论文标题
Nifty 50股票的分层风险平价和最小差异投资组合设计
Hierarchical Risk Parity and Minimum Variance Portfolio Design on NIFTY 50 Stocks
论文作者
论文摘要
投资组合设计和优化一直是研究领域，它吸引了金融领域的研究人员的很多关注。设计最佳投资组合是一项复杂的任务，因为它涉及对未来股票收益和风险的准确预测，并在它们之间进行合适的权衡。本文提出了一种使用两种算法，关键线算法和印度股票市场八个领域的层次风险奇偶校验算法设计投资组合的系统方法。虽然该投资组合是使用2016年1月1日至2020年12月31日的股票价格数据设计的，但对它们进行了对2021年1月1日至2021年8月26日的数据进行测试。投资组合的回测结果表明，CLA算法的性能在培训数据上较高，而HRP AlgorithM对Cla Algorith的测试却很出色。
Portfolio design and optimization have been always an area of research that has attracted a lot of attention from researchers from the finance domain. Designing an optimum portfolio is a complex task since it involves accurate forecasting of future stock returns and risks and making a suitable tradeoff between them. This paper proposes a systematic approach to designing portfolios using two algorithms, the critical line algorithm, and the hierarchical risk parity algorithm on eight sectors of the Indian stock market. While the portfolios are designed using the stock price data from Jan 1, 2016, to Dec 31, 2020, they are tested on the data from Jan 1, 2021, to Aug 26, 2021. The backtesting results of the portfolios indicate while the performance of the CLA algorithm is superior on the training data, the HRP algorithm has outperformed the CLA algorithm on the test data.
