基于启发式遗传算法的指数追踪组合构建策略

消极组合管理方法已由国内外众多基金的表现证明是一种有效的资产组合投资方式. 指数基金作为采取消极管理策略的典型代表, 其业绩超越多数采取积极管理模式的基金. 指数基金管理者的主要目标是使其基金的收益尽可能接近其标的股指, 如我国的沪深300, 美国的标普500的收益. 本文提出了一种基于启发式遗传算法的寻优方案, 通过最大化效用函数来寻找一个最为经济的指数复制组合. 该组合同时应该满足拥有最少的资产数量、尽可能少的权重调整次数、最小的收益波动性等限制条件以减少基金开销, 并使其收益尽量接近或者超越标的指数的收益. 为使该策略具有更强的实用性, 文章考虑了股票具有最小交易规模、投资权重分布不平均等实际限制. 实验所得策略通过构造追踪组合来匹配沪深300指数, 其综合效果超过了使用二次规划、等权或者是先验经验构筑的投资组合.

Heuristic genetic algorithm for optimizing an index tracking portfolio

Passive portfolio management has been proved as an efficient method to get the average market return and can outperform most of funds which are managed actively. Index fund is one of typical passively managed funds. Its major objective is to mimic the performance of a benchmark stock index, i.e. S&P500 in US and CIS300 in China. In order to reduce the cost of transaction by limiting the number of rebalancing and unnecessary spending on less influential stock, this paper aims at proposing a heuristic genetic algorithm that could help to build a tracking portfolio with the help of a tailored utility function. The portfolio contains fewer stocks than the tracked index, while has adequate accuracy and lower costs. The technique is applied on building a stock portfolio which tracked the performance of CIS300, with a positive comprehensive output which outperforms tracking portfolio built by quadratic programming, equally weighted or experience guided models.