Sentiment Analysis of Financial News: Mechanics and Statistics

International Review of Financial Analysis, 2014

ABSTRACT We survey the textual sentiment literature, comparing and contrasting the various information sources, content analysis methods, and empirical models that have been used to date. We summarize the important and influential findings about how textual sentiment impacts on individual, firm-level and market-level behavior and performance, and vice versa. We point to what is agreed and what remains controversial. Promising directions for future research are emerging from the availability of more accurate and efficient sentiment measures resulting from increasingly sophisticated textual content analysis coupled with more extensive field-specific dictionaries. This is enabling more wide-ranging studies that use increasingly sophisticated models to help us better understand behavioral finance patterns across individuals, institutions and markets.

international journal for research in applied science and engineering technology ijraset, 2020

The task point is to break down stock forecast based on news stories and this should be possible by nostalgic examination by having three parameters and this undertaking will give a brief of any news (feelings). The parameters on which our venture will produce results are Positive, Negative or Neutral. This will help the proficiency of any association in light of the fact that previous manual methodology was taken a stab at utilizing a human to peruse the articles and grouping the notion, yet this framework will make a programmed approach for advertise developments (forecast). In this undertaking, we will utilize two innovations for example AI and Natural Language Processing. Because of the unpredictability of the securities exchange, value variances dependent on opinion and news reports are normal. Brokers draw upon a wide assortment of freely accessible data to advise their market choices. For this task, we concentrated on the examination of freely accessible news reports with the utilization of PCs to give guidance to stock exchanging. We execute a model that predicts the following days' stock development by fusing pertinent monetary data, for example, late stock value development and income shock, and literary data from these money related reports. We exhibit that the model which incorporates printed data performs altogether better than a model with budgetary data alone. Index Terms: Stock news prediction, NLP modeling, Profitable Stock analysis, financial market, news articles, sentiment analysi

Financial news articles are believed to have impacts on stock price return. Previous works model news pieces in bag-of-words space, which analyzes the latent relationship between word statistical patterns and stock price movements. However, news sentiment, which is an important ring on the chain of mapping from the word patterns to the price movements, is rarely touched. In this paper, we first implement a generic stock price prediction framework, and plug in six different models with different analyzing approaches. To take one step further, we use Harvard psychological dictionary and Loughran–McDonald financial sentiment dictionary to construct a sentiment space. Textual news articles are then quantitatively measured and projected onto the sentiment space. Instance labeling method is rigorously discussed and tested. We evaluate the models’ prediction accuracy and empirically compare their performance at different market classification levels. Experiments are conducted on five years historical Hong Kong Stock Exchange prices and news articles. Results show that (1) at individual stock, sector and index levels, the models with sentiment analysis outperform the bag-of-words model in both validation set and independent testing set; (2) the models which use sentiment polarity cannot provide useful predictions; (3) there is a minor difference between the models using two different sentiment dictionaries.

International Journal of Machine Learning and Computing, 2014

Business and financial news are important resources that investors referred to when monitoring the stock performance. News brings us the latest information about the stock market. Studies have shown that business and financial news have a strong correlation with future stock performance. Business and financial news can be used to extract sentiments and opinions that may assist in the stock price predictions. In this paper, we present a sentiment analyser for financial news articles using lexicon-based approach. We utilized two most important elements of news, the headline and the content as our test data. We use polarity lexicon to distinguish between positive and negative polarity of each term in the corpus. We further investigate on how news headline will affect the sentiment analysis by adjusting the weights of the news headline and news content's sentiment value. Three sets of experiments were carried out using headline only, content only and headline and content as test data. In the experiment, we used non-stemming tokens and stemming tokens when considering individual word found in the news article. The preliminary results are presented and discussed in this paper.

Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing, 2023

Forecasting financial market trends through time series analysis and natural language processing poses a complex and demanding undertaking, owing to the numerous variables that can influence stock prices. These variables encompass a spectrum of economic and political occurrences, as well as prevailing public attitudes. Recent research has indicated that the expression of public sentiments on social media platforms such as Twitter may have a noteworthy impact on the determination of stock prices. The objective of this study was to assess the viability of Twitter sentiments as a tool for predicting stock prices of major corporations such as Tesla, Apple. Our study has revealed a robust association between the emotions conveyed in tweets and fluctuations in stock prices. Our findings indicate that positivity, negativity, and subjectivity are the primary determinants of fluctuations in stock prices. The data was analyzed utilizing the Long-Short Term Memory neural network (LSTM) model, which is currently recognized as the leading methodology for predicting stock prices by incorporating Twitter sentiments and historical stock prices data. Our analysis yielded findings indicating that Twitter sentiments possess significant potential as an informative resource for forecasting stock prices. The models utilized in our study demonstrated a high degree of reliability and yielded precise outcomes for the designated corporations. In summary, this research emphasizes the significance of incorporating public opinions into the prediction of stock prices. The application of Time Series Analysis and Natural Language Processing methodologies can yield significant scientific findings regarding financial market patterns, thereby facilitating informed decision-making among investors. The results of our study indicate that the utilization of Twitter sentiments can serve as a potent instrument for forecasting stock prices, and ought to be factored in when formulating investment strategies.

2021

Objective of this research is to build a model to predict stock price using sentimental information from news headlines and historical prices, and the model is able to not only conclude better results but also minimize the difference between predicted values and actual values. News headlines show impact on stock price. Unlike previous approaches where the textual information were usually calculated into sentiment score, we apply various approaches to extract information from news headlines. On the other hand, price data through time series are also useful to predict stock prices. Hence, improvement is made with combination of sentiment analysis of news headlines and time series analysis of historical prices, and the combination is able to complement nonavailability of sentiment lexicon and lack of news. Compared to time series models and word embedding models, our combined model shows smaller or similarly error measures MAPE, MAE, and RMSE with time series models, and reduces lag in graphs.

Proceedings of the 18th International Database Engineering & Applications Symposium, 2014

Sentiment analysis has been increasingly applied to the stock market domain. In particular, investor sentiment indicators can be used to model and predict stock market variables. In this context, the quality of the sentiment analysis is highly dependent of the opinion lexicon adopted. However, there is a lack of lexicons adjusted to microblogging stock market data. In this work, we propose an automatic procedure for the creation of such lexicon by exploring a large set of labeled messages from StockTwits, a popular financial microblogging service, and using four statistical measures: adaptations of the known TF-IDF, Information Gain, Class Percentage, and a newly proposed Weighted Class Probability. The obtained lexicons are competitive when compared with a set of six reference lexicons. Moreover, we verified that it is beneficial to use continuous sentiment scores instead of sentiment labels.

International Journal of Finance & Economics, 2017

One of the most important research streams in finance is to understand the determinants of stock market dynamics. Using a large amount of linguistic data regarding 960,808 posts during a 5-year time period (from March 1, 2008, to August 31, 2013), we develop an economics blogs pessimism indicator (considered as a proxy for either investor sentiment or risk aversion), and we show its validity to build performing trading strategies.

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2016

There are numerous studies suggesting that published news stories have an important effect on the direction of the stock market, its volatility, the volume of trades, and the value of individual stocks mentioned in the news. There is even some published research suggesting that automated sentiment analysis of news documents, quarterly reports, blogs and/or twitter data can be productively used as part of a trading strategy. This paper presents just such a family of trading strategies, and then uses this application to reexamine some of the tacit assumptions behind how sentiment analyzers are generally evaluated, in spite of the contexts of their application. This discrepancy comes at a cost.

IEEE Access

Financial and economic news is continuously monitored by financial market participants. According to the efficient market hypothesis, all past information is reflected in stock prices and new information is instantaneously absorbed in determining future stock prices. Hence, prompt extraction of positive or negative sentiments from news is very important for investment decision-making by traders, portfolio managers and investors. Sentiment analysis models can provide an efficient method for extracting actionable signals from the news. However, financial sentiment analysis is challenging due to domain-specific language and unavailability of large labeled datasets. General sentiment analysis models are ineffective when applied to specific domains such as finance. To overcome these challenges, we design an evaluation platform which we use to assess the effectiveness and performance of various sentiment analysis approaches, based on combinations of text representation methods and machine-learning classifiers. We perform more than one hundred experiments using publicly available datasets, labeled by financial experts. We start the evaluation with specific lexicons for sentiment analysis in finance and gradually build the study to include word and sentence encoders, up to the latest available NLP transformers. The results show improved efficiency of contextual embeddings in sentiment analysis compared to lexicons and fixed word and sentence encoders, even when large datasets are not available. Furthermore, distilled versions of NLP transformers produce comparable results to their larger teacher models, which makes them suitable for use in production environments.