Traditional stock market prediction methods commonly only utilize the historical trading data, ignoring the fact that stock market fluctuations can be impacted by various other information sources such as stock related events. Although some recent works propose event-driven prediction approaches by considering the event data, how to leverage the joint impacts of multiple data sources still remains an open research problem. In this work, we study how to explore multiple data sources to improve the performance of the stock prediction. We introduce an Extended Coupled Hidden Markov Model incorporating the news events with the historical trading data. To address the data sparsity issue of news events for each single stock, we further study the fluctuation correlations between the stocks and incorporate the correlations into the model to facilitate the prediction task. Evaluations on China A-share market data in 2016 show the superior performance of our model against previous methods.
There are also a few studies that analyze the impacts of sentiments on stock market volatility. One main data source for sentiment analysis is the news articles @cite_35 @cite_45 @cite_7 @cite_23 , and the other common data source is the social media @cite_6 @cite_22 @cite_40 @cite_48 . @cite_22 use sentiment and attention indicators extracted from microblogs and survey indices to predict stock market behavior. Topics and related sentiments are extracted from the texts in a message board, which are provided to facilitate stock prediction @cite_26 . With Twitter data, the social relations between stocks are exploited to build a stock network based on the co-occurring relationships @cite_49 . Then a lexicon-based sentiment analysis method is applied to compute the sentiment score for each node and each edge. The sentiment time series and price time series are used for prediction. However, these studies didn't take the impacts of the news events into account.
@cite_35 @cite_26 @cite_22 @cite_7 @cite_48 @cite_6 @cite_40 @cite_45 @cite_23 @cite_49
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A novel method for predicting stock price movement was presented.Topics and sentiments of them were extracted from social media as the feature.Two methods were proposed to capture the topic-sentiment feature.Integration of the sentiments was investigated via a large scale experiment.Our model outperformed other methods in the average accuracy of 18 stocks. The goal of this research is to build a model to predict stock price movement using the sentiment from social media. Unlike previous approaches where the overall moods or sentiments are considered, the sentiments of the specific topics of the company are incorporated into the stock prediction model. Topics and related sentiments are automatically extracted from the texts in a message board by using our proposed method as well as existing topic models. In addition, this paper shows an evaluation of the effectiveness of the sentiment analysis in the stock prediction task via a large scale experiment. Comparing the accuracy average over 18 stocks in one year transaction, our method achieved 2.07 better performance than the model using historical prices only. Furthermore, when comparing the methods only for the stocks that are difficult to predict, our method achieved 9.83 better accuracy than historical price method, and 3.03 better than human sentiment method. Abstract In this paper, we propose a robust methodology to assess the value of microblogging data to forecast stock market variables: returns, volatility and trading volume of diverse indices and portfolios. The methodology uses sentiment and attention indicators extracted from microblogs (a large Twitter dataset is adopted) and survey indices (AAII and II, USMC and Sentix), diverse forms to daily aggregate these indicators, usage of a Kalman Filter to merge microblog and survey sources, a realistic rolling windows evaluation, several Machine Learning methods and the Diebold-Mariano test to validate if the sentiment and attention based predictions are valuable when compared with an autoregressive baseline. We found that Twitter sentiment and posting volume were relevant for the forecasting of returns of S&P 500 index, portfolios of lower market capitalization and some industries. Additionally, KF sentiment was informative for the forecasting of returns. Moreover, Twitter and KF sentiment indicators were useful for the prediction of some survey sentiment indicators. These results confirm the usefulness of microblogging data for financial expert systems, allowing to predict stock market behavior and providing a valuable alternative for existing survey measures with advantages (e.g., fast and cheap creation, daily frequency). This paper describes early work trying to predict stock market indicators such as Dow Jones, NASDAQ and S&P 500 by analyzing Twitter posts. We collected the twitter feeds for six months and got a randomized subsample of about one hundredth of the full volume of all tweets. We measured collective hope and fear on each day and analyzed the correlation between these indices and the stock market indicators. We found that emotional tweet percentage significantly negatively correlated with Dow Jones, NASDAQ and S&P 500, but displayed significant positive correlation to VIX. It therefore seems that just checking on twitter for emotional outbursts of any kind gives a predictor of how the stock market will be doing the next day. This paper proposes a technique to leverage topic based sentiments from Twitter to help predict the stock market. We first utilize a con- tinuous Dirichlet Process Mixture model to learn the daily topic set. Then, for each topic we derive its sentiment according to its opin- ion words distribution to build a sentiment time series. We then regress the stock index and the Twitter sentiment time series to predict the market. Experiments on real-life S&P100 Index show that our approach is effective and performs better than existing state-of-the-art non-topic based methods. The goal of this research is to build a model to predict stock price movement using sentiments on social media. A new feature which captures topics and their sentiments simultaneously is introduced in the prediction model. In addition, a new topic model TSLDA is proposed to obtain this feature. Our method outperformed a model using only historical prices by about 6.07 in accuracy. Furthermore, when comparing to other sentiment analysis methods, the accuracy of our method was also better than LDA and JST based methods by 6.43 and 6.07 . The results show that incorporation of the sentiment information from social media can help to improve the stock prediction. The Stock Sonar (TSS) is a stock sentiment analysis application based on a novel hybrid approach. While previous work focused on document level sentiment classification, or extracted only generic sentiment at the phrase level, TSS integrates sentiment dictionaries, phrase-level compositional patterns, and predicate-level semantic events. TSS generates precise in text sentiment tagging as well as sentiment-oriented event summaries for a given stock, which are also aggregated into sentiment scores. Hence, TSS allows investors to get the essence of thousands of articles every day and may help them to make timely, informed trading decisions. The extracted sentiment is also shown to improve the accuracy of an existing document-level sentiment classifier. 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. In this paper we first exploit cash-tags ("$" fol- lowed by stocks' ticker symbols) in Twitter to build a stock network, where nodes are stocks connected by edges when two stocks co-occur frequently in tweets. We then employ a labeled topic model to jointly model both the tweets and the network structure to assign each node and each edge a topic respectively. This Semantic Stock Network (SSN) summarizes discussion topics about stocks and stock relations. We fur- ther show that social sentiment about stock (node) topics and stock relationship (edge) topics are predictive of each stock's market. For predic- tion, we propose to regress the topic-sentiment time-series and the stock's price time series. Ex- perimental results demonstrate that topic senti- ments from close neighbors are able to help im- prove the prediction of a stock markedly.