Portfolio optimization has been an area of research that has attracted a lot of attention from researchers and financial analysts. Designing an optimum portfolio is a complex task since it not only involves accurate forecasting of future stock returns and risks but also needs to optimize them. This paper presents a systematic approach to portfolio optimization using two approaches, the hierarchical risk parity algorithm and the Eigen portfolio on seven sectors of the Indian stock market. The portfolios are built following the two approaches to historical stock prices from Jan 1, 2016, to Dec 31, 2020. The portfolio performances are evaluated on the test data from Jan 1, 2021, to Nov 1, 2021. The backtesting results of the portfolios indicate that the performance of the HRP portfolio is superior to that of its Eigen counterpart on both training and test data for the majority of the sectors studied.
翻译：证券组合优化是一个研究领域,吸引了研究人员和金融分析家的极大关注。设计最佳证券组合是一项复杂的任务,因为它不仅涉及准确预测未来股票回报和风险,而且需要优化这些风险。本文件介绍了利用印度股票市场七个部门的等级风险平价算法和Eigen组合两种方法对证券组合进行系统优化的方法。证券组合是按照从2016年1月1日到2020年12月31日的历史股票价格两种方法构建的。组合绩效是根据从1月1日到2021年11月1日的测试数据进行评估的。组合的背测试结果表明,在大多数研究部门的培训和测试数据方面,人力资源组合的绩效优于Eigen对口单位。
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