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PORTFOLIO OPTIMIZATION USING MULTI-CRITERIA DECISION ANALYSIS AND MACHINE LEARNING

使用多标准决策分析和机器学习进行投资组合优化

基本信息

批准号：
10680441
负责人：
NAKAYAMA Hirotaka
金额：
$ 1.79万
依托单位：
KONAN UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (C)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

项目摘要

One of main features in financial investment problems is that the situation changes very often over time. In applying machine learning techniques under this circumstance, in particular, it has been observed that additional learning plays an effective role. However, since the rule for classification becomes more and more complex with only additional learning, some appropriate forgetting is also necessary. It seems natural that many data are forgotten as the time elapses. We call the way of forgetting based only on the time elapse "passive forgetting". On the other hand, it is expected more effective to forget unnecessary data actively. We call this way of forgetting "unnecessary data" actively "active forgetting". In this research, several ways for active forgetting in machine leaning have been developed and applied to stock portfolio problems. As a result, it has been shown that active forgetting provides better results than mere additional learning or passive forgetting. It can be exp … More ected that an effective decision support system for portfolio problems can be obtained by applying some of multi-objective programming techniques (e.g., Satisficing Trade-off Method developed by the author) to candidate stocks which are selected by machine learning with active forgetting.In the first year of the research term, additional learning and passive forgetting in RBF networks was developed. Through numerical experiments, it was shown that this new technology works effectively in stock portfolio problems.In the next year of the research term, rule extraction was tried by using the rough set theory. Although many machine learning techniques such as artificial neural networks can provide good results, they are not transparent (i.e., of black box). In many actual situations, people want to see how the prediction was made. To this end, extraction of explicit rules is needed. It was shown that the rough set theory can work effectively for this purpose.In the last year of the research term, active forgetting was developed. Applying active forgetting in the potential method, remarkably beneficial results were obtained in stock portfolio problems. On the basis of the obtained results, a decision support system for stock portfolio is on trial to combine the above machine learning techniques and multi-objective programming techniques. Less

金融投资问题的一个主要特征是，随着时间的推移，情况往往会发生变化。特别是，在这种情况下应用机器学习技术时，已经观察到额外的学习发挥了有效的作用。然而，由于分类规则变得越来越复杂，只有额外的学习，一些适当的遗忘也是必要的。随着时间的推移，许多数据被遗忘似乎是很自然的。我们把这种只基于时间流逝的遗忘方式称为“被动遗忘”。另一方面，主动忘记不必要的数据预计会更有效。我们把这种主动遗忘“不必要数据”的方式称为“主动遗忘”。在本研究中，我们发展了几种机器学习中主动遗忘的方法，并将其应用于股票组合问题。结果表明，主动遗忘比单纯的额外学习或被动遗忘提供了更好的结果。可以是EXP…进一步指出，将一些多目标规划技术(如作者开发的满足度权衡方法)应用于通过主动遗忘的机器学习选择的候选股票，可以获得有效的投资组合问题决策支持系统。通过数值实验，验证了该方法在股票投资组合问题中的有效性，并在下一年的研究中尝试了利用粗糙集理论进行规则提取。虽然许多机器学习技术，如人工神经网络，可以提供良好的结果，但它们不是透明的(即，黑匣子)。在许多实际情况下，人们想看看这个预测是如何做出的。为此，需要提取明确的规则。研究表明，粗糙集理论可以有效地解决这一问题。在研究的最后一年，发展了主动遗忘。将主动遗忘方法应用于势法中，在股票投资组合问题中取得了显著的有益效果。在此基础上，将上述机器学习技术与多目标规划技术相结合，试制了一个股票投资组合决策支持系统。较少