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Quantitative Finance > Computational Finance

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, in what manner the market participants are affected by public mood has been rarely discussed. As a result, there has been little progress in leveraging public mood for the asset allocation problem, as the application 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 it into market views that can be integrated into the modern portfolio theory. In this 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 performance of our model using market views on other popular asset allocation strategies. Our experimental results suggest that the formalization of market views significantly increases the profitability (5% to 10%) of the simulated portfolio at a given risk level.
Comments: 15 pages
Subjects: Computational Finance (q-fin.CP); Artificial Intelligence (cs.AI)
Cite as: arXiv:1802.09911 [q-fin.CP]
  (or arXiv:1802.09911v1 [q-fin.CP] for this version)

Submission history

From: Frank Z. Xing [view email]
[v1] Tue, 27 Feb 2018 14:37:54 GMT (2172kb,D)