J Empir Financ 11(1):1–27Ĭao J, Cui H, Shi H, Jiao L (2016) Big data: a parallel particle swarm optimization-back-propagation neural network algorithm based on MapReduce. Appl Sci 8(9):1521īrown GW, Cliff MT (2004) Investor sentiment and the near-term stock market. Springer, Berlin, pp 43–85īrezočnik L, Fister I, Podgorelec V (2018) Swarm intelligence algorithms for feature selection: a review. In: IEEE region 10 conference on TENCON, pp 1–5īastianin A, Manera M (2018) How does stock market volatility react to oil price shocks? Mach Dyn 22(3):666–682īlum C, Li X (2008) Swarm intelligence in optimization. In: IEEE 8th international conference ICICS, pp 130–135Īttigeri GV, MM MP, Pai RM, Nayak A (2015) Stock market prediction: a big data approach. In: IEEE/WIC/ACM international conference on WI-IAT 1, pp 523–530Īl-Zoubi A, Faris H (2017) Spam profile detection in social networks based on public features. In: IEEE 18th international conference on ICACT, pp 710–714Īlostad H, Davulcu H (2015) Directional prediction of stock prices using breaking news on Twitter. Random forest classifier is found to be consistent and highest accuracy of 83.22% is achieved by its ensemble.Īfzal H, Mehmood K (2016) Spam filtering of bi-lingual tweets using machine learning. 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. Our experimental results show that highest prediction accuracies of 80.53% and 75.16% are achieved using social media and financial news, respectively. Finally, for achieving maximum prediction accuracy, deep learning is used and some classifiers are ensembled. We compare results of different algorithms to find a consistent classifier. 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. For improving performance and quality of predictions, feature selection and spam tweets reduction are performed on the data sets. 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. 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. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. 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.