Combining Enterprise Knowledge Graph and News Sentiment Analysis for Stock Price Prediction

Many state of the art methods analyze sentiments in news to predict stock price. When predicting stock price movement, the correlation between stocks is a factor that can’t be ignored because correlated stocks could cause co-movement. Traditional methods of measuring the correlation between stocks are mostly based on the similarity between corresponding stock price data, while ignoring the business relationships between companies, such as shareholding, cooperation and supply-customer relationships. To solve this problem, this paper proposes a new method to calculate the correlation by using the enterprise knowledge graph embedding that systematically considers various types of relationships between listed stocks. Further, we employ Gated Recurrent Unit (GRU) model to combine the correlated stocks’ news sentiment, the focal stock’s news sentiment and the focal stock’s quantitative features to predict the focal stock’s price movement. Results show that our method has an improvement of 8.1% compared with the traditional method

Predicting Stock Price Movement Direction with Enterprise Knowledge Graph

Predicting stock price movement direction is a challenging task for financial investment. Previous researches focused on investigating the impacts of external factors (e.g., big events, economic influence and sentiments) in combination with the historical price to predict short-term stock price movement, while few researches leveraged the power of various relationships among enterprises. To bridge this gap, this research proposes power vector model and influence propagation model to mine the rich information in constructed Enterprise Knowledge Graph (EKG) for price movement prediction. In addition, Deep Neural Network (DNN) is introduced to train the model. The proposed model shows good prediction performance on the dataset of China top 500 enterprises

Customer and Employee Social Media Comments/Feedback and Stock Purchasing Decisions Enhanced by Sentiment Analysis

The U.S. Securities and Exchange Commission (SEC) warns professional investors that sentiment analysis tools may lead to impulsive investment decision-making. This warning comes despite evidence showing that aided social sentiment investment decision tools can increase accurate investment decision-making by 18%. Using Fama\u27s theory of efficient market hypothesis, the purpose of this quantitative correlational study was to examine whether customer Twitter comments and employee Glassdoor feedback sentiment predicted successful investing decisions measured by business stock prices. Two thousand records from 3 archival U.S. public NASDAQ 100 datasets from March 28, 2016, to June 15, 2016 (79 days) of 53 companies with over 100 comments were analyzed using multiple linear regression. The multiple regression analysis results indicated no significant predictability for successful investing decisions, F(10, 2993) = .295, p = .982, R2 = .001. The results indicated that the sentiment from both Twitter and Glassdoor was not necessarily an indicator for investors to make successful investment decisions for the 79 days in 2016. The knowledge about Artificial Intelligence (AI) sentiment usage may help professional investors gain profit or prevent losses. A recommendation to investors is to heed warnings from the SEC about tools for sentiment analysis investment decisions. Implications for positive social change include preventing an investor from using a risky sentiment tool for investment decision-making that may lead to losing capital