Modeling Movements in Oil, Gold, Forex and Market Indices using Search Volume Index and Twitter Sentiments

TL;DR

This paper develops a unified model using Twitter sentiments and search volume data to forecast movements in various financial markets, achieving high accuracy and significant correlations.

Contribution

It introduces a comprehensive causative analysis and a unified approach for modeling multiple market securities using social media and search data.

Findings

High correlation (up to 0.82) between search volume and gold prices.

Weekly directional prediction accuracy up to 94.3% for DJIA.

Significant reduction in forecasting error across models.

Abstract

Study of the forecasting models using large scale microblog discussions and the search behavior data can provide a good insight for better understanding the market movements. In this work we collected a dataset of 2 million tweets and search volume index (SVI from Google) for a period of June 2010 to September 2011. We perform a study over a set of comprehensive causative relationships and developed a unified approach to a model for various market securities like equity (Dow Jones Industrial Average-DJIA and NASDAQ-100), commodity markets (oil and gold) and Euro Forex rates. We also investigate the lagged and statistically causative relations of Twitter sentiments developed during active trading days and market inactive days in combination with the 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.