Chaotic Test and Non-Linearity of Abnormal Stock Returns: Selecting an Optimal Chaos Model in Explaining Abnormal Stock Returns around the Release Date of Annual Financial Statements
Publish Year: 1400
نوع سند: مقاله ژورنالی
زبان: English
View: 190
This Paper With 13 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
JR_AMFA-6-2_008
تاریخ نمایه سازی: 20 تیر 1400
Abstract:
For many investors, it is important to predict the future trend of abnormal stock returns. Thus, in this research, the abnormal stock returns of the listed companies in Tehran Stock Exchange were tested since ۲۰۰۸- ۲۰۱۷ using three hypotheses. The first and second hypotheses examined the non-linearity and non-randomness of the abnormal stock returns ′ trend around the release date of annual financial statements, respectively. While, the third hypothesis tested the potential of the chaos model in explaining future abnormal returns based on the past abnormal returns around the release date of the annual financial statements. For this pur-pose, BDS, Teraesvirta Neural Network, and White Neural Network tests were used to investigate its non-linearity. In addition, Lyapunov exponent, correlation dimension, Dickey-Fuller, and Hurst exponent tests were used for testing non-randomness and the fitness of AR, SETAR, and LSTAR models to determine the optimal model in explaining the abnormal returns utilizing R software. Results of these tests represented a non-linear and non-random process and chaos in the abnormal stock returns, implying the predictability of abnormal stock returns. Also, among three used chaos models, the LSTAR model had lower error and more predictability than the other two models.
Authors
Reyhaneh Enayayi Taebi
Department of Accounting, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran
Alireza Mehrazeen
Department of Accounting, Neyshabur Branch , Islamic Azad University, Neyshabur, Iran
Mehdi Jabbari Nooqabi
Department of Statistics, Mashhad Branch, Ferdowsi University, Mashhad, Iran
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :