Comparative analysis of the BRIC countries stock markets using network approach
The paper presents the analysis of the network model referred to as market graph of the BRIC countries stock markets. We construct the stock market graph as follows: each vertex represents a stock, and the vertices are adjacent if the price correlation coefficient between them over a certain period of time is greater than or equal to specified threshold. The market graphs are constructed for different time periods to understand the dynamics of their characteristics such as correlation distribution histogram, mean value and standard deviation, size and structure of the maximum cliques. Our results show that we can split the BRIC countries into two groups. Brazil, Russia and India constitute the first group, China constitutes the second group.
The article describes proposed by the authors methodology of analysis of the Russian mutual funds. The aim of this methodology is to find out how attractive they are to investors and if they are able to provide the possibility of obtaining higher returns with less risk than the market in general. The study determines what type of fund management (active or passive) is more optimal. It also explains the effectiveness of focusing on past performance of the funds for making future investments. In addition, the ability of the management companies to repeat their past results is analyzed. Moreover, it is shown if it makes sense to focus on management companies that achieved the best results in the past while making decisions about future investments. These and other results achieved in this article reveal the features of the Russian market of collective investments and allow investors to form more competent policy of mutual funds’ investments. The methodology proposed by the authors is universal. Its application for the analysis of the other markets of collective investments will allow revealing their features.
Problem of construction of the market graph as a multiple decision statistical problem is considered. Detailed description of a optimal unbiased multiple decision statistical procedure is given. This procedure is constructed using the Lehmann’s theory of multiple decision statistical procedures and the conditional tests of the Neyman structures. The equations for thresholds calculation for the tests of the Neyman structure are presented and analyzed.
The paper presents an analysis of the stocks traded on MICEX from 2007 to 2011. In order to analyze the data, we construct a market graph model. The vertices of the graph represent stocks; the edges represent strong similarity between considered stocks returns. We suggest using the following way to calculate the similarity measure: we calculate the number of the periods when two considered stocks have the positive return simultaneously. Our results show that the market graph model with the suggested similarity measure can be used to describe the stock market dynamics in an effi- cient and concise manner.
Market graph is built on the basis of some similarity measure for financial asset returns. The paper considers two similarity measures: classic Pearson correlation and sign correlation. We study the associated market graphs and compare the conditional risk of the market graph construction for these two measures of similarity. Our main finding is that the conditional risk for the sign correlation is much better than for the Pearson correlation for larger values of threshold for several probabilistic models. In addition, we show that for some model the conditional risk for sign correlation dominates over the conditional risk for Pearson correlation for all values of threshold. These properties make sign correlation a more appropriate measure for the maximum clique analysis.