Iranian Journal of Finance

Although theoretical and empirical literature regarding the stylized facts shows evidence of their correlations to herding behavior in financial markets, the causes of such phenomena are still unknown. Using an agent-based model strengthened by the competition co-evolution algorithm (STGP) technique, this study provides laboratory evidence on capital market dynamics and analyses the behavioral foundations of stylized facts such as fat tails, leverage effects, and volatility clustering. The simulated stock markets consist of two groups; the “Best agents”, which are a small portion of artificial agents, and the “Residual agents”, which are the main group of artificial agents. The best performance in terms of breeding fitness returns is the main feature of the “Best agents”. More, the size of the “Best Agents” group is specified as 2.5%, 5%, 10% &20% of the total population size. An agent-based model consists of two portions, a two thousand population of trader agents that each has its decision-making strategy, and a virtual market that creates the trading strategies. Then the model evolved step by step using a feed with real quotes of the financial instruments by Adaptive Modeler. A training period is considered 2500 bars (started in November 2003), and the test period started in December 2013. The observation shows that the herding behavior in the price series created by the “Residual agents” is less than the “Best agents” series. Therefore, the greater diversity of trade strategies as the genetic differences of artificial agents leads to less herding. The observations exhibit that the volatility clustering, leverage effects, and nonlinear dependence are more likely to experience in the price series generated by “Best gents”. Furthermore, observations indicate that if the population is well diversified in terms of trading strategies, the efficiency of the market increases.

Data mining is known as one of the powerful tools in generating information and knowledge from raw data, and Clustering as one of the standard methods in data mining is a suitable method for grouping data in different clusters that helps to understand and analyze relationships. It is one of the essential issues in the field of investment, so by using stock market clustering, helpful information can be obtained to predict changes in stock prices of different companies and then on how to decide the correct number and shares in the portfolio to private investors and financial professionals' help. The purpose of this study is to cluster the companies listed on the Tehran stock exchange using three methods of K-means Clustering, Hierarchical clustering, and Affinity propagation clustering and compare these three methods with each other. To conduct this research, the adjusted price of 50 listed companies for the period 2019-07-01 to 2020-09-29 has been used.  The evaluation results show that the obtained silhouette coefficient for K-means Clustering is higher and, therefore, better than other methods for stock exchange data. In the continuation of the research, calculating the co-integration of stock pairs that have the same co-movement with each other were identified, and finally, clusters were compiled using the t-SNE method.

While travelers' desire to visit the world's most remote places has grown, the inefficiency of global payments indicates a significant barrier to tourism growth. As an emerging, decentralized, and borderless digital innovation, Bitcoin technology seems to have the ability to serve as a payment alternative and address such fundamental inefficiencies. On the other hand, bitcoin adoption can only happen when tourists and business owners choose to operate bitcoin simultaneously. The study has developed a novel Bitcoin Collaborative Network and Tourism Collaborative Network model to examine Bitcoin adoption factors. Then a fuzzy DEMATEL method was applied to the factors influencing the adoption domain, as identified based on an extensive literature review, in-depth interviews, and an international Delphi process. The study offered a model for the heterogeneous collaborative network of Bitcoin and Tourism (BCN and TCN), revealing that Perceived Usefulness is the most influencing criterion and the most prominent variable in Bitcoin Adoption. Bitcoin Technological Complexity, Government Regulatory, and Bitcoin Awareness are the factors that give the highest impacts. Also, Bitcoin's Technological Complexity is the most significant factor in bitcoin adoption. The findings might assist businesses in adopting a new market expansion strategy and benefiting from technological spillover, while government officials can explore new supporting legislation.

The ability to predict the stock market and analyze market trends is invaluable to researchers and anyone interested in investing. However, this task is a challenging problem due to a large number of parameters and unpredictable noise that may affect the stock price. To overcome this issue, researchers have employed numerous approaches such as Moving Average (MA), Support Vector Machine (SVM), and Neural Networks. With technological advances, deep learning methods have become popular in processing time-series data. In this paper, we compare two recently introduced deep learning models, namely a Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), in forecasting daily movements of the Standard & Poor (S&P 500) index using the daily closing price of this index from 14/5/1991 to 14/5/2021. Results show that both models are effective and accurate in stock market prediction. In this case study, the mean squared error (MSE) and mean absolute error (MAE) for the GRU model are slightly lower than the LSTM model; hence, GRU outperformed the LSTM model despite its simpler structure. The results of this study are applicable in various instances where it is challenging to identify patterns among large volumes of unstructured data, such as medical data analysis, text mining, and financial time series modeling.

Presenting an audit report is the basis for developing competitive strategies that, in the form of operational transparency, are able to guide stakeholders in financial decisions and inform them of operational information and facts. Because auditors have a crucial role to play in reporting these reports, they need to be more balanced, both professionally and on the basis of psychological functions, in finding performance gaps with the functional realities of companies. Therefore, knowing the individual characteristics in this area can help to develop the functions of the audit report. This is because auditors, like any person in charge of a profession, may face disruptions and negative performance characteristics that make it difficult for them to disclose corporate performance facts. Hence this study was to analyze the causes of alienation of conduct in a professional career path for a more comprehensive understanding of the auditor's job is frustration. This research is a mix of methods because through the analysis of grounded theory and with the participation of 17 auditing experts, it seeks to identify the components and statements related to the causes of behavioral alienation in the professional career path of trusted auditors of the Stock Exchange Organization. Then, through the link analysis method and with the participation of trusted auditors of the stock exchange organization, it tries to examine the stimuli and consequences of behavioral alienation in the professional career path of auditors in the form of a systematic representation model. The results obtained from the qualitative part of the existence of three cultural; Social and structural dimensions in the form of 7 categories. The results in the quantitative part also showed that the lack of symbols of professional behavior in the auditors of the stock exchange organization under the component of cultural dimension is the most important functional stimulus in auditors' professional frustrations as causes of professional behavior alienation that can result from this cultural disorder. Cause social conflicts in the professional work of certified auditors of the stock exchange organization.

The purpose of this paper is to focus on examining the impact of conservatism on IPOs underpricing and then examine the role of audit quality as a moderating variable in the relationship between conservatism and IPOs. Based on financial-behavioral theories, analyses are conducted of data from a sample of Tehran Stock Exchange (TSE) listed companies for the fiscal years 2008–2017 (i.e. 230 firm-year observations). Correlation and regression analyses are performed to evaluate possible associations between conservatism and initial public offerings (IPOs) underpricing with regard to audit quality. There is a negative significant relationship between conservatism and IPOs underpricing, i.e., it reduces IPOs underpricing. Also, the research results indicated that the auditing quality cannot moderate the relationship between conservatism and IPOs underpricing in Iran’s stock market. This conclusion may be explained under the winner’s curse theory. Accordingly, informed investors do not request to purchase unattractive stocks, and uninformed investors demand and obtain all the unattractive stocks since there is no competition between informed and uninformed investors. A limitation of this paper is the number of firms, which for future studies needs to be considered. This research is expected to contribute comprehensively to expanding the theoretical foundations and increase audience knowledge of the underpricing of initial public offerings (IPOs). It is also expected that the results of the study will: 1. determine the underpricing of IPOs in Iran during the research period; 2. document the role of conservatism in reducing underpricing of IPOs; 3. potentially prevent inappropriate pricing of new stocks. Furthermore, the findings of this study suggest that the application of financial-behavioral theories calls for more inquiry.