基于分解的神经动力学,用于投资组合管理,在交易成本下进行风险和利的权衡
Xinwei Cao1, Junchao Lou2, Bolin Liao3
1School of Business, Jiangnan University, Wuxi, China.
概括
本研究介绍了一种新的动态神经网络,用于优化高频交易 (HFT) 投资组合管理. 该方法简化了复杂的马尔科维茨模型计算,减少了计算负载,以实现更快,更有效的实时交易决策.
科学领域:
- 计算金融是指计算金融.
- 金融中的机器学习
- 量化交易是指数量化交易.
背景情况:
- 马尔科维茨模型是投资组合优化的基石,通常用受约束的二次编程问题来解决.
- 实时在线优化对于高频交易 (HFT) 来说至关重要,但传统方法与马尔科维茨模型的复杂约束作斗争.
- 对于马尔科维茨模型的现有数值解决方案,对于HFT的速度和效率要求提出了计算挑战.
研究的目的:
- 在HFT投资组合管理中开发一种高效的计算方法,用于实时在线优化.
- 解决传统的数值方法的局限性,以解决马尔科维茨模型对HFT的受约束二次编程任务.
- 提出一种新的方法,将模型简化与动态神经网络相结合,以提高计算性能.
主要方法:
- 马尔科维茨问题的分解成分析上可解决的和不可解决的组成部分.
- 开发一个创新的动态神经网络,快速解决计算密集型部分的问题.
- 理论分析和证明建议方法的最佳性和全球趋同.
主要成果:
- 拟议的方法显著降低了计算负载,使其适合实时HFT计算.
- 使用广泛的股票数据,包括道斯工业平均线 (DJIA) 的数值实验验证实了该方法的有效性.
- 在DJIA库存数据实验中,与标准的MATLAB quadprog () 解决器相比,总成本降低了5.54%.
结论:
- 新的动态神经网络方法为HFT的实时投资组合优化提供了有效的解决方案.
- 这种方法克服了与高频交易中的传统马科维茨模型解决方案相关的计算瓶.
- 经过验证的有效性证明了其作为提高HFT战略绩效的有价值工具的潜力.
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