论文标题
通过QUBO和数字退火通过市场图聚类
Market Graph Clustering Via QUBO and Digital Annealing
论文作者
论文摘要
我们的目标是找到市场图的代表节点，该节点可以最好地复制更广泛的市场图（INDEX）的回报，这是金融行业中的常见任务。我们将参考索引建模为市场图形，并以二次K-Medoids形式表达索引跟踪问题。我们利用了富裕的硬件架构，即富士通数字灭火器，以避免问题的NP牢固性质，并有效地解决我们的配方。在本文中，我们结合了文献的三个独立领域，市场图，K-Medoid聚类和二进制二进制优化模型，以将索引跟踪问题作为二次K-Medoid clagrustering问题提出。我们的最初结果表明，我们仅使用其组成资产的一小部分来准确地复制广泛市场指数的回报。此外，我们的二次配方使我们能够利用最新的硬件进步，克服问题的NP坚强性质。
Our goal is to find representative nodes of a market graph that best replicate the returns of a broader market graph (index), a common task in the financial industry. We model our reference index as a market graph and express the index tracking problem in a quadratic K-medoids form. We take advantage of a purpose built hardware architecture, the Fujitsu Digital Annealer, to circumvent the NP-hard nature of the problem and solve our formulation efficiently. In this article, we combine three separate areas of the literature, market graph models, K-medoid clustering and quadratic binary optimization modeling, to formulate the index-tracking problem as a quadratic K-medoid graph-clustering problem. Our initial results show we accurately replicate the returns of a broad market index, using only a small subset of its constituent assets. Moreover, our quadratic formulation allows us to take advantage of recent hardware advances, to overcome the NP-hard nature of the problem.
