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.
Various studies have found that financial news can dramatically affect the stock price @cite_47 @cite_14 @cite_28 @cite_0 @cite_9 @cite_3 . According to @cite_25 , it summarizes several text mining methods that are used in the financial field. Structured events have been extracted as tuples including the agent, predicate and object from news documents in @cite_44 . Deep learning-based method is proposed to learn event representations to capture syntactic and semantic information based on word embeddings @cite_23 . Recently, the external information from knowledge graphs is incorporated into the learning process to generate event representations @cite_38 . A deep neural model is proposed to measure the information content of financial news, aiming to understand an event's economic value @cite_29 . Temporal properties of news events that have short- or long-term influences on stock prices are modeled in @cite_52 . However, these works only consider the impacts of news events but ignore the fact that the moods of investors may also lead to stock price fluctuations.
@cite_38 @cite_14 @cite_28 @cite_9 @cite_29 @cite_52 @cite_3 @cite_0 @cite_44 @cite_23 @cite_47 @cite_25
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It has been shown that news events influence the trends of stock price movements. However, previous work on news-driven stock market prediction rely on shallow features (such as bags-of-words, named entities and noun phrases), which do not capture structured entity-relation information, and hence cannot represent complete and exact events. Recent advances in Open Information Extraction (Open IE) techniques enable the extraction of structured events from web-scale data. We propose to adapt Open IE technology for event-based stock price movement prediction, extracting structured events from large-scale public news without manual efforts. Both linear and nonlinear models are employed to empirically investigate the hidden and complex relationships between events and the stock market. Largescale experiments show that the accuracy of S&P 500 index prediction is 60 , and that of individual stock prediction can be over 70 . Our event-based system outperforms bags-of-words-based baselines, and previously reported systems trained on S&P 500 stock historical data. Investors make decisions based on various factors, including consumer price index, price-earnings ratio, and also miscellaneous events reported by newspapers. In order to assist their decisions in a timely manner, many studies have been conducted to automatically analyze those information sources in the last decades. However, the majority of the efforts was made for utilizing numerical information, partly due to the difficulty to process natural language texts and to make sense of their temporal properties. This study sheds light on this problem by using deep learning, which has been attracting much attention in various areas of research including pattern mining and machine learning for its ability to automatically construct useful features from a large amount of data. Specifically, this study proposes an approach to market trend prediction based on a recurrent deep neural network to model temporal effects of past events. The validity of the proposed approach is demonstrated on the real-world data for ten Nikkei companies. Abstract Over the past years, the effect of massive information from the web on the financial market has increased. How to process and utilize such information attracts both researchers and portfolio managers. In this paper, financial information obtained daily from the web is treated as a time series and then associated with stock price volatilities. First, six research topics on financial time series are outlined, namely, analyses of stock price time series P , trading volume time series V , web information time series W , and relationship between P and V , P and W , as well as V and W . Second, a model connecting P and W based on the support vector regression (SVR) is examined as an example of the six research topics. Third, given that a typically successful way of computer-based natural language processing is through the conduct of keyword analysis, the novel finance-computer time series W is explicitly defined in terms of financial keywords and is used in the present paper as the topic of investigation. The relationship between P and W is modeled using SVR. Because during the pre-web era people cannot manually and efficiently process image information from the newspapers and sounds from the television and radio over a longer period of time (e.g., a year), they were unable to obtain the time series W . Therefore, it is the web that makes the research on the relationship between P and W in the meaning of quantity of W possible. Finally, experiments on the Shanghai and Shenzhen security markets revealed that the introduction of W helps improve model accuracies. As the web further develops, more and more ordinary people share their views on the web. The “long-tail” of massive financial information formed by these “grass roots” has a noticeable effect on the financial markets. In financial markets, those who quickly capture and interpret financial information have the potential to generate profits. With the use of the newly found model connecting P with W and fast decision making, financial market practitioners can be rewarded. We propose a deep learning method for event-driven stock market prediction. First, events are extracted from news text, and represented as dense vectors, trained using a novel neural tensor network. Second, a deep convolutional neural network is used to model both short-term and long-term influences of events on stock price movements. Experimental results show that our model can achieve nearly 6 improvements on S&P 500 index prediction and individual stock prediction, respectively, compared to state-of-the-art baseline methods. In addition, market simulation results show that our system is more capable of making profits than previously reported systems trained on S&P 500 stock historical data.