Modelling cost into a genetic algorithm-based portfolio optimization system by seeding and objective sharing
Modelling cost into a genetic algorithm-based portfolio optimization system by seeding and objective sharing
复制标题
通过种子和目标共享将成本建模为基于遗传算法的投资组合优化系统
DOI：
10.1109/cec.2007.4424472
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
H. Iba
中科院分区：
文献类型：
--
作者：
C. Aranha;H. Iba
用遗传算法进行投资组合优化是一个最近受到广泛关注的问题。然而，迄今为止，该领域的大多数工作都忽视了成本对投资组合优化的影响，并且没有直接解决投资组合管理（投资组合随着时间的推移而持续优化）的问题。在这项工作中，我们使用在两个连续的时间段的投资组合选择之间的欧氏距离作为衡量成本，和目标共享方法来平衡的目标，最大化的回报和最小化的距离随着时间的推移。我们还通过将以前运行的遗传物质添加到新的种群（播种）来改进GA方法。我们对NASDAQ和NIKKEI指数的历史月度数据进行了实验，得到了比纯GA更好的结果，在非泡沫市场条件下击败了指数。
Portfolio optimization by GA is a problem that has recently received a lot of attention. However, most works in this area have so far ignored the effects of cost on Portfolio Optimization, and haven't directly addressed the problem of portfolio management (continuous optimization of a portfolio over time). In this work, we use the Euclidean Distance between the portfolio selection in two consecutive time periods as measure of cost, and the objective sharing method to balance the goals of maximizing returns and minimizing distance over time. We also improve the GA method by adding genetic material from previous runs into the new population (seeding). We experiment our method on historical monthly data from the NASDAQ and NIKKEI indexes, and obtain a better result than pure GA, defeating the index under non-bubble market conditions.