علي تشكيني

Start-up businesses play an essential role in the development of a society's economy, increase in production and job creation. Various factors are effective in the expansion of these businesses at the micro and macro levels, one of the most important of which is raising the required fund at different stages of business development. The prerequisite for the effectiveness of this important factor is to understand as much as possible the investment process and the appropriateness of investors. In this research, after reviewing the literature in the field of investment behavior analysis, it has been tried to achieve a better understanding of the interactions between investors with intelligent and data-oriented approaches. The tool of this research is based on the Social Network Analysis, which is based on a created network of investors, whose basis of relationships in this network is "similar behavioral history in investing in a start-up business". In order to achieve the mentioned network, the records of investors' investment in startups - collected in the well-known Crunchbase platform - were clustered and similar behavioral patterns were identified. In the next step, a weighting graph was formed based on the amount of investors commonality in similar behaviors. The proposed application of this research in a platform like the aforementioned network is to identify small investment syndicates or trade unio‎ns, which can be used as a platform for consultation, following more expert investors, and, in general, maximization in using synergy and productivity in the process of investing. Investment benefit. Identification of syndicates in the network of this research was achieved by means of Community Detection tools in two methods. At the end, the key people of each community were identified using centrality indicators.

تحليل رفتار سرمايه‌گذار , سنديكاي سرمايه‌گذاري , خوشه‌بندي , تحليل شبكه اجتماعي , شناسايي انجمن

: investor behavior analysis , investment syndicate , clustering , social network analysis , community detection

Ali Tashkini

Dr. Mohammad Reza Rasouli