摘要翻译：
恒定再平衡投资组合是一种资产配置算法，它在一段时间内保持财富在一组资产之间的相同分配。最近，有研究在线投资组合选择算法的工作，这些算法与事后确定的最佳恒定再平衡投资组合具有竞争力。根据它们的本质，这些算法采用了一个假设，即使用固定资产配置策略可以获得高回报。然而，股票市场远不是一成不变的，在许多情况下，不断重新平衡的投资组合所获得的财富远小于适应市场变化的临时投资策略所获得的财富。在本文中，我们提出了一个有效的贝叶斯投资组合选择算法，能够跟踪变化的市场。我们还描述了该算法在一般交易费用情况下的简单推广，包括Blum和Kalai最近研究的交易费用模型。我们对算法的竞争力进行了简单的分析，并在纽约证券交易所22年来积累的真实股票数据上检验了算法的性能。
---
英文标题：
《Switching Portfolios》
---
作者：
Yoram Singer
---
最新提交年份：
2013
---
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
--
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。
--

---
英文摘要：
  A constant rebalanced portfolio is an asset allocation algorithm which keeps the same distribution of wealth among a set of assets along a period of time. Recently, there has been work on on-line portfolio selection algorithms which are competitive with the best constant rebalanced portfolio determined in hindsight. By their nature, these algorithms employ the assumption that high returns can be achieved using a fixed asset allocation strategy. However, stock markets are far from being stationary and in many cases the wealth achieved by a constant rebalanced portfolio is much smaller than the wealth achieved by an ad-hoc investment strategy that adapts to changes in the market. In this paper we present an efficient Bayesian portfolio selection algorithm that is able to track a changing market. We also describe a simple extension of the algorithm for the case of a general transaction cost, including the transactions cost models recently investigated by Blum and kalai. We provide a simple analysis of the competitiveness of the algorithm and check its performance on real stock data from the New York Stock Exchange accumulated during a 22-year period.
---
PDF链接：
https://arxiv.org/pdf/1301.7413