Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the people's sentiments towards the market and stocks, have been proved to play important roles in the stocks' volatility, and are extracted to feed into the prediction models for improving the prediction accuracy. However, a major limitation of previous methods is that the indicators are obtained from only a single source whose reliability might be low, or from several data sources but their interactions and correlations among the multi-sourced data are largely ignored. In this work, we extract the events from Web news and the users' sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsic relations among the events and the investors' sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model.
There are also a line of researches trying to apply sentiment analysis on information sources to analyze the impacts of sentiments on stock market volatility. One main data source is the news articles @cite_33 @cite_36 @cite_7 @cite_27 @cite_17 . A hybrid approach is introduced in @cite_36 for stock sentiment analysis based on news articles on companies. @cite_48 , textual data are represented as feature vectors by using the bag-of-words approach, and the data are organized as the input of a time series model, showing the use of market sentiments can improve the forecasting accuracy. The role of author tone in financial news articles is investigated in @cite_30 , which suggests a contrarian investment manner, i.e., see good news, sell; see bad news, buy. The time series data of the Web information in a day is built by summing the sentiment values of the words in all the articles, and a support vector regression (SVR) is used to build the mapping from the Web information time series to the price values @cite_4 . News sentiments are measured and the combined effect of Web news and social media on stock markets are studied in @cite_1 .
@cite_30 @cite_4 @cite_33 @cite_7 @cite_36 @cite_48 @cite_1 @cite_27 @cite_17
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Studying the relationship between public sentiment and stock prices has been the focus of several studies. This paper analyzes whether the sentiment expressed in Twitter feeds, which discuss selected companies and their products, can indicate their stock price changes. To address this problem, an active learning approach was developed and applied to sentiment analysis of tweet streams in the stock market domain. The paper first presents a static Twitter data analysis problem, explored in order to determine the best Twitter-specific text preprocessing setting for training the Support Vector Machine (SVM) sentiment classifier. In the static setting, the Granger causality test shows that sentiments in stock-related tweets can be used as indicators of stock price movements a few days in advance, where improved results were achieved by adapting the SVM classifier to categorize Twitter posts into three sentiment categories of positive, negative and neutral (instead of positive and negative only). These findings were adopted in the development of a new stream-based active learning approach to sentiment analysis, applicable in incremental learning from continuously changing financial tweet streams. To this end, a series of experiments was conducted to determine the best querying strategy for active learning of the SVM classifier adapted to sentiment analysis of financial tweet streams. The experiments in analyzing stock market sentiments of a particular company show that changes in positive sentiment probability can be used as indicators of the changes in stock closing prices. Can the choice of words and tone used by the authors of financial news articles correlate to measurable stock price movements? If so, can the magnitude of price movement be predicted using these same variables? We investigate these questions using the Arizona Financial Text (AZFinText) system, a financial news article prediction system, and pair it with a sentiment analysis tool. Through our analysis, we found that subjective news articles were easier to predict in price direction (59.0 versus 50.0 of chance alone) and using a simple trading engine, subjective articles garnered a 3.30 return. Looking further into the role of author tone in financial news articles, we found that articles with a negative sentiment were easiest to predict in price direction (50.9 versus 50.0 of chance alone) and a 3.04 trading return. Investigating negative sentiment further, we found that our system was able to predict price decreases in articles of a positive sentiment 53.5 of the time, and price increases in articles of a negative sentiment 52.4 of the time. We believe that perhaps this result can be attributable to market traders behaving in a contrarian manner, e.g., see good news, sell; see bad news, buy. 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. Extracting sentiment from text is a hard semantic problem. We develop a methodology for extracting small investor sentiment from stock message boards. The algorithm comprises different classifier algorithms coupled together by a voting scheme. Accuracy levels are similar to widely used Bayes classifiers, but false positives are lower and sentiment accuracy higher. Time series and cross-sectional aggregation of message information improves the quality of the resultant sentiment index, particularly in the presence of slang and ambiguity. Empirical applications evidence a relationship with stock values---tech-sector postings are related to stock index levels, and to volumes and volatility. The algorithms may be used to assess the impact on investor opinion of management announcements, press releases, third-party news, and regulatory changes.