带耦合时序网络、重要性节点识别及投资组合研究-----以股票市场为例
Study on Temporal Network with Coupling, Nodes Importance and Portfolio Optimization: A Case of Stock Market
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摘要: 时序网络可以更好地描述复杂网络中节点拓扑结构的动态演变. 考虑到节点在不同时间层的相互影响及多层网络中的层间时序关联耦合关系,本文提出了一种基于向量自回归VAR模型的带耦合时序网络,研究网络的构建过程及性质, 并将其应用于纳斯达克100、标普500、深证100和上证180四个股票市场的实证分析. 结果表明:与已有模型比如文献15及16相比,本文提出的带耦合时序网络模型无论在重要性节点识别的分辨率,还是投资组合的内样本及外样本表现上, 都具有明显的优势.同时本文还基于重要性节点序列探讨了``外围''股票的确定方法.这些研究可进一步丰富时序网络理论, 为金融市场研究提供新的技术工具.Abstract: Temporal network can better describe the dynamic evolution properties of topology construction among nodes in complex network. Taking into account the mutual influence of nodes in different time layers, and being inspired by inter-layer temporal correlation coupling relationship in multilayer network, this paper proposes a kind of temporal network with coupling based on vector autoregressive (VAR) model. How to construct temporal network is given and the empirical application in four stock markets including NDX100, S\&P 500, ZSSE100 and SSE180 is explored. Compared with the literature such as 15 and 16, the study shows that the proposed new network exhibits more obvious advantages on resolution power of nodes (stocks) importance ranking and the in-sample and out-sample performance of portfolio optimization. Meanwhile, this paper also discusses how to determine ``peripheral'' stocks on the basis of the nodes importance sequence. It is apparent that our study could further enrich temporal network theory and provide new technical tools for financial market research.