scholarly journals Neural Networks for Estimating Speculative Attacks Models

Entropy ◽  
2021 ◽  
Vol 23 (1) ◽  
pp. 106
Author(s):  
David Alaminos ◽  
Fernando Aguilar-Vijande ◽  
José Ramón Sánchez-Serrano
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Currency Crises ◽  
Estimation Method ◽  
Ordinary Least Squares ◽  
Speculative Attacks ◽  
Speculative Attack ◽  
Network Methods ◽  
Time Required ◽  
Attack Models

Currency crises have been analyzed and modeled over the last few decades. These currency crises develop mainly due to a balance of payments crisis, and in many cases, these crises lead to speculative attacks against the price of the currency. Despite the popularity of these models, they are currently shown as models with low estimation precision. In the present study, estimates are made with first- and second-generation speculative attack models using neural network methods. The results conclude that the Quantum-Inspired Neural Network and Deep Neural Decision Trees methodologies are shown to be the most accurate, with results around 90% accuracy. These results exceed the estimates made with Ordinary Least Squares, the usual estimation method for speculative attack models. In addition, the time required for the estimation is less for neural network methods than for Ordinary Least Squares. These results can be of great importance for public and financial institutions when anticipating speculative pressures on currencies that are in price crisis in the markets.

scholarly journals Health Demand: Empirical Study of Effective Urban Households Demand in Indonesia

2021 ◽  
Vol 9 (E) ◽  
pp. 812-816
Author(s):  
Mohamad Ichwan ◽  
Firmansyah Firmansyah ◽  
Eko Jokolelono
Keyword(s):  
Health Insurance ◽  
Least Squares ◽  
Urban Areas ◽  
Estimation Method ◽  
Ordinary Least Squares ◽  
Demand Model ◽  
Estimation Model ◽  
Medical Expenses ◽  
Urban Households ◽  
Health Demand

BACKGROUND: Grossman's health demand model recognizes medical price as a determinant of the estimation model. This article aims to examine the role of medical expenses in health demand by utilizing the number of sick and disturbed days obtained from Susenas, a survey on the expenditure of household food and non-food consumption conducted by the Central Bureau of Statistics to measure health demand and health insurance as a medical price in a reduction model. Health insurance can replace medical expenses because those who have health insurance face relatively low medical costs and face lower medical prices than those without health insurance.   METHODS: Using the Ordinary Least Squares (OLS) estimation technique, sebuah teknik estimasi model regresi for 6,642 households this was obtained through three stages: First, using 71,932 sample households of susenas that relied fully on the Susenas sampling method by BPS; Second, find households that have experienced health problems during the last 6 months; Third, find households that have health expenditures of 24,341. Furthermore, the estimation model is based on 6,642 households identified to be in urban areas using the Ordinary Least Squares (OLS) estimation method.   FINDINGS: The health demand estimation model that can be used to determine the behavior of health demand among urban households is limited to households with formal primary school (SD) education levels. Taking advantage of certain wages, age, cigarette expenditure, and sports expenses, it was found that the number of sick days and felt disturbed in the household group that had health insurance was 5.68 days relatively greater than those without health insurance. However, expanding to higher education and older age was found to be 1.47 days and 1.57 days. Aging tends to decrease good health and health insurance tends to increase it.   CONCLUSION: It was found that health stocks differed between insured households and households without health insurance in those with aging.

Gender diversity and financial performance of Islamic banks

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huthayfa Nabeel Jabari ◽  
Rusnah Muhamad
Keyword(s):  
Financial Performance ◽  
Least Squares ◽  
Gender Diversity ◽  
Estimation Method ◽  
Supervisory Board ◽  
Ordinary Least Squares ◽  
Least Squares Estimation ◽  
Islamic Banks ◽  
Content Type ◽  
Gender Diverse

Purpose The purpose of this paper is to examine the influence of gender diversity among the board of directors (BOD) and Shariah supervisory board (SSB) members on the financial performance of Islamic banks in Indonesia and Malaysia. Design/methodology/approach Data for a sample of 19 Islamic banks for the period 2010–2018 were collected to test the research hypotheses using pooled ordinary least squares estimation method. Generalized least squares estimation method was used to confirm that the results are robust. This study lagged the explanatory variables by one period to control for potential endogeneity. Findings The findings suggest that Islamic banks with more gender-diverse BOD and SSB are expected to have better financial performance. In addition, this paper finds that an increase in Islamic banks’ size may undermine the positive impact of gender diversity among SSB members on Islamic banks’ financial performance. Research limitations/implications This study was conducted only on Islamic banks in Indonesia and Malaysia owing to data constraints; thus, the results may not be generalizable to Islamic banks in other countries. Practical implications Improving financial performance is crucial for banks, especially for Islamic banks, to sustain their fast-growing share globally. Therefore, the findings of this study are expected to provide insight and understanding in the selection and appointment of BOD and SSB members at Islamic banks. Social implications By having women represented in the BOD and SSB, Islamic banks will benefit equally from valuable abilities across demographic groups in the society. Furthermore, if the members of the BOD and SSB are properly selected, Islamic banks with more gender-diverse boards can effectively contribute to enhancing social welfare of various segments in the society. Originality/value This is the first study, as far as is known to the authors, that provides empirical evidence on the influence of gender diversity among BOD and SSB members on the financial performance of Islamic banks. This paper is expected to be used as a reference by the shareholders and customers of Islamic banks in ensuring that the BOD and SSB have the best optimal composition that maximizes their profits.

