دانلود رایگان مقاله انگلیسی بازار سهام به عنوان شبکه زمانی - الزویر 2018

abstract

Financial networks have become extremely useful in characterizing the structures of complex financial systems. Meanwhile, the time evolution property of the stock markets can be described by temporal networks. We utilize the temporal network framework to characterize the time-evolving correlation-based networks of stock markets. The market instability can be detected by the evolution of the topology structure of the financial networks. We then employ the temporal centrality as a portfolio selection tool. Those portfolios, which are composed of peripheral stocks with low temporal centrality scores, have consistently better performance under different portfolio optimization frameworks, suggesting that the temporal centrality measure can be used as new portfolio optimization and risk management tool. Our results reveal the importance of the temporal attributes of the stock markets, which should be taken serious consideration in real life applications.

4. Conclusion

In conclusion, we have used the temporal network framework to analyze the temporal evolution of three major stock markets. The topology evolution of the correlation-based networks for three markets give some signals of corresponding financial turbulences in each market. With the help of temporal centrality measure, we can construct some risk diversified portfolios with high return and low risk. Under both the mean–variance and expected shortfall frameworks, the portfolios constructed with those peripheral stocks in both temporal and static centrality measures outperform those portfolios constructed with central stocks. Moreover, those peripheral portfolios selected with the guidance of temporal centrality measure performed way better than other portfolios(temporal central portfolios and aggregated peripheral portfolios) under both mean–variance and expected shortfall evaluation criterion. The in sample and out of sample tests have verified the robustness of the temporal peripheral portfolios. This is the first study to analyze the time evolving correlation-based networks with temporal network theory. The application of temporal centrality measure on portfolio selection has revealed the importance of the temporal attributes of the correlation-based networks of stock markets. Thus it should be quite interesting to investigate the temporal structure of the correlation-based networks with other tools developed for temporal network [16]. At the same instant, the investigation about the correlation-based networks of financial systems by using other tools from complex network theory, such as community detection [23,40–43] and network percolation theory [44], are also very promising directions. Those should subject to future researches.