scholarly journals Analysis of the Dynamics of Market Graph Characteristics

2019 ◽  
Author(s):  
Alexey Faizliev ◽  
Vladimir Balash ◽  
Andrey Vlasov ◽  
Tatiana Tryapkina ◽  
Sergei Mironov ◽  
...  
Keyword(s):  
Market Graph

Market Graph and Markowitz Model

2014 ◽  
pp. 293-306 ◽  
Author(s):  
Valery Kalyagin ◽  
Alexander Koldanov ◽  
Petr Koldanov ◽  
Viktor Zamaraev
Keyword(s):  
Markowitz Model ◽  
Market Graph

scholarly journals Optimal decision for the market graph identification problem in a sign similarity network

2017 ◽  
Vol 266 (1-2) ◽  
pp. 313-327 ◽  
Author(s):  
V. A. Kalyagin ◽  
A. P. Koldanov ◽  
P. A. Koldanov ◽  
P. M. Pardalos
Keyword(s):  
Identification Problem ◽  
Optimal Decision ◽  
Similarity Network ◽  
Market Graph

scholarly journals Estimating Fund-Raising Performance for Start-up Projects from a Market Graph Perspective

2021 ◽  
pp. 108204
Author(s):  
Likang Wu ◽  
Zhi Li ◽  
Hongke Zhao ◽  
Qi Liu ◽  
Enhong Chen
Keyword(s):  
Fund Raising ◽  
Market Graph ◽  
Start Up

scholarly journals Market Graph Clustering via QUBO and Digital Annealing

2021 ◽  
Vol 14 (1) ◽  
pp. 34
Author(s):  
Seo Woo Hong ◽  
Pierre Miasnikof ◽  
Roy Kwon ◽  
Yuri Lawryshyn
Keyword(s):  
Graph Clustering ◽  
Small Subset ◽  
Index Tracking ◽  
Np Hard ◽  
Tracking Problem ◽  
Financial Industry ◽  
Graph Models ◽  
Clustering Problem ◽  
Market Graph ◽  
Common Task

We present a novel technique for cardinality-constrained index-tracking, a common task in the financial industry. Our approach is based on market graph models. We model our reference indices as market graphs and express the index-tracking problem as a quadratic K-medoids clustering problem. We take advantage of a purpose-built hardware architecture to circumvent the NP-hard nature of the problem and solve our formulation efficiently. The main contributions of this article are bridging three separate areas of the literature, market graph models, K-medoid clustering and quadratic binary optimization modeling, to formulate the index-tracking problem as a binary quadratic K-medoid graph-clustering problem. Our initial results show we accurately replicate the returns of various market indices, using only a small subset of their constituent assets. Moreover, our binary quadratic formulation allows us to take advantage of recent hardware advances to overcome the NP-hard nature of the problem and obtain solutions faster than with traditional architectures and solvers.

scholarly journals Network Approaches to Analyze the Dynamics of Financial Markets

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Miranda Rose Lochner
Keyword(s):  
Financial Markets ◽  
Power Law ◽  
Stock Prices ◽  
Spanning Tree ◽  
Minimum Spanning Tree ◽  
Threshold Value ◽  
Exponential Relationship ◽  
Market Graph ◽  
Data Points ◽  
Network Approaches

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.

scholarly journals Comparative Analysis of Financial Network Topology for the Russian, Chinese and US Stock Markets

2020 ◽  
Vol 17 ◽  
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Minimum Spanning Tree ◽  
Topological Properties ◽  
Hierarchical Tree ◽  
Market Graph ◽  
Topological Network ◽  
The Us ◽  
Russian Stock Market ◽  
Network Approaches

This paper studies the properties of the Russian stock market by employing the data-driven science and network approaches. The theory of complex networks allows us to build and examine topological network structures of the market with the further identification of relationships between stocks and the analysis of hidden information and market dynamics. In this paper we will present an analysis of structural and topological properties of the Russian stock market using market graph, hierarchical tree, minimum spanning tree approaches. We compare topological properties of the networks constructed for the US and China stock markets with the properties of corresponding networks constructed for the Russian stock market using a dataset spanning over eight years.

Sign in / Sign up

Export Citation Format

Share Document