英文标题：
《A novel dynamic asset allocation system using Feature Saliency Hidden
Markov models for smart beta investing》
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作者：
Elizabeth Fons, Paula Dawson, Jeffrey Yau, Xiao-jun Zeng and John
Keane
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最新提交年份：
2019
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英文摘要：
The financial crisis of 2008 generated interest in more transparent, rules-based strategies for portfolio construction, with Smart beta strategies emerging as a trend among institutional investors. While they perform well in the long run, these strategies often suffer from severe short-term drawdown (peak-to-trough decline) with fluctuating performance across cycles. To address cyclicality and underperformance, we build a dynamic asset allocation system using Hidden Markov Models (HMMs). We test our system across multiple combinations of smart beta strategies and the resulting portfolios show an improvement in risk-adjusted returns, especially on more return oriented portfolios (up to 50$\\%$ in excess of market annually). In addition, we propose a novel smart beta allocation system based on the Feature Saliency HMM (FSHMM) algorithm that performs feature selection simultaneously with the training of the HMM, to improve regime identification. We evaluate our systematic trading system with real life assets using MSCI indices; further, the results (up to 60$\\%$ in excess of market annually) show model performance improvement with respect to portfolios built using full feature HMMs.
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中文摘要：
2008年的金融危机引发了人们对更透明、基于规则的投资组合构建策略的兴趣，智能贝塔策略正在成为机构投资者的一种趋势。虽然从长期来看，这些策略表现良好，但往往会出现严重的短期下降（从峰值到谷底的下降）以及周期间的波动。为了解决周期性和表现不佳的问题，我们使用隐马尔可夫模型（HMMs）构建了一个动态资产配置系统。我们对我们的系统进行了多个智能测试策略组合的测试，结果表明，风险调整后的投资组合回报率有所提高，尤其是在回报导向型投资组合上（每年超过市场50$\\%$）。此外，我们提出了一种基于特征显著性HMM（FSHMM）算法的智能beta分配系统，该算法在HMM训练的同时进行特征选择，以改进状态识别。我们使用摩根士丹利资本国际指数（MSCI Index）评估我们的系统交易系统和真实资产；此外，结果（每年超过市场60$\\%$）表明，相对于使用全功能HMM构建的投资组合，模型性能有所提高。
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分类信息：

一级分类：Computer Science 计算机科学
二级分类：Computational Engineering, Finance, and Science 计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Machine Learning 机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Quantitative Finance 数量金融学
二级分类：Portfolio Management 项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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