通过学习预测市场敏感性的指数跟踪
Index Tracking via Learning to Predict Market Sensitivities
摘要：
如今，指数基金受到投资者的青睐，而市场敏感度在管理指数基金方面至关重要。指数基金是一种共同基金，旨在跟踪预定义的市场指数（例如，S&P 500）。管理指数基金的一个基本策略是复制指数的成分和权重相同，但这是成本效益和不切实际的。为了解决这个问题，需要在准确预测市场敏感性的情况下部分复制指数。因此，我们提出了一种新的部分复制方法，通过学习预测市场的敏感性。我们首先研究了深度学习模型，通过我们的数据处理方法以监督的方式预测市场敏感性。然后，我们提出了一个部分指数跟踪优化模型，控制净预测的市场敏感性的投资组合和指数是相同的。这些过程的有效性得到了我们对韩国股票价格指数200的实验的证实。我们的实验表明，与历史估计和竞争跟踪误差复制指数利用不到一半的整个成分相比，预测误差显着减少。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，我们的投资组合构建方法是实际有效的。此外，据我们所知，这是第一项针对深度学习市场敏感性的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
