Conf42: Python 2022

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Financial Network Analysis using Python

Kalyan Prasad
Data Scientist & Analytics Manager @ Creative Crewz

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Historically, networks have been studied extensively in graph theory, an area of mathematics. After many applications to several different subjects including physics, health science, and sociology, over the last years, network analysis has become an active topic not only in data science but also in finance. In a nutshell, a network is a system with nodes connected by linkages. Network analysis is popular to describe the characteristics or behaviors of complex networks. There has been also some research conducted to model the stock market using networks. The motivation is that the performances of certain stocks are often correlated, either because of the general market direction or the cyclicity of the same segments of the market. To model the stock market using network analysis, different stocks are represented as different nodes. However, defining the interaction, or creating edges, between different nodes is rather non-intuitive, unlike some physical networks, such as friendship network, in which interaction between different nodes can be defined explicitly. A traditional way to create edges between different nodes for stock market is to look at the correlations of some defined attributes. In our case, we analyze one of the reputed stock index data and identifies stock relationships in it. We propose a model that can depict such relationships and create networks of stocks. We investigate and create different networks according to the degree of correlation of stocks. Finally, we will visualize and evaluate our results accordingly. In this talk, we are going to cover the following points: • Introduction to Networks • History & why graphs • Finance evolution in networks • Understanding Network structure • Leveraging the power of Python Graphs • Real-time finance usage of network analysis using two examples(hands-on) • Wrap-up

Goal

By the end of the talk, I will make sure that: • How is data connected with other data?
• How do these financial connections matter? • How do complex systems move in time in the stock market? I promise you; it is an interesting one!

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