Iranian Journal of Finance

Abstract The publication of financial statements, a vital corporate communication tool, significantly influences behavioral biases in both individuals and organizations. While the importance of behavioral factors for investors is well recognized, the impact of financial statement releases on these biases in the Iranian capital market has yet to be thoroughly examined. This study investigates the effect of financial statement releases on two key biases: loss aversion and overconfidence among investors. By employing both quantitative and qualitative research methods, comprehensive data was collected from the Iranian stock market. For the analysis of loss aversion, a sample of 111 companies over six years (2017–2022) was selected using the systematic elimination method, while overconfidence bias was assessed at the market level. An event study method was used to measure the impact of financial statement releases on these biases. To analyze loss aversion, a regression model using the Dynamic Panel Data (GMM) method was applied, and for overconfidence, a Vector Autoregression (VAR) model was estimated during the financial statement release period at the market level. The findings show that investor’s exhibit heightened loss aversion during financial statement releases, and overconfidence bias was also evident, with investors attributing positive market outcomes to themselves and acting more aggressively in subsequent transactions. Based on the results, investors should consider behavioral bias patterns in their decision-making and incorporate technical analysis. Additionally, companies should ensure transparency and accuracy in their financial statements while remaining attentive to market behaviors and reacting promptly to changes.

Abstract One of the most important effective elements in economic growth is the efficiency of manufacturing units. Therefore, measuring the efficiency of firms is necessary in order to increase efficiency in future planning courses. In the current research, using Stochastic Frontier Production Function, the efficiency of firms in Tehran Stock Exchange has been measured. In the above method, the efficient frontier is determined by using the Trans log production function, and the efficiency of each firm measured by the efficient frontier. The most important superiority of Stochastic Frontier Production Function is to specify the role of random and environmental elements (out of firm authorities) and inter-organizational elements (in-firm authorities) to assess the inefficiency of firms as compared to other methods. Thus, 105 firms were selected using maximum likelihood method in 2008-2017 to evaluate the research model. Results indicated that the minerals industry and cement industry with the averages of 53% and 90% had the least and most efficiency values, respectively. Separating the inefficiency values showed that the food industry and chemicals industry had the least and most inefficiency resulting from the firm authorities as 33.6% and 95.2%, respectively. According to research results, financial analysts and investors are recommended to rank the efficiency and assess the performance based on the firm authorities. Due to the importance of efficiency measurement in operational auditing, the auditors are recommended to use the current research model to assess the firm’s efficiency. Also, Organization of Industries and Mines is suggested to tackle the obstacles after identifying the elements out of firm authorities which affect the inefficiency in the firms.

Abstract Market participants use different tools basically technical or fundamental analysis to have a higher return in constructing a well-maintained portfolio. Examining the efficiency of technical strategies in creating a portfolio is the main objective of this study. Technical analysis is based on using historical trading data to launch selling and buying rules that maximize return and still control risks of loss. We use the adjusted trading data of 50 active stocks in the Tehran Stock Exchange as our sample which includes daily trading data from 2008 to 2019. We construct two types of portfolio; strategy-based portfolio versus random one. Then we calculate abnormal returns of each type of portfolio, applying the Monte-Carlo technique. Using Independent-Samples T-Test to compare means of the abnormal returns, our findings show that there is a significant positive abnormal return for both strategies applied in constructing a portfolio (0.057 and 0.062 mean difference for the first and second strategy, respectively), confirming the higher efficiency of applying technical strategies in portfolio management. Therefore, it is suggested to have and apply a strategy or combination of strategies for trading as an active participant, instead of constructing, rebalancing and maintaining one’s portfolio only by chance, since there will be undesirable results in the long-run.