scholarly journals Predictive Regressions: A Reduced-Bias Estimation Method

2004 ◽  
Vol 39 (4) ◽  
pp. 813-841 ◽  
Author(s):  
Yakov Amihud ◽  
Clifford M. Hurvich
Keyword(s):  
Least Squares ◽  
Estimation Method ◽  
Estimation Procedure ◽  
Ordinary Least Squares ◽  
Small Samples ◽  
Bias Estimation ◽  
Predictive Variables ◽  
Empirical Estimates ◽  
Predictive Regressions ◽  
Predictor Model

AbstractStandard predictive regressions produce biased coefficient estimates in small samples when the regressors are Gaussian first-order autoregressive with errors that are correlated with the error series of the dependent variable. See Stambaugh (1999) for the single regressor model. This paper proposes a direct and convenient method to obtain reduced-bias estimators for single and multiple regressor models by employing an augmented regression, adding a proxy for the errors in the autoregressive model. We derive bias expressions for both the ordinary least-squares and our reduced-bias estimated coefficients. For the standard errors of the estimated predictive coefficients, we develop a heuristic estimator that performs well in simulations, for both the single predictor model and an important specification of the multiple predictor model. The effectiveness of our method is demonstrated by simulations and empirical estimates of common predictive models in finance. Our empirical results show that some of the predictive variables that were significant under ordinary least squares become insignificant under our estimation procedure.

scholarly journals Pemodelan Regresi Nonparametrik dengan Estimator Spline Truncated vs Deret Fourier

2021 ◽  
Vol 1 (1) ◽  
pp. 26-36
Author(s):  
Andrea Tri Rian Dani ◽  
Narita Yuri Adrianingsih
Keyword(s):  
Fourier Series ◽  
Least Squares ◽  
Nonparametric Regression ◽  
Case Fatality Rate ◽  
Estimation Method ◽  
Predictor Variable ◽  
Case Fatality ◽  
Ordinary Least Squares ◽  
Regression Modeling ◽  
Fatality Rate

ABSTRAKPendekatan regresi nonparametrik digunakan apabila hubungan antara variabel prediktor dan variabel respon tidak diketahui polanya. Spline truncated dan deret Fourier merupakan estimator dalam pendekatan nonparametrik yang terkenal, karena memiliki fleksibilitas yang tinggi dan mampu menyesuaikan terhadap sifat lokal data secara efektif. Penelitian ini bertujuan untuk mendapatkan estimator model regresi nonparametrik terbaik menggunakan spline truncated dan deret Fourier. Metode estimasi kurva regresi nonparametrik dilakukan dengan menyelesaikan optimasi Ordinary Least Squares (OLS). Kriteria kebaikan model menggunakan GCV, R2 dan MSE. Pemodelan regresi nonparametrik diterapkan pada data Case Fatality Rate (CFR) akibat Demam Berdarah Dengue (DBD) di Indonesia.  Berdasarkan hasil analisis, hasil estimasi dari pemodelan regresi nonparametrik menunjukkan bahwa estimator spline truncated memberikan performa yang lebih baik dibandingkan estimator deret Fourier. Hal ini ditunjukkan dengan nilai R2 dari estimator spline truncated yaitu sebesar 91,80% dan MSE sebesar 0,04, sedangkan dengan estimator deret Fourier diperoleh nilai R2 sebesar 65,44% dan MSE sebesar 0,19.ABSTRACTThe nonparametric regression approach is used when the relationship between the predictor variable and the response variable is unknown. Spline truncated and Fourier series are well-known estimators in the nonparametric approach because they have high flexibility and are able to adjust to the local properties of the data effectively. This study aims to obtain the best nonparametric regression model estimator using the truncated spline and the Fourier series. The nonparametric regression curve estimation method is done by completing the Ordinary Least Squares (OLS) optimization. The criteria for the goodness of the model use GCV, R2, and MSE. Nonparametric regression modeling is applied to Case Fatality Rate (CFR) modeling due to Dengue Hemorrhagic Fever (DBD) in Indonesia. Based on the analysis, the estimation results from the nonparametric regression modeling show that the truncated spline estimator provides better performance than the Fourier series estimator. This is shown by the R2 value of the truncated spline estimator which is 91.80% and the MSE is 0.04, while the Fourier series estimator obtained an R2 value of 65.44% and MSE of 0.19.

