scholarly journals Mexican Stock Exchange Performance after the Crisis of 2008: Application of Data Mining

2020 ◽  
Vol 18 ((1)) ◽  
Author(s):  
Eliseo Ramírez Reyes ◽  
Arturo Morales Castro ◽  
Néstor Juan Sanabria Landazábal
Keyword(s):  
Neural Networks ◽  
Default Risk ◽  
Regression Models ◽  
Prediction Models ◽  
Stock Exchange ◽  
Sovereign Default ◽  
Linear Regression Models ◽  
Exchange Performance ◽  
Sovereign Default Risk ◽  
Dow Jones Industrial Average

Different prediction models are explored to analyze the performance of the Mexican Stock Exchange (PQI) after the 2008 crisis. These models have demonstrated a good prognostic capacity for both multivariable and univariable approaches given their non-parametric characteristics. The selected variables were: Dow Jones Industrial Average Index (DJIA), CPI, International Reserves (IR), CETES28, USDMX exchange rate, (M1) and the sovereign default risk of Mexico (MRDS). The models were evaluated with MAPE and compared with linear regression models (LR) and neural networks (NN). The results show that the models have a similar performance according to the percentages of error they presented.

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

scholarly journals Prediction of Blended Yarn Evenness and Tensile Properties by Using Artificial Neural Network and Multiple Linear Regression

2016 ◽  
Vol 16 (2) ◽  
pp. 43-50 ◽  
Author(s):  
Samander Ali Malik ◽  
Assad Farooq ◽  
Thomas Gereke ◽  
Chokri Cherif
Keyword(s):  
Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Tensile Properties ◽  
Regression Models ◽  
Prediction Models ◽  
Research Work ◽  
Absolute Error ◽  
Knowledge Domain ◽  
Artificial Neural

Abstract The present research work was carried out to develop the prediction models for blended ring spun yarn evenness and tensile parameters using artificial neural networks (ANNs) and multiple linear regression (MLR). Polyester/cotton blend ratio, twist multiplier, back roller hardness and break draft ratio were used as input parameters to predict yarn evenness in terms of CVm% and yarn tensile properties in terms of tenacity and elongation. Feed forward neural networks with Bayesian regularisation support were successfully trained and tested using the available experimental data. The coefficients of determination of ANN and regression models indicate that there is a strong correlation between the measured and predicted yarn characteristics with an acceptable mean absolute error values. The comparative analysis of two modelling techniques shows that the ANNs perform better than the MLR models. The relative importance of input variables was determined using rank analysis through input saliency test on optimised ANN models and standardised coefficients of regression models. These models are suitable for yarn manufacturers and can be used within the investigated knowledge domain.

Banks’ international assets and sovereign default risk

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Filippo Gori
Keyword(s):  
Credit Risk ◽  
Default Risk ◽  
Sovereign Default ◽  
Content Type ◽  
Sovereign Credit ◽  
Developed Economies ◽  
Sovereign Default Risk ◽  
Sovereign Credit Risk ◽  
Using Data ◽  
International Assets

Purpose This paper aims to investigate the nexus between banks’ foreign assets and sovereign default risk in a panel of 15 developed economies. The empirical evidence suggests that banks’ foreign exposure is an important determinant of sovereign default probability. Design/methodology/approach Using data from the consolidated banking statistics (total foreign claims on ultimate risk basis) by the Bank of International Settlements, the author constructs a measure of bank international exposure to peer countries. This measure is then used as the target variable in a panel regression for sovereign credit default swaps. The model includes 15 European and non-European developed economies. Identification is discussed extensively in the paper. Findings Quantitatively, a 1% increase in banks’ cross-border claims increases sovereign default risk by about 0.19%. The relationship is weaker when banks are more capitalised. On the other hand, governments are more vulnerable to credit risk spillovers from banks’ international portfolios when having higher debt to GDP ratios. Originality/value To the best of the author’s knowledge, this is the first paper that attempts explicitly to establish an empirical connection between banks’ international assets and sovereign default risk. To the author’s opinion, this paper represents a contribution to our understanding of how sovereign credit risk spills over across countries. It also extends significantly the existing literature on the determinants of sovereign risk (that primarily focused on fundamentals, market characteristics – such as liquidity – and global factors). This paper ultimately sheds some new light on the role of intermediaries in the international transmission of credit risk, also adding to today’s discussion about the linkages between banks and sovereigns.

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