A local and global event sentiment based efficient stock exchange forecasting using deep learning. (February 2020) - Maqsood, Haider; Mehmood, Irfan; Maqsood, Muazzam; Yasir, Muhammad; Afzal, Sitara; Aadil, Farhan; Selim, Mahmoud Mohamed; Muhammad, Khan
Highlights: A deep learning-based method is proposed to forecast stock prices for top companies from four countries that were selected from developed, underdeveloped and emerging economies. We investigate the effect of different famous events occurred from 2012 to 2016 comprising 11.42 Million tweets on stock prediction. The effect of local and global events for stock companies from each country has been investigated. The results show that event sentiment improves the results for stock forecasting depending upon local and global events. Abstract: Stock exchange forecasting is an important aspect of business investment plans. The customers prefer to invest in stocks rather than traditional investments due to high profitability. The high profit is often linked with high risk due to the nonlinear nature of data and complex economic rules. The stock markets are often volatile and change abruptly due to the economic conditions, political situation and major events for the country. Therefore, to investigate the effect of some major events more specifically global and local events for different top stock companies (country-wise) remains an open research area. In this study, we consider four countries- US, Hong Kong, Turkey, and Pakistan from developed, emerging and underdeveloped economies' list. We have explored the effect of different major events occurred during 2012–2016 on stock markets. We use the Twitter dataset to calculate the sentiment analysis for each of these events. The dataset consists of 11.42 million tweets that were used to determine the event sentiment. We have used linear regression, support vector regression and deep learning for stock exchange forecasting. The performance of the system is evaluated using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results show that performance improves by using the sentiment for these events.