In financial markets, investors grapple with selecting the optimal subset of assets from a vast range of options. This is a challenging task because the system changes dynamically depending on various different factors. This study aims to identify an optimal subset of assets by ensuring diversity through company description and price behavior over time, which are two important characteristics of assets. We introduce the Investment Universe Complex Network (InUCoN) framework, integrating these aspects to address previous limitations. The main strategy of InUCoN is to treat the investment universe as a complex system, model it with a complex network, and analyze it using this model. The framework has three components: (i) dynamic network generation; (ii) snapshot aggregation; and (iii) investment universe filtering. The experiments on the 200 stocks with the highest average trading volume from the S&P 500 demonstrate that InUCoN effectively reduced risk by selecting a more independent set of stocks, as evidenced by improved portfolio performance metrics.
