Complexity, Risk Management and Dynamic Portfolio Selection in Investment Management using Advances in Evolutionary Parallel-computing Artificial Intelligence

利用进化并行计算人工智能的进步，研究投资管理中的复杂性、风险管理和动态投资组合选择

• 批准号: DP0770081
• 负责人: Prof Timothy Brailsford
• 金额: $ 15.05万
• 依托单位: Bond University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2007
• 资助国家: 澳大利亚
• 起止时间: 2007-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0770081
• 关键词: Complexity; Risk; Management; Dynamic; Portfolio

With over $1 trillion of investors' monies in the hands of fund managers, the health of the Australian economy is critically dependent on the investment decisions of these managers. However, the majority of the funds are invested in risky assets with histories of volatile price movements about which we do not possess a deep understanding. This project draws upon a set of inter-disciplinary concepts and models centred in neural networks that allow for learning over time to advance our understanding of complexity, leading to superior quantitative tools and models to allow for improved decision-making in respect of risk management and asset allocation.

基金经理手中有超过1万亿美元的投资者资金，澳大利亚经济的健康状况严重依赖于这些基金经理的投资决策。然而，大多数基金投资于具有价格波动历史的风险资产，而我们对这些资产并没有深入了解。该项目借鉴了一套以神经网络为中心的跨学科概念和模型，这些概念和模型允许随着时间的推移学习，以提高我们对复杂性的理解，从而产生卓越的定量工具和模型，从而改进风险管理和资产配置方面的决策。