Abstract
Accurate prediction of exchange rates is critical for devising robust monetary policies. Machine learning methods such as shallow neural networks have higher predictive accuracy than time series models when trained on input features carefully crafted by domain knowledge experts. This suggests that deep neural networks, with their ability to learn abstract features from raw data, may provide improved predictive accuracy with raw exchange rates as inputs. The preponderance of research focuses on developed currency markets. The paucity of research in emerging currency markets, and the crucial role that stable currencies play in such economies, motivates us to investigate the effectiveness of deep networks for exchange rate prediction in emerging markets. Literature suggests that the Efficient Market Hypothesis, which posits that asset prices reflect all relevant information, may not hold in such markets because of extraneous factors such as political instability and governmental interventions. This motivates our hypothesis that inclusion of carefully chosen macroeconomic factors as input features may improve the predictive accuracy of deep networks in emerging currency markets. This position paper proposes novel input features based on currency clusters and presents our method for investigating the hypothesis using exchange rates from developed as well as emerging currency markets.
Year
DOI
Venue
2017
10.5220/0006250506810686
ICAART: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 2
Keywords
Field
DocType
Neural Networks,Deep Learning,Convolution Networks,Exchange Rate Prediction,Emerging Markets
Data science,Computer science,Artificial intelligence,Deep learning,Machine learning,Currency
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
2
Name
Order
Citations
PageRank
Svitlana Galeshchuk1304.36
Sumitra Mukherjee231131.75