Shohre Hadidifard; Mona Parsaei; Nafiseh Shahmoradi
Abstract
The substitution hypothesis postulates that various corpo- rate governance forms and dividend disbursements serve as alternatives. Given that transparent information disclosure mitigates agency issues by lessening information asymmetry and fortifying corporate governance, this study aims to explore the ...
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The substitution hypothesis postulates that various corpo- rate governance forms and dividend disbursements serve as alternatives. Given that transparent information disclosure mitigates agency issues by lessening information asymmetry and fortifying corporate governance, this study aims to explore the influence of Material Information Dis- closure which includes Groups A, B and Other Cases—characterized by their promptitude and significance—on dividends. Examining the period from 2018 to 2021 and encompassing a sample of 173 listed firms from the Tehran Stock Exchange, the findings affirm the substitution hypothesis. Moreover, Board independence is identified as a moderator in the rela- tionship between Material Information Disclosures and dividend. Fur- thermore, the findings indicate that during the Covid-19 period, Group A and Other Cases were more potent factors for dividend reduction than Group B disclosure.
Nafiseh Shahmoradi; Hasan Ghalibaf Asl
Abstract
A large number of investors have been attracted to the Iran Mercantile Exchange as a result of launching Bahar Azadi Coin future contracts, also known as gold coin future contracts, since 2007. The nature of gold price as a physical-commodity and financial asset, as well as other contributing factors ...
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A large number of investors have been attracted to the Iran Mercantile Exchange as a result of launching Bahar Azadi Coin future contracts, also known as gold coin future contracts, since 2007. The nature of gold price as a physical-commodity and financial asset, as well as other contributing factors to the gold futures market, extremely complicates the analysis of the relationship between the underlying variables.One of the methods to forecast the price volatility is the Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model. However, the high percentage of errors in such prediction has forced researchers to apply a variety of techniques in the hope of more accurate projections. Similarly, in this study, a hybrid model of the GARCH and Artificial Neural Network model (ANN) was used to predict the volatility of gold coin spot and future prices in the Iran Mercantile Exchange.In this study, variables such as global gold price, spot or future gold coin price (depending on which one is analyzed), US Dollar/IR Rial, world price of OPEC crude oil, and Tehran Stock Exchange Index were considered as factors affecting the price of gold coin. The results of the study indicate that the ANN-GARCH model provides a better prediction model compared to the Autoregressive models. Moreover, the ANN-GARCH model was utilized to compare the predictive power of spot and future gold coin prices, and it revealed that gold coin future price fluctuations predicted spot price of gold coin more accurately.
