CS6101

Deep Learning for Natural Language Processing


Foreign Exchange Forecasting

By xiao nan, luo wuqiong (nick), yesha simaria, vikash ranjan

This project explored forecasting foreign currency exchange price using both historical price data and market news of the trading pair. We first built a market sentiment classifier to obtain daily market sentiment score from market news of the trading pair. To reduce the amount of labelled training data required, we used transfer learning technique by first training a language model using Wikipedia data, then fine-tuning the language model using market news, and finally using the language model as the basis to train the market sentiment classifier. We then combined the market sentiment score and historical price data and fed them in an end-to-end encoder-decoder recurrent neural network (RNN) to forecast foreign exchange trends.

Objective

Motivated by technical and fundamental analysis for stock price forecasting, we aim to predict foreign exchange trends by both historical price data and market news of the trading pair.

Methods

We first built a market sentiment classifier to obtain daily market sentiment score from market news of the trading pair. To reduce the amount of labelled training data required, we used transfer learning technique by first training a language model using Wikipedia data, then fine-tuning the language model using market news, and finally using the language model as the basis to train the market sentiment classifier. We then combined the market sentiment score and historical price data and fed them in an end-to-end encoder-decoder recurrent neural network (RNN) to forecast foreign exchange trends.

Datasets

We used past 10 years of EUR/USD tick data and 200,000 market news of this trading pair in these 10 years.


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