作者：Álvaro Rubio-García Samuel Fernández-Lorenzo Juan José García-Ripoll Diego Porras
从长远来看，积极管理的投资组合几乎永远不会战胜市场。因此，许多投资者经常求助于被动管理的投资组合，其目的是遵循特定的金融指数。建立这种被动投资组合以最大限度地降低交易成本的任务被称为指数跟踪（IT），其目标是通过只持有指数中的一小部分资产来跟踪指数。因此，这是一个NP难问题，对于资产超过100的指数来说，精确求解是不可行的。在这项工作中，我们提出了一种新的混合模拟退火方法，该方法可以有效地解决大指数的IT问题，并且具有足够的灵活性来适应财务相关的约束。通过跟踪2011年至2018年期间的标准普尔500指数，我们表明我们的算法能够在过去回报的样本期内找到最优解，并且可以调整以在未来回报的样本外期内提供最优回报。最后，我们将重点放在
An actively managed portfolio almost never beats the market in the long term. Thus, many investors often resort to passively managed portfolios whose aim is to follow a certain financial index. The task of building such passive portfolios aiming also to minimize the transaction costs is called Index Tracking (IT), where the goal is to track the index by holding only a small subset of assets in the index. As such, it is an NP-hard problem and becomes unfeasible to solve exactly for indices with more than 100 assets. In this work, we present a novel hybrid simulated annealing method that can efficiently solve the IT problem for large indices and is flexible enough to adapt to financially relevant constraints. By tracking the S&P-500 index between the years 2011 and 2018 we show that our algorithm is capable of finding optimal solutions in the in-sample period of past returns and can be tuned to provide optimal returns in the out-of-sample period of future returns. Finally, we focus on the task of holding an IT portfolio during one year and rebalancing the portfolio every month. Here, our hybrid simulated annealing algorithm is capable of producing financially optimal portfolios already for small subsets of assets and using reasonable computational resources, making it an appropriate tool for financial managers.
论文链接：http://arxiv.org/pdf/2303.13282v1
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