Network Approaches to Analyze the Dynamics of Financial Markets

Abstract

Analyzing financial markets requires gathering large amounts of data and determining appropriate methods so that accurate and appropriate conclusions can be drawn. The purpose of this paper is to investigate network approaches to understand large amounts of financial data and the implications of different approaches. Creating a market graph has been used to analyze financial instruments, and prices fluctuations of stocks over a large time period. A market graph is constructed with nodes and edges; nodes represent the quantity of interest, or specific data points, such as stock prices at an instance of time. Edges represent a relationship between one node and another. Creating edges can be accomplished through many different approaches including correlation coefficients, power law, and minimum spanning tree. Pearson’s correlation coefficient can be used to relate a set of two data points and can be further filtered through a minimum threshold value. The power law graph is another unique way to relate data points to one another. The power law graph creates edges among nodes by considering a probability and the binomial distribution. The power law graph has powerful implications on network analysis because it concludes that the degree distribution, the number of connections a node has to other nodes, is represented as an exponential relationship. A minimum spanning tree is a hierarchical method used to analyze market graphs. A minimum spanning tree clusters data by partitioning data appropriately. Overall, many methods are defined to establish a market graph depending on the purpose of the analysis and the parameter of interest.