Accounting and Auditing Research

Accounting and Auditing Research

The Moderating Effect of Investor Sentiment on The Relationship between Conditional Variance and Stock Returns by Comparing the Rolling Window Model. Mixed Data Sampling Approach, GARCH and Asymmetric GARCH Models

Document Type : Original Article

Authors
1 Assistant Professor, Department of Accounting, parandak Institue of Higher Education, Parandak, Iran
2 Assistant Professor, Department of Accounting, Mobarake Branch, Islamic Azad University, Isfahan, Iran
10.22034/iaar.2025.222686
Abstract
The aim of this research is to present a model based on investor sentiment and the relationship between conditional variance and stock returns by comparing the rolling window model. The mixed data sampling approach is GARCH and asymmetric GARCH models. The statistical population of this research consists of the companies accepted in the Tehran Stock Exchange, based on the systematic elimination method, 112 companies were selected as the sample size during the years 1394-1400. The method of collecting information is library and field. Data analysis was done using the multivariable regression model presented in the research with the help of Stata software. The results of the research hypotheses test showed that the relationship between the expected stock variance and the average stock return had a positive and significant relationship. According to the obtained results, it was observed that the conditional variance of stock returns obtained from the Garch method and mixed data sampling is strongly influenced by investor sentiment. The moderating effect of investor sentiment was such that it led to an increase in the positive effect of the expected future variance on the average stock. Therefore, the predicted and unanticipated variance of stock returns on the average stock return under the influence of investor sentiments was also confirmed using the GARCH model and mixed data sampling approach, but it was not confirmed with the revolving window model and the asymmetric GARCH model.
Keywords

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