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摘要翻译：
本文从信号处理的角度研究了具有有限数量资产的金融市场中的最优投资问题。我们研究了投资者应该如何在这些资产上分配资本，以及何时应该在这些资产上重新分配资金，以使在任何投资期间的累积财富最大化。特别地，我们引入了一个投资组合选择算法，使投资组合中的期望累积财富最大化。两种资产的离散时间市场，市场在买卖股票时征收比例交易费用。我们使用“阈值再平衡投资组合”来实现这一点，在这种情况下，只有当投资组合突破特定阈值时，才会进行交易。在Black-Scholes模型的相对价格序列服从对数正态分布的假设下，我们对比例交易费用下的期望财富进行了评估，找到了在任意投资周期内实现最大期望累积财富的阈值再平衡投资组合。我们的推导可以很容易地推广到有两个以上股票的市场，这些扩展在本文中被指出。正如从我们的推导中预测的那样，我们显著改善了从历史数据集上的文献中获得的资产组合选择算法。
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英文标题：
《Optimal Investment Under Transaction Costs: A Threshold Rebalanced
Portfolio Approach》
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作者：
Sait Tunc and Suleyman S. Kozat
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最新提交年份：
2012
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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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一级分类：Computer Science 计算机科学
二级分类：Systems and Control 系统与控制
分类描述：cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.
cs.sy是eess.sy的别名。本部分包括理论和实验研究，涵盖了自动控制系统的各个方面。本节主要介绍利用建模、仿真和优化工具进行控制系统分析和设计的方法。具体研究领域包括非线性、分布式、自适应、随机和鲁棒控制，以及混合和离散事件系统。应用领域包括汽车和航空航天控制系统、网络控制、生物系统、多智能体和协作控制、机器人学、强化学习、传感器网络、信息物理和能源相关系统的控制以及计算系统的控制。
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英文摘要：
We study optimal investment in a financial market having a finite number of assets from a signal processing perspective. We investigate how an investor should distribute capital over these assets and when he should reallocate the distribution of the funds over these assets to maximize the cumulative wealth over any investment period. In particular, we introduce a portfolio selection algorithm that maximizes the expected cumulative wealth in i.i.d. two-asset discrete-time markets where the market levies proportional transaction costs in buying and selling stocks. We achieve this using "threshold rebalanced portfolios", where trading occurs only if the portfolio breaches certain thresholds. Under the assumption that the relative price sequences have log-normal distribution from the Black-Scholes model, we evaluate the expected wealth under proportional transaction costs and find the threshold rebalanced portfolio that achieves the maximal expected cumulative wealth over any investment period. Our derivations can be readily extended to markets having more than two stocks, where these extensions are pointed out in the paper. As predicted from our derivations, we significantly improve the achieved wealth over portfolio selection algorithms from the literature on historical data sets.
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PDF链接：
https://arxiv.org/pdf/1203.4156
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