深成指数时间序列的网络拓扑结构研究

以2000年~2010年中国股市深圳成指的收盘价数据为依据，利用可见图方法将指数时间序列转化为复杂网络，计算和分析了复杂网络的拓扑结构指标，发现该网络具有典型的小世界特征、无标度特征和自相似性，并针对网络所呈现的特征进行了深入解释.研究发现，将金融时间序列转化为复杂网络能够从一个新的视角揭示金融系统复杂性及其内在的结构规律，为预测金融市场提供了新的思路.

Network Topology of Shenzhen Component Index Time Series

Based on the closing price data of Shenzhen Component Index in Chinese stock market from 2000 to 2010, it turned the component index time series into complex networks by the visible diagram method, calculates, analyzed these complex networks' topological structure index, and found that these networks have the typical network characteristics of a small world and self-similarity, being scale-free. Finally, it gave an explanation in detail of this phenomenon. The study finds that a new perspective to explore the complexity of the financial system and its internal structure can be acquired by turning the financial time series into complex networks, which provides a new way of thinking to predict financial markets.