The improvement of investment approaches by developing and applying bootstrap methods to innovative evolutionary kernel-based subset time-series modelling.

通过开发引导方法并将其应用于创新的基于进化内核的子集时间序列建模来改进投资方法。

• 批准号: LP0883801
• 负责人: Prof Terence O'Neill
• 金额: $ 1.54万
• 依托单位: The Australian National University
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2009
• 资助国家: 澳大利亚
• 起止时间: 2009-07-30 至 2010-05-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP0883801
• 关键词: improvement; investment; approaches; developing; applying

With over $1 trillion of investors' monies in the hands of fund managers, the importance of efficient investment decisions across all industry sectors is self evident. Even if the modest target of systematically improving decision making by 1 or 2 % is set, the aggregate economic benefit achieved, given the compounding effects will be enormous. Any developed or developing country will profit from such advanced decision-making approaches. Therefore it is critical that more sophisticated statistical methods be established, and practical applications conducted, in order to advance the understanding of complexity management in the financial investment and other relevant sectors.

在基金经理手中，拥有超过1万亿美元的投资者款项，在所有行业领域有效投资决策的重要性是自我明显的。即使设定了系统地将决策制定的适度目标设定为1或2％，鉴于复合效应将是巨大的，实现了总体经济利益。任何发达国家或发展中国家都将从这种先进的决策方法中获利。因此，至关重要的是要建立更复杂的统计方法，并进行了实际应用，以促进对金融投资和其他相关部门中复杂性管理的理解。