Title: Discovering Bayesian Market Views for Intelligent Asset Allocation,
Abstract: Along with the advance of opinion mining techniques, public mood has been
found to be a key element for stock market prediction. However, how market
participants' behavior is affected by public mood has been rarely discussed.
Consequently, there has been little progress in leveraging public mood for the
asset allocation problem, which is preferred in a trusted and interpretable
way. In order to address the issue of incorporating public mood analyzed from
social media, we propose to formalize public mood into market views, because
market views can be integrated into the modern portfolio theory. In our
framework, the optimal market views will maximize returns in each period with a
Bayesian asset allocation model. We train two neural models to generate the
market views, and benchmark the model performance on other popular asset
allocation strategies. Our experimental results suggest that the formalization
of market views significantly increases the profitability (5% to 10% annually)
of the simulated portfolio at a given risk level.