scholarly journals Topological Structure of Stock Market Networks during Financial Turbulence: Non-Linear Approach

2019 ◽  
Vol 7 (4) ◽  
pp. 106-121
Author(s):  
Arash Sioofy Khoojine ◽  
Han Dong
Keyword(s):  
Stock Market ◽  
Topological Structure ◽  
Linear Approach ◽  
Non Linear ◽  
Market Networks

Overcoming “The Third Way” Concept

2006 ◽  
pp. 126-134
Author(s):  
L. Evstigneeva ◽  
R. Evstigneev
Keyword(s):  
Economic Research ◽  
Economic Uncertainty ◽  
Third Way ◽  
The Third ◽  
Linear Approach ◽  
The World ◽  
Non Linear

“The Third Way” concept is still widespread all over the world. Growing socio-economic uncertainty makes the authors revise the concept. In the course of discussion with other authors they introduce a synergetic vision of the problem. That means in the first place changing a linear approach to the economic research for a non-linear one.

Energy resources demand-supply system analysis and empirical research based on non-linear approach

Energy ◽  
2011 ◽  
Vol 36 (9) ◽  
pp. 5460-5465 ◽  
Author(s):  
Mei Sun ◽  
Xiaofang Wang ◽  
Ying Chen ◽  
Lixin Tian
Keyword(s):  
Empirical Research ◽  
System Analysis ◽  
Supply System ◽  
Energy Resources ◽  
Linear Approach ◽  
Non Linear

scholarly journals Dynamic Evolution of Securities Market Network Structure under Acute Fluctuation Circumstances

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11
Author(s):  
Haifei Liu ◽  
Tingqiang Chen ◽  
Zuhan Hu
Keyword(s):  
Stock Market ◽  
Network Structure ◽  
Topological Structure ◽  
Securities Market ◽  
Structure Stability ◽  
Dynamic Evolution ◽  
Bear Market ◽  
Bull Market ◽  
Market Transaction ◽  
Measuring Network

This empirical research applies cointegration in the traditional measurement method first to build directed weighted networks in the context of stock market. Then, this method is used to design the indicators and the value simulation for measuring network fluctuation and studying the dynamic evolution mechanism of stock market transaction networks as affected by price fluctuations. Finally, the topological structure and robustness of the network are evaluated. The results show that network structure stability is strong in the bull market stage and weak in the bear market stage. And the convergence rate of the dynamic evolution of network fluctuation is higher in the bull market stage than in the bear market stage.

scholarly journals Foreign Ownership Effect to Stock Market Liquidity in Indonesia

MANAJERIAL ◽  
2021 ◽  
Vol 8 (01) ◽  
pp. 01
Author(s):  
Annisa Yasmin
Keyword(s):  
Stock Market ◽  
Financial Institutions ◽  
Capital Market ◽  
Foreign Ownership ◽  
Market Liquidity ◽  
Linear Effect ◽  
Non Linear Effect ◽  
Non Linear ◽  
Stock Market Liquidity ◽  
Research Period

Background – One of economic indicators of a country is the capital market. Liquid capital market can attract investors, both foreign and domestic investors, to invest their ownership in that country, which in turn can improve the country’s economic growth. Aim – This research aims to examine the influence foreign ownership on stock market liquidity in Indonesia. Design / methodology / approach – This research splits foreign ownership into two groups, the first one is foreign ownership by financial institutions, and the second one is foreign ownership by non-financial corporations. The type of data used is panel data using fixed effect model (FEM). The technique for examining the influence of foreign ownership on liquidity used multiple regression analysis. Findings – The result found that foreign ownership by financial institutions and non-financial corporations negatively affect liquidity.  The study also found a positively non-linear effect between foreign ownership by financial institutions to liquidity and a negatively non-linear effect between foreign ownership by non-financial institutions to liquidity. Research implication – This research can assist investors in determining investment in the Indonesian capital market by pay attention to variables such as foreign ownership, return, turnover, market capitalization and standard deviation. Limitation – The research period was short, which was only 21 months due to limited data and the research period that has passed too long, that is January 2012 to September 2013.

scholarly journals Forecasting stock returns on the Amman Stock Exchange: Do neural networks outperform linear regressions?

2021 ◽  
Vol 18 (4) ◽  
pp. 280-296
Author(s):  
Abdel Razzaq Al Rababa’a ◽  
Zaid Saidat ◽  
Raed Hendawi
Keyword(s):  
Stock Market ◽  
Regression Models ◽  
Linear Models ◽  
Stock Exchange ◽  
Market Returns ◽  
Stock Market Returns ◽  
Statistical Measures ◽  
Amman Stock Exchange ◽  
Non Linear ◽  
Ann Models

Different models have been used in the finance literature to predict the stock market returns. However, it remains an open question whether non-linear models can outperform linear models while providing accurate predictions for future returns. This study examines the prediction of the non-linear artificial neural network (ANN) models against the baseline linear regression models. This study aims specifically to compare the prediction performance of regression models with different specifications and static and dynamic ANN models. Thus, the analysis was conducted on a growing market, namely the Amman Stock Exchange. The results show that the trading volume and interest rates on loans tend to explain the monthly returns the most, compared to other predictors in the regressions. Moreover, incorporating more variables is not found to help in explaining the fluctuations in the stock market returns. More importantly, using the root mean square error (RMSE), as well as the mean absolute error statistical measures, the static ANN becomes the most preferred model for forecasting. The associated forecasting errors from these metrics become equal to 0.0021 and 0.0005, respectively. Lastly, the analysis conducted with the dynamic ANN model produced the highest RMSE value of 0.0067 since November 2018 following the amendment to the Jordanian income tax law. The same observation is also seen since the emerging of the COVID-19 outbreak (RMSE = 0.0042).

scholarly journals A study of co-movements between U.S. and Latin American stock markets: A cross-bicorrelations perspective

DYNA ◽  
2016 ◽  
Vol 83 (196) ◽  
pp. 143-148 ◽  
Author(s):  
Semei Coronado-Ramirez ◽  
Omar Rojas-Altamirano ◽  
Rafael Romero-Meza ◽  
Francisco Venegas-Martínez
Keyword(s):  
Stock Market ◽  
Latin American ◽  
Stock Markets ◽  
Nonlinear Dependence ◽  
Standard And Poor's ◽  
Non Linear ◽  
Market Indexes

<p>This work applies a test that detects dependence between pairs of variables. The kind of dependence is a non-linear one, and the test is known as cross-bicorrelation, which is associated with Brooks and Hinich [1]. We study dependence periods between U.S. Standard and Poor's 500 (SP500), used as a benchmark, and six Latin American stock market indexes: Mexico (BMV), Brazil (BOVESPA), Chile (IPSA), Colombia (COLCAP), Peru (IGBVL) and Argentina (MERVAL). We have found windows of nonlinear dependence and comovement between the SP500 and the Latin American stock markets, some of which coincide with periods of crisis, leading to an interpretation of a possible contagion or interdependence.</p>

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