Predicting Financial Markets Using The Wisdom of Crowds

In the world of finance, one key lesson is the importance of psychology in the behavior of financial markets. Many investors are irrationally exuberant when making financial decisions, but predictive analytics can generate insights that are free of investors’ emotions, and hence human irrational exuberance in decision-making can be mitigated. Data sources that investors adopt in their investment decision-making are, in most cases, traditional – including quarterly earnings reports and financial statements. In this work, we propose a predictive analytics framework that aims at mining insights from two alternative data sources: news articles and micro-blogs. We investigate the predictive correlation and causation between (1) collective opinion mining in news articles fused with Twitter mood and (2) movements in financial markets. Experimental results indicate a relationship between stock market prices and collective opinion mining variations on news articles combined with Twitter’s sentiment variations. The framework introduced in this work could potentially be adopted as a supplement to the conventional analyses being used in major investment banks. This research was partially funded by the Australian government under the Awards-Endeavour research grant.