Abnormal stock returns

With the increasing competitiveness of the capital market, one of the topics that has attracted the attention of many financial researchers in recent years is the reasons for the formation of abnormal stock returns as a stimulus for restructuring companies due to the difference between real returns and Expected (normal) returns can motivate investment in the capital market. The purpose of this study is to evaluate the impact of the reasons for the formation of abnormal stock returns on the content of the autopoietic restructuring theory of companies operating in the petrochemical industry in the capital market. In this study, in order to identify the components (reasons for the formation of abnormal stock returns) and research propositions (content themes of corporate autopoietic restructuring theory), a combined analysis was used with the participation of 15 accounting experts at the university level. In the quantitative part, the components and propositions identified in the form of matrix questionnaires were evaluated by interpretive ranking process (IRP) by 20 financial managers of capital market petrochemical companies. The results showed that the most effective reason for the formation of abnormal stock returns is the institutional and regulatory causes of the stock market, which has the greatest impact on the dimension of autopoietic restructuring strategy of petrochemical companies. In other words, institutional and regulatory changes in the stock market by upstream institutions will be able to influence the restructuring process with the aim of matching the content with the structural process (the autopoietic approach).

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 2008- 2017 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.