论文标题
金融市场对象的多解决信号处理
Multiresolution Signal Processing of Financial Market Objects
论文作者
论文摘要
多分辨率分析在复杂系统及其动态的研究中都有许多学科的应用。金融市场是我们环境中最复杂的实体之一，但主流定量模型以预定规模运行，依靠线性相关措施，并难以认识非线性或因果结构。在本文中，我们结合了已知可捕获非线性关联的神经网络与多尺度分解，以促进对金融市场数据子结构的更好理解。量化使我们的分解在每个规模上都校准为市场。我们在七个用例的背景下说明了我们的方法。
Multiresolution analysis has applications across many disciplines in the study of complex systems and their dynamics. Financial markets are among the most complex entities in our environment, yet mainstream quantitative models operate at predetermined scale, rely on linear correlation measures, and struggle to recognize non-linear or causal structures. In this paper, we combine neural networks known to capture non-linear associations with a multiscale decomposition to facilitate a better understanding of financial market data substructures. Quantization keeps our decompositions calibrated to market at every scale. We illustrate our approach in the context of seven use cases.
