Incorporating Financial News for Forecasting Bitcoin Prices Based on Long Short-Term Memory Networks
| Autor: | Andreas Geyer-Schulz, Frank J. Fabozzi, Johannes Jakubik, Abdolreza Nazemi |
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| Rok vydání: | 2020 |
| Předmět: |
Rate of return
History Polymers and Plastics Monetization Computer science business.industry Deep learning Sentiment analysis Financial news Unstructured data Industrial and Manufacturing Engineering Long short term memory Leverage (negotiation) Econometrics Artificial intelligence Business and International Management business General Economics Econometrics and Finance Finance |
| Zdroj: | SSRN Electronic Journal. |
| ISSN: | 1556-5068 |
| DOI: | 10.2139/ssrn.3733398 |
| Popis: | In this paper we investigate how a deep learning machine learning model can be applied to improve Bitcoin price forecasting and trading by incorporating unstructured information from financial news. The two-stage model we propose outperforms other machine learning models significantly. In the first stage, we leverage long short-term memory (LSTM) networks to extract structured information from financial news. In the second stage, we apply a second LSTM network with structured input from financial news to the prediction of Bitcoin prices. In addition to the superior performance relative to other machine learning models, we find that the out-of-time rate of return attained with the proposed deep learning model is substantially higher than for a buy-and-hold strategy. Our study highlights how combining deep learning and financial news offers investors and traders support for the monetization of unstructured data in finance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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