Friendship Of Stock Indices

  title={Friendship Of Stock Indices},
  author={L{\'a}szl{\'o} Nagy and Mihaly Ormos},
The aim of this study is to cluster different stock indices based on historical time series data. The current research shows that tail events have minor effect on the equity index structure. It also turns out that major part of the total variance can be explained by clusters. In addition, clusterwise regressions are reliable, hence CAPM with clusters gives real information about risk and reward. 

Review Of Global Industry Classification

The financial market implied industry classification standard is introduced with a spectral clustering based quantitative methodology to unveil the Financial Market Implied Classification (FMIC) and it is shown that the normalized modularity cut and GICS are highly comparable.



Correlations in Financial Time Series during Extreme Events-Spectral Clustering and Partition Decoupling Method

  • Economics
  • 2009
This paper presents scale-dependent topological description of the network structure of stock market participants. The correlation structure of stock price series reveals the correlation network

Entropy-Based Financial Asset Pricing

The results show that entropy decreases in the function of the number of securities involved in a portfolio in a similar way to the standard deviation, and that efficient portfolios are situated on a hyperbola in the expected return – entropy system.

Topological structures in the equities market network

We present a new method for articulating scale-dependent topological descriptions of the network structure inherent in many complex systems. The technique is based on “partition decoupled null

Measuring the Discriminative Power of Rating Systems

This article analyzes the Cumulative Accuracy Profile and the Receiver Operating Characteristic and shows how to identify accounting ratios with high discriminative power, how to calculate confidence intervals for the area below the ROC curve, and how to test if two rating models validated on the same data set are different.

A tutorial on spectral clustering

This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches.

Penalized versions of the Newman-Girvan modularity and their relation to normalized cuts and k-means clustering.

  • M. Bolla
  • Mathematics
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2011
Two penalized-balanced and normalized-versions of the Newman-Girvan modularity are introduced and estimated by the non-negative eigenvalues of the modularity and normalized modularity matrix,

A survey of kernel and spectral methods for clustering

Normalized cuts and image segmentation

  • Jianbo ShiJ. Malik
  • Computer Science
    Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition
  • 1997
This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.

R and Data Mining: Examples and Case Study

  • Elsevir Inc
  • 2015