scholarly journals Measurement Of A Financial Model Of The Firm: A Field Study

2011 ◽  
Vol 9 (4) ◽  
pp. 83 ◽  
Author(s):  
John J. Wild
Keyword(s):  
Least Squares ◽  
Field Study ◽  
Financial Models ◽  
Estimation Method ◽  
Ordinary Least Squares ◽  
Parameter Measurement ◽  
Corporate Management ◽  
Financial Model ◽  
Simple Estimation ◽  
Pro Forma

A financial model of the firm is a useful tool for corporate management in formulating and executing strategic company operations and in understanding past managerial actions. Yet, while financial models have evolved to simultaneously-determined systems which portray the myriad of interdependencies among accounting variables, measurement of their parameters typically relies on simple parsimonious techniques which are theoretically inferior. Accurate measurement of the parameters is important for reliable application of the mode3l, including what if analyses, managerial planning exercises, and production of pro forma reports. This article reports on a field study of the implications associated with using the commonly-employed ordinary least squares technique of parameter measurement for a financial model of the firm. The results show that parameter measurement using this simple estimation method are significantly different from those obtained from a theoretically superior technique. Decomposition of the measurement differences demonstrates an association with characteristics of both the firm and its environment; moreover, the differences are shown to be primarily attributed to the earnings-based relations of the model.

Different inference approaches for the estimators of the sushila distribution

2021 ◽  
Vol 16 (4) ◽  
pp. 251-260
Author(s):  
Marcos Vinicius de Oliveira Peres ◽  
Ricardo Puziol de Oliveira ◽  
Edson Zangiacomi Martinez ◽  
Jorge Alberto Achcar
Keyword(s):  
Maximum Likelihood ◽  
Least Squares ◽  
Bayesian Method ◽  
Weighted Least Squares ◽  
Computational Cost ◽  
Estimation Method ◽  
Computation Time ◽  
Ordinary Least Squares ◽  
Likelihood Method ◽  
Estimation Methods

In this paper, we order to evaluate via Monte Carlo simulations the performance of sample properties of the estimates of the estimates for Sushila distribution, introduced by Shanker et al. (2013). We consider estimates obtained by six estimation methods, the known approaches of maximum likelihood, moments and Bayesian method, and other less traditional methods: L-moments, ordinary least-squares and weighted least-squares. As a comparison criterion, the biases and the roots of mean-squared errors were used through nine scenarios with samples ranging from 30 to 300 (every 30rd). In addition, we also considered a simulation and a real data application to illustrate the applicability of the proposed estimators as well as the computation time to get the estimates. In this case, the Bayesian method was also considered. The aim of the study was to find an estimation method to be considered as a better alternative or at least interchangeable with the traditional maximum likelihood method considering small or large sample sizes and with low computational cost.

COMPARISON OF NEURAL NETWORK AND ORDINARY LEAST SQUARES MODELS IN FORECASTING CHINESE STOCK PRICES

2019 ◽  
Vol 19 (1) ◽  
pp. 17-32
Author(s):  
Ojoung Kwon ◽  
Sasan Rahmatian ◽  
Alicia Iriberri ◽  
Zijian Wu
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Stock Prices ◽  
Ordinary Least Squares

Research of assembling optimized classification algorithm by neural network based on Ordinary Least Squares (OLS)

2011 ◽  
Vol 22 (1) ◽  
pp. 187-193 ◽  
Author(s):  
Xinzheng Xu ◽  
Shifei Ding ◽  
Weikuan Jia ◽  
Gang Ma ◽  
Fengxiang Jin
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Ordinary Least Squares ◽  
Classification Algorithm

scholarly journals Approximation Techniques for Neuromimetic Calculus

1999 ◽  
Vol 09 (03) ◽  
pp. 227-234
Author(s):  
VINCENT VIGNERON ◽  
CLAUDE BARRET
Keyword(s):  
Neural Network ◽  
Least Squares ◽  
Statistical Methods ◽  
Data Structures ◽  
Approximation Theory ◽  
Entire Range ◽  
Network Modeling ◽  
Ordinary Least Squares ◽  
Neural Network Modeling ◽  
Approximation Techniques

Approximation Theory plays a central part in modern statistical methods, in particular in Neural Network modeling. These models are able to approximate a large amount of metric data structures in their entire range of definition or at least piecewise. We survey most of the known results for networks of neurone-like units. The connections to classical statistical ideas such as ordinary least squares (LS) are emphasized.

Sign in / Sign up

Export Citation Format

Share Document