Using the topological structure of financial networks to build a portfolio has attracted a wide range of research interests. A similarity matrix based on the technical indicators (TIs), and a correlation matrix based on the stock returns, are used to construct the financial networks. Hybrid topological measures are calculated for both networks to select the stocks that are further used to build portfolios. The random matrix theory (RMT) tool is used to filter the risk measurement in the Markowitz optimization process. A general result is presented in this study, which indicates that the portfolios composed of peripheral stocks consistently outperform those composed of central stocks. As shown in the empirical results, the RMT method can improve the Markowitz optimization process and further improve the performance of portfolios. By comparing the different portfolios established under different networks, the results reveal the advantage of the topological measure based on TIs. This study provides an important approach for constructing financial networks from the time series data, and it explores the impact of different similarity measurements on the network-based models for portfolio selection.

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