Investigating the informativeness of technical indicators and news sentiment in financial market price prediction

【Author】 Farimani, Saeede Anbaee; Jahan, Majid Vafaei; Fard, Amin Milani; Tabbakh, Seyed Reza Kamel

【Source】KNOWLEDGE-BASED SYSTEMS

【影响因子】8.139

【Abstract】Real-time market prediction tool tracking public opinion in specialized newsgroups and informative market data persuades investors of financial markets. Previous works mainly used lexicon-based sentiment analysis for financial markets prediction, while recently proposed transformer-based sentiment analysis promise good results for cross-domain sentiment analysis. This work considers temporal relationships between consecutive snapshots of informative market data and mood time series for market price prediction. We calculate the sentiment mood time series via the probability distribution of news embedding generated through a BERT-based transformer language model fine-tuned for financial domain sentiment analysis. We then use a deep recurrent neural network for feature extraction followed by a dense layer for price regression. We implemented our approach as an open-source API for real-time price regression. We build a corpus of financial news related to currency pairs in foreign exchange and Cryptocurrency markets. We further augment our model with informative technical indicators and news sentiment scores aligned based on news release timestamp. Results of our experiments show significant error reduction compared to the baselines. Our Financial News and Financial Sentiment Analysis RESTFul APIs are available for public use.(c) 2022 Elsevier B.V. All rights reserved.

【Keywords】Market prediction; Transformer-based language models; Financial sentiment analysis; Information gain; FinBERT

【发表时间】2022 JUL 8

【收录时间】2022-06-08

【文献类型】实证性文章

【主题类别】

区块链治理-市场治理-价格预测