Judgment on Short Term Trends and Portfolio Choices Optimization

doi:10.12005/orms.2018.0245

Abstract

Abstract: This paper aims to devise a method that utilizes the very limited predictability of stock index dynamics and the price fluctuations of individual stocks, such that the available data of technical variables can be employed to add analytical values for portfolio choices optimization. It studies a dynamic portfolio selection approach integrated with short-term trend forecasts both at stock index level using Hurst exponent and that of individual stocks using random forest model. By virtue of the predicted trend of stock index and the marginal return of risk defined, we propose a rule of time window selection while optimizing portfolio choices. Tested with the data in Shanghai-Shenzhen 300 index component stocks, as well as in A-stocks, the approach of dynamic portfolio selection considering short-term forecasts at multiple levels and the rules for time window selection, demonstrates that it effectively improves portfolio performance, and therefore contributes as an effective tool for risk management of stock equities.

Key words: portfolio selection, short-term foresight, Hurst exponent, random forest

摘要： 针对股市非常有限的可预测性和投资组合优化时间窗口的选择问题,本文从股指趋势和个股收益率趋势类别两个层次上进行前瞻分析,并依据股指趋势判断和不同时间窗口对应的边际风险收益,提出投资组合优化计算的时间窗口选择规则,动态地进行资产组合优化。通过沪深A股及沪深300成份股两个样本集合的应用研究表明,考虑多层次短期趋势预测的动态投资组合选择方法能有效地改善资产组合绩效,是股市资产风险管理的有效方法之一。

关键词: 投资组合选择, 短期趋势预判, Hurst指数, 随机森林

CLC Number:

HUANG Dong-bin, SUN Hong-wei, ZUO Chuan-chang. Judgment on Short Term Trends and Portfolio Choices Optimization[J]. Operations Research and Management Science, 2018, 27(10): 164-173.

黄东宾, 孙宏伟, 左传长. 短期趋势判断与投资组合优选[J]. 运筹与管理, 2018, 27(10): 164-173.

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