BERT for Stock Market Sentiment Analysis

Sousa, Matheus Gomes; Sakiyama, Kenzo; Rodrigues, Lucas de Souza; Moraes, Pedro Henrique; Fernandes, Eraldo Rezende; Matsubara, Edson Takashi

doi:10.1109/ictai.2019.00231

Cited by 79 publications

(41 citation statements)

References 11 publications

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“…A self-learning task is used to pre-train this model on a large number of general-domain texts. They manually categorized stock news items as positive, neutral, or negative to fine-tune their model on sentiment analysis for the stock market [29]. In a similar direction, in our study, we use a newer distilled version of BERT, DistilBERT [30], to perform the sentiment classification of the news events into positive, negative, and neutral.…”

Section: Literature Reviewmentioning

confidence: 99%

Event Study: Advanced Machine Learning and Statistical Technique for Analyzing Sustainability in Banking Stocks

et al. 2021

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Machine learning has grown in popularity in recent years as a method for evaluating financial text data, with promising results in stock price projection from financial news. Various research has looked at the relationship between news events and stock prices, but there is little evidence on how different sentiments (negative, neutral, and positive) of such events impact the performance of stocks or indices in comparison to benchmark indices. The goal of this paper is to analyze how a specific banking news event (such as a fraud or a bank merger) and other co-related news events (such as government policies or national elections), as well as the framing of both the news event and news-event sentiment, impair the formation of the respective bank’s stock and the banking index, i.e., Bank Nifty, in Indian stock markets over time. The task is achieved through three phases. In the first phase, we extract the banking and other co-related news events from the pool of financial news. The news events are further categorized into negative, positive, and neutral sentiments in the second phase. This study covers the third phase of our research work, where we analyze the impact of news events concerning sentiments or linguistics in the price movement of the respective bank’s stock, identified or recognized from these news events, against benchmark index Bank Nifty and the banking index against benchmark index Nifty50 for the short to long term. For the short term, we analyzed the movement of banking stock or index to benchmark index in terms of CARs (cumulative abnormal returns) surrounding the publication day (termed as D) of the news event in the event windows of (−1,D), (D,1), (−1,1), (D,5), (−5,−1), and (−5,5). For the long term, we analyzed the movement of banking stock or index to benchmark index in the event windows of (D,30), (−30,−1), (−30,30), (D,60), (−60,−1), and (−60,60). We explore the deep learning model, bidirectional encoder representations from transformers, and statistical method CAPM for this research.

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Section: Literature Reviewmentioning

confidence: 99%

Event Study: Advanced Machine Learning and Statistical Technique for Analyzing Sustainability in Banking Stocks

et al. 2021

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“…In 2017, transformers architecture was introduced by the "Attention Is All You Need" titled paper and moved natural language processing research one step further by implementing transfer learning [13]. The most successful transformers models indicated as BERT (Bidirectional Encoder Representations from Transformers), XLNet, and Flair in different studies regarding the dataset and the experiment environment [14][15][16][17][18][19]. In this paper, all mentioned natural language processing techniques will be applied to commodity news data to obtain the best model for sentiment analysis of commodities.…”

Section: Literaturementioning

confidence: 99%

From Statistical to Deep Learning Models: A Comparative Sentiment Analysis Over Commodity News

Sivri

Korkmaz

Üstündağ

2021

Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation

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The sentiment analysis of news and social media posts is a growing research area with advancements in natural language processing and deep learning techniques. Although various studies addressing the extraction of the sentiment score from news and other resources for specified stocks or a stock index, still there is a lack of an analysis of the sentiment in more specialized topics such as commodity news. In this paper, several natural language processing techniques with a varying range from statistical methods to deep learning-based methods were applied on the commodity news. Firstly, the dictionary-based methods were investigated with the most common dictionaries in financial sentiment analysis such as Loughran & McDonald and Harvard dictionaries. Then, statistical models have been applied to the commodity news with count vectorizer and TF-IDF. The compression-based NCD has been also included to test on the labeled data. To improve the results of the sentiment extraction, the news data was processed by deep learning-based state-of-art models such as ULMFit, Flair, Word2Vec, XLNet, and BERT. A comprehensive analysis of all tested models was held. The final analysis indicated the performance difference between the deep learningbased and statistical models for the sentiment analysis task on the commodity news. BERT has achieved superior performance among the deep learning models for the given data.

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“…Stock price prediction is a very challenge task due to the diverse and complicate factors, including corporate financial performance [18], industry information [10], public news [19], [13], social sentiment [20], [21]. Recently, various traditional machine learning and deep learning approaches have been proposed to extract valuable clues from different types of information sources for better stock prediction.…”

Section: Related Work a Stock Price Predictionmentioning

confidence: 99%

“…The stock movement prediction is a binary classification problem. Several metrics [21] are selected to justify the effectiveness of all the approaches, i.e., Accuracy (ACC), Precision, Recall, F1-score and Matthews Correlation Coefficient (MCC) [5]. ACC measures the ratio of correct predictions over all examples.…”

Section: B Evaluation Metricsmentioning

confidence: 99%

Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction

Ye,

Zhao,

et al. 2020

Preprint

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Stock price movement prediction is commonly accepted as a very challenging task due to the volatile nature of financial markets. Previous works typically predict the stock price mainly based on its own information, neglecting the cross effect among involved stocks. However, it is well known that an individual stock price is correlated with prices of other stocks in complex ways. To take the cross effect into consideration, we propose a deep learning framework, called Multi-GCGRU, which comprises graph convolutional network (GCN) and gated recurrent units (GRU) to predict stock movement. Specifically, we first encode multiple relationships among stocks into graphs based on financial domain knowledge and utilize GCN to extract the cross effect based on these pre-defined graphs. To further get rid of prior knowledge, we explore an adaptive relationship learned by data automatically. The cross-correlation features produced by GCN is concatenated with historical records and fed into GRU to model the temporal dependency of stock prices. Experiments on two stock indexes in China market show that our model outperforms other baselines. Note that our model is rather feasible to incorporate more effective stock relationships containing expert knowledge as well as learn relationship on the basis of data dynamically.

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BERT for Stock Market Sentiment Analysis

Cited by 79 publications

References 11 publications

Event Study: Advanced Machine Learning and Statistical Technique for Analyzing Sustainability in Banking Stocks

Event Study: Advanced Machine Learning and Statistical Technique for Analyzing Sustainability in Banking Stocks

From Statistical to Deep Learning Models: A Comparative Sentiment Analysis Over Commodity News

Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction

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