THE DESIGN OF A NETWORK-BASED MODEL FOR BUSINESS PERFORMANCE PREDICTION

While much research work has been devoted to analysis and prediction of individuals’ behavior in social networks, very few studies about the analysis of business networks are conducted. Empowered by recent research on automated mining of business networks, this paper illustrates the design of a novel business network-based model called Energy Cascading Model (ECM) for the analysis and prediction of business performance using the proxies of stock prices. More specifically, the proposed prediction model takes into account both influential business relationships and twitter sentiments of firms to infer their stock price movements. Our empirical experiments based on a publicly available financial corpus and social media postings reveal that the proposed ECM model is effective for the prediction of directional stock price movements. The business implication of our research is that business managers can apply our design artifacts to more effectively analyze and predict the potential business performance of targeted firms

Stock market prediction using machine learning classifiers and social media, news

Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors’ behavior. In this paper, we use algorithms on social media and financial news data to discover the impact of this data on stock market prediction accuracy for ten subsequent days. For improving performance and quality of predictions, feature selection and spam tweets reduction are performed on the data sets. Moreover, we perform experiments to find such stock markets that are difficult to predict and those that are more influenced by social media and financial news. We compare results of different algorithms to find a consistent classifier. Finally, for achieving maximum prediction accuracy, deep learning is used and some classifiers are ensembled. Our experimental results show that highest prediction accuracies of 80.53% and 75.16% are achieved using social media and financial news, respectively. We also show that New York and Red Hat stock markets are hard to predict, New York and IBM stocks are more influenced by social media, while London and Microsoft stocks by financial news. Random forest classifier is found to be consistent and highest accuracy of 83.22% is achieved by its ensemble

Investigating Predictive Power of Stock Micro Blog Sentiment in Forecasting Future Stock Price Directional Movement

This study attempts to discover and evaluate the predictive power of stock micro blog sentiment on future stock price directional movements. We construct a set of robust models based on sentiment analysis and data mining algorithms. Using 72,221 micro blog postings for 1909 stock tickers and 3874 distinct authors, our study reveals not only that stock micro blog sentiments do have predictive power for simple and market-adjusted returns respectively, but also that this predictive accuracy is consistent with the underreaction hypothesis observed in behavioral finance. We establish that stock micro blog with its succinctness, high volume and real-time features do have predictive power over future stock price movements. Furthermore, this study provides support for the model of irrational investor sentiment, recommends a supplementary investing approach using user-generated content and validates an instrument that may contribute to the monetization schemes for Virtual Investing Communities