Anticipating Abrupt Changes in Complex Networks: Significant Falls in the Price of a Stock Index

2018 ◽  
pp. 317-338
Author(s):  
Antonio Cordoba ◽  
Christian Castillejo ◽  
Juan J. García-Machado ◽  
Ana M. Lara
Keyword(s):  
Complex Networks ◽  
Stock Index ◽  
Abrupt Changes

scholarly journals Thermodynamic Analysis of Time Evolving Networks

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 759 ◽  
Author(s):  
Cheng Ye ◽  
Richard Wilson ◽  
Luca Rossi ◽  
Andrea Torsello ◽  
Edwin Hancock
Keyword(s):  
Complex Networks ◽  
Network Size ◽  
Von Neumann Entropy ◽  
Von Neumann ◽  
Evolving Networks ◽  
Thermodynamic Framework ◽  
Network Time ◽  
Abrupt Changes ◽  
Consecutive Time ◽  
Thermodynamic Variables

The problem of how to represent networks, and from this representation, derive succinct characterizations of network structure and in particular how this structure evolves with time, is of central importance in complex network analysis. This paper tackles the problem by proposing a thermodynamic framework to represent the structure of time-varying complex networks. More importantly, such a framework provides a powerful tool for better understanding the network time evolution. Specifically, the method uses a recently-developed approximation of the network von Neumann entropy and interprets it as the thermodynamic entropy for networks. With an appropriately-defined internal energy in hand, the temperature between networks at consecutive time points can be readily derived, which is computed as the ratio of change of entropy and change in energy. It is critical to emphasize that one of the main advantages of the proposed method is that all these thermodynamic variables can be computed in terms of simple network statistics, such as network size and degree statistics. To demonstrate the usefulness of the thermodynamic framework, the paper uses real-world network data, which are extracted from time-evolving complex systems in the financial and biological domains. The experimental results successfully illustrate that critical events, including abrupt changes and distinct periods in the evolution of complex networks, can be effectively characterized.

Complex Networks

2009 ◽  
Author(s):  
Reuven Cohen ◽  
Shlomo Havlin
Keyword(s):  
Complex Networks

Volatility and Trading Demands in Stock Index Futures

CFA Digest ◽  
2003 ◽  
Vol 33 (3) ◽  
pp. 101-102
Author(s):  
Frank T. Magiera
Keyword(s):  
Stock Index ◽  
Stock Index Futures ◽  
Index Futures

Complex Networks from Simple Rules

2013 ◽  
Vol 22 (2) ◽  
pp. 151-174 ◽  
Author(s):  
Richard Southwell ◽  
Jianwei Huang ◽  
Chris Cannings ◽  
◽  
Keyword(s):  
Complex Networks ◽  
Simple Rules

META-ANALYSIS ON AMERICAN PRESIDENTIAL ELECTION - 2016 AND AMERICAN STOCK MARKET

2020 ◽  
Vol 38 (1) ◽  
Author(s):  
Farhan Ahmed ◽  
Salman Bahoo ◽  
Sohail Aslam ◽  
Muhammad Asif Qureshi
Keyword(s):  
Content Analysis ◽  
Stock Market ◽  
Event Study ◽  
Presidential Election ◽  
Republican Party ◽  
Stock Exchange ◽  
Meta Analysis ◽  
Composite Index ◽  
Stock Index ◽  
Political Event

This paper aims to analyze the efficient stock market hypothesis as responsive to American Presidential Election, 2016. The meta-analysis has been done combining content analysis and event study methodology. The all major newspapers, news channels, public polls, literature and five important indices as Dow Jones Industrial Average (DJIA), NASDAQ Stock Market Composit Indexe (NASDAQ-COMP), Standard & Poor's 500 Index (SPX-500), New York Stock Exchange Composite Index (NYSE-COMP) and Other U.S Indexes-Russell 2000 (RUT-2000) are critically examined and empirically analyzed. The findings from content analysis reflect that stunned winning of Mr Trump from Republican Party worked as shock for American stock market. From event study, findings confirmed that all the major indices reflected a decline on winning of Trump and losing of Ms. Clinton from Democratic. The results are supported empirically and practically through the political event like BREXIT that resulted in shock to Global stock index and loss of $2 Trillion.

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