Using twitter sentiments and search volumes index to predict oil, gold, forex and markets indices

Abstract

Behavioral finance is an upcoming research field which is drawing a lot of attention of both academia and industry. With changing dynamics of internet behavior of millions across the globe, it provides opportunity to create a unified forecasting model comprising of large scale microblog discussions and search behavior for better understanding of market movements. In this work we used 2 million tweets and search volume index (SVI from Google) for a period of June 2010 to September 2011; studied causative relationships and developed a comprehensive and unified approach for a model for equity (Dow Jones Industrial Average-DJIA and NASDAQ- 100), commodity markets (oil and gold) and Euro Forex rates. We investigate the lagged and statistically causative relations of Twitter sentiments developing prior during active trading days to market inactive days and search behavior of public before any change in the prices/ indices. Our results show extent of lagged significance with high correlation value upto 0.82 between search volumes and gold price in USD. We find weekly accuracy in direction (up and down prediction) uptil 94.3% for DJIA and 90% for NASDAQ-100 with significant reduction in mean average percentage error for all the forecasting models.