Predicting negative stock price shocks based on the Meta heuristic approach
According to capital market research, the negative shock of stock price in any market is a function of environmental factors and specific characteristics of the company and any insight into how to describe and predict the shock can influence the decisions of investors and stakeholders. In this study, based on the data related to 96 financial ratios of 140 companies listed on the Tehran Stock Exchange during a period of 9 years between 2010 and 3012, we have predicted a negative shock of stock price based on the meta-heuristic approach. In this research, in order to extract the optimal financial ratios, genetic algorithms and particle swarm optimization have been used. The proposed model is then tested using these extracted features by a support vector machine with a radial core and an artificial neural network. The results showed that the variables extracted from the particle swarm optimization algorithm, together with the support vector machine learning algorithm, create better results for predicting shocks (temporary and permanent) and their number.
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Coping Strategy, Internal Auditors, Auditor Conflicts.
Maryam Asghari, Reza Fallah*, Hamidreza Gholamnia Roshan, , Azade Kiapoor
Journal of Audit Science, -
Decision-making in ethical dilemmas faced by auditors: a case study of small and medium sized audit firms that are trusted by the stock exchange
Maryam Rokhshi, *, Seyyed Ali Nabavi Chashmi
Journal of Decisions and Operations Research,