Econometrics
Mojtaba Rostami; Seyed Nezamuddin Makiyan
Abstract
Stock returns forecasting is very crucial for investors, share-holders and arbiters. Different methods have been developed for this purpose. In general, there are four methods of forecasting in stock markets, which are; Technical Analysis, Fundamental Analysis, Traditional Time Series and Machine Learning. ...
Read More
Stock returns forecasting is very crucial for investors, share-holders and arbiters. Different methods have been developed for this purpose. In general, there are four methods of forecasting in stock markets, which are; Technical Analysis, Fundamental Analysis, Traditional Time Series and Machine Learning. This study is classified in the third category that is a time series prediction in which the values of a variable are predicted over time. Studies which have been done so far indicate that most of them concentrate on Neural Networks and Genetic Algorithm which are in Machine Learning class and none of them uses Bayesian approach or Exponential Smoothing and Box Jenkins techniques placed in the group of time series forecasting. This paper focuses on forecasting with time series methodology for predicting and comparing the results of the Bayesian, Exponential Smoothing and Box Jenkins methods together. In fact, the difference between this study and others is the comparison of the mentioned methods for stock return forecasting. The period of investigation was 2018- 2020, which covers daily frequency structure. Results, indicated that Bayesian method, based on the Root Mean Square Error (RMSE) criterion is the best technique for the prediction of stock returns. This is because, in addition to information derived from data, this method also uses other sources of information such as non-sample information or vague prior density as well for forecasting. Results illustrate the importance of considering the Bayesian approach in predicting stock market returns.
Seyed Nezamuddin Makiyan; Mojtaba Rostami; Davood Farhadi; Mohammad Amin Zabol
Abstract
Many empirical studies have analyzed the relationship between economic and social behavior. Unemployment is considered as a phenomenon which is not desirable. The effect of unemployment on crime, may have heterogeneous impact on distinct regions (province or country). The technical approach in econometric ...
Read More
Many empirical studies have analyzed the relationship between economic and social behavior. Unemployment is considered as a phenomenon which is not desirable. The effect of unemployment on crime, may have heterogeneous impact on distinct regions (province or country). The technical approach in econometric literature indicates that, due to high integration of economic and social activities, the independency assumption-in this study, provinces- in the sample cannot be accepted, especially if the sample is small. In order to show the difference of crime effect on unemployment rate in various provinces, the random coefficients Panel Bayesian Conjugate Poisson model (Hierarchical) is used. To do this, the data of five provinces of Iran are used during the period of 2009-2014 to demonstrate such heterogeneous effects. Results show that for an increase in unemployment, there is a positive and varying effects on crime in the provinces for which the data were collected. In other words, in this study, for a %1 increase in unemployment, the greatest impact is in Tehran and the lowest is in Semnan province.
