论文标题
通过学习预测市场敏感性的索引跟踪
Index Tracking via Learning to Predict Market Sensitivities
论文作者
论文摘要
当今的投资者基本上首选指数资金，市场敏感性在管理指数资金方面起了重要作用。指数基金是一个共同基金，旨在跟踪预定义市场指数的回报（例如标准普尔500指数）。管理索引基金的基本策略是复制指数的组成部分和权重，但是，成本友善和不切实际。为了解决这个问题，需要以准确预测的市场敏感性部分复制指数。因此，我们通过学习预测市场敏感性提出了一种新型的部分复制方法。我们首先使用我们的数据处理方法研究了深入学习模型，以监督的方式预测市场敏感性。然后，我们提出了一个控制投资组合和索引的净预测市场敏感性的部分索引跟踪优化模型是相同的。我们对韩国股票价格指数200的实验证实了这些过程的功效。我们的实验显示，与历史估计和竞争性跟踪错误相比，使用少于一半的整个组成部分相比，预测错误显着降低。因此，我们表明，应用深度学习来预测市场敏感性是有希望的，并且我们的投资组合构建方法实际上是有效的。此外，据我们所知，这是针对市场敏感性的第一个研究，重点是深度学习。
Index funds are substantially preferred by investors nowadays, and market sensitivities are instrumental in managing index funds. An index fund is a mutual fund aiming to track the returns of a predefined market index (e.g., the S&P 500). A basic strategy to manage an index fund is replicating the index's constituents and weights identically, which is, however, cost-ineffective and impractical. To address this issue, it is required to replicate the index partially with accurately predicted market sensitivities. Accordingly, we propose a novel partial-replication method via learning to predict market sensitivities. We first examine deep-learning models to predict market sensitivities in a supervised manner with our data-processing methods. Then, we propose a partial-index-tracking optimization model controlling the net predicted market sensitivities of the portfolios and index to be the same. These processes' efficacy is corroborated by our experiments on the Korea Stock Price Index 200. Our experiments show a significant reduction of the prediction errors compared with historical estimations and competitive tracking errors of replicating the index utilizing fewer than half of the entire constituents. Therefore, we show that applying deep learning to predict market sensitivities is promising and that our portfolio construction methods are practically effective. Additionally, to our knowledge, this is the first study addressing market sensitivities focused on deep learning.
