Multisource financial sentiment analysis for detecting Bitcoin price change indications using deep learning

Abstract

The success of deep learning (DL) in various areas, such as computer vision, fueled the interest in several novel DLenabled applications, such as financial trading, which could potentially surpass the previously used approaches. Indeed, there has been a plethora of DL-based trading methods proposed in recent years. Despite the success of these methods, they typically rely on a very restricted set of information, usually employing only price-related information. As a result, they ignore sentiment-related information, which can have a profound impact and be a strong predictor of various assets, such as cryptocurencies. The contribution of this paper is multifold. First, we examine whether the use of sentiment information, as extracted by various online sources, including news articles, is beneficial when training DL agents for trading. Then, given the difficulty of training reliable sentiment extractors for financial applications, we evaluate the impact of using different DL models as sentiment extractors, as well as employ an unsupervised training pipeline for further improving their performance. Finally, we propose an effective multisource sentiment fusion approach that can improve the performance over the rest of the evaluated approaches. The conducted experiments have been performed using several different configurations and models, ranging from multilayer perceptrons (MLPs) to convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to provide a reliable evaluation of sentiment-aware DL-based trading strategies providing evidence that sentiment information might be a stronger predictor compared to the information provided by the actual price time series for Bitcoin.

Forecasting of Bitcoin Prices Using Hashrate Features: Wavelet and Deep Stacking approach

“…The success of neural networks in integrating sentiment analysis can lead to better predictions for price analysis. Another example of these studies is the article [23]. They proposed a practical multi-source emotion hybrid approach to improve performance over other evaluated approaches.…”

Financial sentiment analysis: Classic methods vs. deep learning models

“…Furthermore, [55] delved into the utilization of traditional LSTM and attention-based LSTM deep neural networks for predicting future stock market movements, incorporating SA on data collected from Twitter. In [56], various deep learning architectures, spanning from Multilayer Perceptrons (MLPs) to CNNs and Recurrent Neural Networks (RNNs), were harnessed alongside sentiment data gathered from diverse online sources to detect changes in Bitcoin prices. Regarding the real-time BITCOIN price prediction, [57] leveraged RNNs equipped with LSTMs in conjunction with data extracted from Twitter and Reddit.…”

From Text Representation to Financial Market Prediction: A Literature Review

“…[15,21,60,95,96,106] have studied the predictability of the Foreign Stock Exchange (Forex) and cryptocurrencies market, where a currency is traded based on the ratio of two currency pairs, such as the EUR/USD, UDS/JPY, or BTC/USD. News [13,86,89,107]; social network data, such as Twitter [55,108,109], Stocktweet [88,108,110], or Sina Weibo [111]; as well as trends in search engines [95,112]; referring to Wikipedia page statistics [59]; and online reviews of customers about firm's products [113] are the types of media-based sources available to investors. Thomson Reuters and Bloomberg newsgroup are the mainstream news resources in this category; however, it is a basic challenge to utilize relevant texts to the target market because of redundancy and noise in the associated texts.…”

“…In this survey, we study methods for identifying the contextual information published in social media related to financial markets. Text mining techniques, such as a sentiment analysis [10][11][12][13], part of speech tagging (POS) [14,15], text representation, such as transformer-based word embedding [16][17][18][19][20][21][22], and machine learning techniques [23][24][25][26][27][28][29][30][31], have been used in this area after 2006. Recently, researchers have focused on using deep learning-based natural language processing (NLP), such as Bidirectional Encoder Representations from Transformer (BERT) [18,21,[32][33][34] or seq2seq architecture with an attention mechanism [20,[35][36][37][38], to structure textual web data.…”