英文标题：
《Threshold-Based Portfolio: The Role of the Threshold and Its
  Applications》
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作者：
Sang Il Lee, Seong Joon Yoo
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最新提交年份：
2018
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英文摘要：
  This paper aims at developing a new method by which to build a data-driven portfolio featuring a target risk-return. We first present a comparative study of recurrent neural network models (RNNs), including a simple RNN, long short-term memory (LSTM), and gated recurrent unit (GRU) for selecting the best predictor to use in portfolio construction. The models are applied to the investment universe consisted of ten stocks in the S&P500. The experimental results shows that LSTM outperforms the others in terms of hit ratio of one-month-ahead forecasts. We then build predictive threshold-based portfolios (TBPs) that are subsets of the universe satisfying given threshold criteria for the predicted returns. The TBPs are rebalanced monthly to restore equal weights to each security within the TBPs. We find that the risk and return profile of the realized TBP represents a monotonically increasing frontier on the risk-return plane, where the equally weighted portfolio (EWP) of all ten stocks plays a role in their lower bound. This shows the availability of TBPs in targeting specific risk-return levels, and an EWP based on all the assets plays a role in the reference portfolio of TBPs. In the process, thresholds play dominant roles in characterizing risk, return, and the prediction accuracy of the subset. The TBP is more data-driven in designing portfolio target risk and return than existing ones, in the sense that it requires no prior knowledge of finance such as financial assumptions, financial mathematics, or expert insights. In a practical application, we present the TBP management procedure for a time horizon extending over multiple time periods; we also discuss their application to mean-variance portfolios to reduce estimation risk.
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中文摘要：
本文旨在开发一种新的方法来构建一个以目标风险回报为特征的数据驱动投资组合。我们首先对递归神经网络模型（RNN）进行了比较研究，包括一个简单的RNN、长-短期记忆（LSTM）和选通递归单元（GRU），以选择用于投资组合构建的最佳预测因子。这些模型适用于由标准普尔500指数中的十只股票组成的投资领域。实验结果表明，LSTM在提前一个月预测的命中率方面优于其他方法。然后，我们构建基于预测阈值的投资组合（TBP），这些投资组合是宇宙的子集，满足预测回报的给定阈值标准。TBP每月重新平衡，以恢复TBP内各证券的同等权重。我们发现，已实现TBP的风险和回报曲线代表了风险回报平面上单调增长的前沿，其中所有十只股票的等权投资组合（EWP）在其下限中起作用。这表明了TBP在针对特定风险回报水平方面的可用性，并且基于所有资产的EWP在TBP的参考投资组合中发挥着作用。在此过程中，阈值在表征风险、回报和子集预测精度方面起着主导作用。TBP在设计投资组合目标风险和回报方面比现有的更为数据驱动，因为它不需要金融方面的先验知识，如金融假设、金融数学或专家见解。在实际应用中，我们提出了跨越多个时间段的时间范围的TBP管理程序；我们还讨论了它们在均值-方差投资组合中的应用，以降低估计风险。
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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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