英文标题：
《Diversity and Sparsity: A New Perspective on Index Tracking》
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作者：
Yu Zheng and Timothy M. Hospedales and Yongxin Yang
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最新提交年份：
2020
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英文摘要：
We address the problem of partial index tracking, replicating a benchmark index using a small number of assets. Accurate tracking with a sparse portfolio is extensively studied as a classic finance problem. However in practice, a tracking portfolio must also be diverse in order to minimise risk -- a requirement which has only been dealt with by ad-hoc methods before. We introduce the first index tracking method that explicitly optimises both diversity and sparsity in a single joint framework. Diversity is realised by a regulariser based on pairwise similarity of assets, and we demonstrate that learning similarity from data can outperform some existing heuristics. Finally, we show that the way we model diversity leads to an easy solution for sparsity, allowing both constraints to be optimised easily and efficiently. we run out-of-sample backtesting for a long interval of 15 years (2003 -- 2018), and the results demonstrate the superiority of the proposed algorithm.
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中文摘要：
我们解决部分索引跟踪问题，使用少量资产复制基准索引。稀疏投资组合的精确跟踪是一个经典的金融问题。然而，在实践中，为了将风险降至最低，跟踪投资组合也必须多样化——这一要求以前只通过特殊方法处理过。我们介绍了第一种索引跟踪方法，该方法在单个联合框架中显式优化了多样性和稀疏性。多样性是通过基于资产成对相似性的正则化器实现的，我们证明了从数据中学习相似性可以优于一些现有的启发式算法。最后，我们展示了我们对多样性建模的方式可以很容易地解决稀疏性问题，从而可以轻松有效地优化这两个约束。我们在长达15年的时间间隔内（2003-2018年）进行了样本回溯测试，结果证明了该算法的优越性。
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分类信息：

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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