英文标题：
《Discovering Bayesian Market Views for Intelligent Asset Allocation》
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作者：
Frank Z. Xing and Erik Cambria and Lorenzo Malandri and Carlo
  Vercellis
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最新提交年份：
2018
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英文摘要：
  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.
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中文摘要：
随着意见挖掘技术的进步，公众情绪已被发现是股市预测的关键因素。然而，公众情绪如何影响市场参与者的行为却鲜有讨论。因此，在利用公众情绪解决资产配置问题方面进展甚微，这是一种值得信赖和可解释的方式。为了解决从社交媒体分析公众情绪的问题，我们建议将公众情绪形式化为市场观点，因为市场观点可以整合到现代投资组合理论中。在我们的框架中，最优市场观点将通过贝叶斯资产配置模型在每个时期实现收益最大化。我们训练两个神经模型来生成市场视图，并在其他流行的资产配置策略上对模型性能进行基准测试。我们的实验结果表明，在给定的风险水平下，市场观点的形式化显著提高了模拟投资组合的盈利能力（每年5%-10%）。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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