英文标题：
《Robust Asset Allocation for Robo-Advisors》
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作者：
Thibault Bourgeron, Edmond Lezmi, Thierry Roncalli
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最新提交年份：
2019
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英文摘要：
  In the last few years, the financial advisory industry has been impacted by the emergence of digitalization and robo-advisors. This phenomenon affects major financial services, including wealth management, employee savings plans, asset managers, etc. Since the robo-advisory model is in its early stages, we estimate that robo-advisors will help to manage around $1 trillion of assets in 2020 (OECD, 2017). And this trend is not going to stop with future generations, who will live in a technology-driven and social media-based world. In the investment industry, robo-advisors face different challenges: client profiling, customization, asset pooling, liability constraints, etc. In its primary sense, robo-advisory is a term for defining automated portfolio management. This includes automated trading and rebalancing, but also automated portfolio allocation. And this last issue is certainly the most important challenge for robo-advisory over the next five years. Today, in many robo-advisors, asset allocation is rather human-based and very far from being computer-based. The reason is that portfolio optimization is a very difficult task, and can lead to optimized mathematical solutions that are not optimal from a financial point of view (Michaud, 1989). The big challenge for robo-advisors is therefore to be able to optimize and rebalance hundreds of optimal portfolios without human intervention. In this paper, we show that the mean-variance optimization approach is mainly driven by arbitrage factors that are related to the concept of hedging portfolios. This is why regularization and sparsity are necessary to define robust asset allocation. However, this mathematical framework is more complex and requires understanding how norm penalties impacts portfolio optimization. From a numerical point of view, it also requires the implementation of non-traditional algorithms based on ADMM methods.
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中文摘要：
在过去几年中，金融咨询行业受到了数字化和机器人顾问的影响。这一现象影响到主要的金融服务，包括财富管理、员工储蓄计划、资产管理等。由于机器人咨询模式尚处于早期阶段，我们估计机器人顾问将在2020年帮助管理约1万亿美元的资产（OECD，2017）。这一趋势不会随着子孙后代而停止，他们将生活在一个技术驱动和社交媒体为基础的世界。在投资行业，robo advisors面临着不同的挑战：客户分析、定制、资产池、负债约束等。从其基本意义上讲，robo advisory是一个定义自动投资组合管理的术语。这包括自动交易和再平衡，但也包括自动投资组合分配。最后一个问题无疑是未来五年机器人咨询业面临的最重要挑战。今天，在许多机器人顾问中，资产配置是基于人的，而不是基于计算机的。原因是投资组合优化是一项非常困难的任务，可能会导致优化的数学解决方案从财务角度来看不是最优的（Michaud，1989）。因此，机器人顾问面临的最大挑战是能够在没有人为干预的情况下优化和重新平衡数百个最佳投资组合。在本文中，我们证明了均值-方差优化方法主要由与对冲投资组合概念相关的套利因素驱动。这就是为什么需要正则化和稀疏性来定义稳健的资产配置。然而，这个数学框架更为复杂，需要了解定额惩罚如何影响投资组合优化。从数值的角度来看，它还需要实现基于ADMM方法的非传统算法。
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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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