Bitcoin price prediction using transfer learning on financial micro-blogs
Files
Published version
Date
2020-12-23
DOI
Authors
Davchev, Jovan
Mishev, Kostadin
Vodenska, Irena
Chitkushev, Lubomir T.
Trajanov, Dimitar
Version
Published version
OA Version
Citation
J. Davchev, K. Mishev, I. Vodenska, L. Chitkushev, D. Trajanov. 2020. "Bitcoin Price Prediction using Transfer Learning on Financial Micro-blogs." Proceedings of the 16th Annual International Conference on Computer Science and Education in Computer Science.
Abstract
We present a methodology for predicting the price of
Bitcoin using Twitter data and historical Bitcoin prices. Bitcoin is
the largest cryptocurrency that, in terms of market capitalization,
represents over 110 billion dollars. The news volume is rapidly
growing, and Twitter is increasingly used as a news source
influencing purchase decisions by informing users of the currency
and its popularity. Using modern Natural Language Processing
models for transfer learning, we analyze tweets’ meaning and
calculate sentiment using the NLP transformers. We combine
the daily historical Bitcoin price data with the daily sentiment
and predict the next day’s price using auto-regressive models for
time-series forecasting.
The results show that modern approaches for sentiment
analysis, time-series forecasting, and transfer-learning are applicable for predicting Bitcoin price when we include sentiment
extracted from financial micro-blogs as input. The results show
improvement when compared to the old approaches using only
historical price data. Additionally, we show that the NLP models
based on transfer-learning methodologies improve the efficiency
in sentiment extraction in financial micro-blogs compared to
standard sentiment extraction methods.