In previous studies, the normal mixture, as well as the Markov process, were used to model the financial return, separately. In this study, the normal mixture model is extended to the Markov mixture of normals. The mixture weights in every state are considered time-vary More
In previous studies, the normal mixture, as well as the Markov process, were used to model the financial return, separately. In this study, the normal mixture model is extended to the Markov mixture of normals. The mixture weights in every state are considered time-varying and as a function of past observations, so the limit of constant weight assumption is removed. The proposed model is estimated using Bayesian inference and a Gibbs sampling algorithm has been created to compute posterior density. The performance of algorithm is tested with simulation, then a two-state Markov time-varying Mixed Normal-GARCH model (MMN) with one and two components in every state, as well as limited cases (mean zero), were compared by comparison of their likelihood function. Finally, the model is applied to S&P500 and TEPIX daily return and results show that MMN models with two components provide better results than MMN model with one component which is so-called Markov switching GARCH model.
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The purpose of this research is Monte Carlo Markov chain simulation under Bayesian inference to identify the parameters affecting earning quality measurement. In this regard, in order to predict the earning behavior of companies and to derive the exact parameters of the More
The purpose of this research is Monte Carlo Markov chain simulation under Bayesian inference to identify the parameters affecting earning quality measurement. In this regard, in order to predict the earning behavior of companies and to derive the exact parameters of the model from the Bayesian Markov Monte Carlo (MCMC) technique, which takes cross-sectional heterogeneity into account, an analysis was done by coding in Python. In this research, the earning signals extracted from the financial statements on a quarterly basis for a period of 5 years (2018-2022), for 104 companies admitted to the Tehran Stock Exchange, were collected and analyzed using a new measure of earning quality. Auxiliary variables of accounting comparability, financial leverage, operating cycle, and sales volatility were used to achieve more accurate results, and several statistical performance measures (R2, RMSE, and MSE) were used to evaluate the effectiveness of Bayesian-based forecasting models. The results showed that the proposed criterion of the present study derived from the Bayesian model for training and testing data is well able to predict the quality of earning. The evidence shows that the results of the proposed model are superior to the conventional accrual earning management model, which suggests an error rate of MSE=0.0188 and RMSE=0.1369, respectively. The results of the present research can be used to analyze the portfolio and predict the quality of future earnings of companies using historical data. It can also be used to study factors affecting investment performance.
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هدف از این مطالعه، برآورد پارامترهای ژنتیکی صفات تولید، تولید مثل و بهداشت گاوهای هلشتاین ایران با استفاده از روش بیزی از طریق نمونه­گیری گیبس بود. رکوردهای اولین شیردهی 320666 گاو هلشتاین متولد شده از 7696 نر و 260302 ماده که مابین سال‌های 1370 تا 1389 توسط مرکز اص More
هدف از این مطالعه، برآورد پارامترهای ژنتیکی صفات تولید، تولید مثل و بهداشت گاوهای هلشتاین ایران با استفاده از روش بیزی از طریق نمونه­گیری گیبس بود. رکوردهای اولین شیردهی 320666 گاو هلشتاین متولد شده از 7696 نر و 260302 ماده که مابین سال‌های 1370 تا 1389 توسط مرکز اصلاح نژاد دام کشور جمع­آوری شده بود، مورد استفاده قرار گرفت. مولفه­های واریانس- کواریانس با استفاده از مدل حیوانی چند صفتی از طریق نمونهگیری گیبس برآورد شدند. بعد از رسیدن به همگرایی، دامنه تراکم پسین وراثت­پذیری برای شیر (MY305)، چربی (FY305)، پروتئین (PY305)، سن در اولین گوساله ­زایی (AFC)، فاصله گوساله ­زایی (CI) و نمره سلول‌های بدنی (SCS) به ترتیب 275/0-255/0، 215/0-195/0، 225/0-195/0، 275/0-260/0، 080/0-065/0 و 075/0-055/0 بود. دامنه همبستگی ژنتیکی بین 121/0- (بین تولید چربی و سن در اولین گوساله زایی) تا 914/0 (بین تولید شیر و پروتئین) و همبستگی فنوتیپبی بین 083/0- (بین تولید شیر و نمره سلول‌های بدنی) تا 929/0 (بین تولید شیر و پروتئین) به دست آمد. نتایج این مطالعه نشان داد که صفات تولیدی و سن در اولین گوساله زایی تنوع ژنتیکی کافی برای بهبود در برنامه­های اصلاحی را دارند. همبستگی­های ژنتیکی برآورد شده پیشنهاد کننده این هستند که صفات تولید شیر و فاصله گوساله ­زایی در صورتی که افزایش تولید شیر در اهداف انتخابی مد نظر قرار گیرد می­توانند تحت تأثیر قرار بگیرند. همبستگی ژنتیکی بالا بین فاصله گوساله­ زایی و نمره سلول‌های بدنی بیان کننده این است که افزایش فاصله گوساله ­زایی منجر به افزایش نمره سلول‌های بدنی می­شود.
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