Advances in Mathematical Finance and Application (AMFA)

The purpose of this study was to investigate the effect of managerial overconfidence as a behavioral bias on the tone of management reports, including directors’ report and management discussion and analysis (MD&A). In this research, the frequency of technical words was used to measure the tone of the management report, and overinvestment was used as a proxy for managerial overconfidence. The hypotheses were tested on 134 companies over a 4-year period from 2017 to 2020, using multivariate regression models in STATA. The results show that managerial overconfidence is not significantly associated with the positive tone of management reports, but is positively associated with the negative tone of these reports. The results suggest that, due to the uncertainties and risks in the economic environment of Iran and their escalation within the time frame of the present research, overconfident managers tend to use a more negative tone when reporting on risks and uncertainties to avoid the negative consequences of overstatement.

Option pricing is a main topic in contemporary financial theories, captivating the attention of numerous financial analysts and economists. Barrier option, classified as an exotic option, derives its value from the behavior of an underlying asset. The outcome of this option is based on whether or not the price of the underlying asset has reached a predetermined barrier level. Over the years, the stock price has been represented through continuous stochastic processes, with the prominent one being the Brownian motion process. Correspondingly, the widely used Black-Scholes model has been employed. Nevertheless, it has become evident that utilizing stochastic differential equations to characterize the stock price process is unsuitable and leads to a perplexing paradox. As a result, many researchers have turned to incorporating fuzzy or uncertain environments in such situations. This study presents a methodology for pricing barrier options on stocks in an uncertain environment, in which the interarrival times are uncertain variables. The approach employs the Liu process and renewal uncertain process, considering the interest rate as dynamic and floating. The pricing formulas for knock-in barrier options are derived using α-paths of uncertain differential equations with jumps.

Nowadays, financial markets and especially the stock market are important and undeniable sources of financing for investment toward the economic growth and development of countries. These markets also have a tangible role as a basis for implementing monetary policy. This study aims to investigate the effect of monetary volatility on the seasonal performance of the Iranian stock market from April 2001 to March 2021.The TEDPIX index of the Tehran Stock Exchange was used for designing and explaining the research model for measuring monetary policy uncertainty in terms of the debt of banks to the Central Bank and to measure the Iranian stock market’s performance. With portfolio theory as the theoretical basis for the study, the housing price index and the exchange rate were added to the research model as other independent variables due to their importance to the portfolio of individuals. In this regard, monetary policy uncertainty was first calculated using the exponential general autoregressive conditional heteroskedastic (EGARCH) method. Then, the effect of uncertainty on the TEDPIX index was calculated using the vector auto regression (VAR) statistical method in EVIEWS 12. The findings indicate a significant negative correlation between monetary policy uncertainty and short and long term TEDPIX index. Moreover, exchange rate and housing price index has a significant positive effect on the TEDPIX index.

Financial statements should have general objectives rather than specific group interests. The possibility of forecasting earnings based on seasonal performance instead of the previous year's earnings and in terms of the contradiction between timeliness and the ability to verify earnings can be a new and thought-provoking issue. The present study examines stock price drift from the content of projected earnings forecast for quarterly operations. The research hypotheses were tested through univariate regression, multivariate regression and correlation coefficient tests using Eviews software. Findings of this study indicate that 1- Profit forecast based on quarterly performance has more verifiability than the previous year (profit stability). 2- The Verifiability of the year profit is more than the profit forecast based on the 9-month performance. 3- Stock price drift is expected on the day after the announcement of earnings and there are changes in earnings compared to the forecast of the previous season. 4- No relationship was observed between the volumes of shares traded the next day and the announcement of the forecasted profit and the changes in the profit compared to the forecast of the previous season.

The objective of the ongoing research is to introduce the initial, substantial, and practical implementation of the Walsh-Hadamard Transform in the realm of quantitative finance. It is worth noting that this particular tool, which has limited utility in the domain of digital signal processing, has demonstrated its effectiveness in evaluating the statistical significance of any binary sequence. Therefore, employing this approach in financial series would be exceptionally noteworthy. By employing five primary tests to assess the randomness of the series, including those pertaining to the Tehran Stock Exchange, as well as copper and gold, the outcomes reveal the presence of randomness in the transformed series in all aspects. Naturally, this random-ness could be examined to identify any underlying trends.

This study aimed to investigate the role of managers' information interpretation on cost behavior. The locative domain of this research is the companies listed in the Tehran Stock Exchange during 2014-2020 and through systematic elimination method, 112 companies have been selected as statistical sample. Managers' information interpretation is considered as an independent variable and cost behavior is considered a dependent variable. The current research is applied research, and if the classification of types of re-searches be considered based on the nature and method, the method of the present study is essentially descriptive in terms of the nature, and in terms of the method is considered in correlation researches category. In this study, library method was used to collect data. In the research data section, data was collected through collecting data of sample companies by referring to financial statements, explanatory notes and stock exchange magazine. In order to describe and summarize the data collected, the descriptive and inferential statistics are used. In order to analyze the data, variance heterogeneity pre-test, F Leimer test, Hausman test and Jarque-Bera test and then multivariate regression test were used to confirm and reject the research hypotheses (EVIEWS software). The results showed that the extent of effectiveness of managers’ information interpretation factors, including changes in managers' consensus on profit, changes in public profit information, changes in private profit information, and changes in bias in profit forecasting on cost behavior in potentially competitive conditions are different from de facto competition. The results obtained in this research are consistent with the documents mentioned in the research theoretical framework and financial literature.

Purpose of this research is Predicting Stock Price Crash Risk with a Deep Learning Approach from Artificial Intelligence and Comparing its Efficiency with Classical Predicting Methods. This research is post-event correlation type and practical in terms of purpose. The research data were extracted from the website of the Stock Exchange Organization and Codal website. The risk variable of crashing stock prices was introduced as a predictor. 3200 obser-vations were obtained from 10-year data of 320 companies between 2012 and 2021. In the following, 29 variables were identified as variables that can affect the risk of crashing stock prices. Statistical methods such as unit root test, composite data, Hausman test and variance heterogeneity test were used. Next, the top 10 algorithms in the field of deep learning were selected and used to model the mentioned variables with the CNN method. Python, Eviews and Excel software were used in this research. Examining the performance of different deep learning algorithms shows that the convolutional neural network method performs better compared to other algorithms and can improve the prediction accuracy. Therefore, it is suggested to use this algorithm in reviewing econometric data and especially predicting the risk of crashing stock prices.

Social trust is considered as an important type of social capital and in fact an informal institution in any country. The purpose of the present study is to evaluate the impacts of social trust and managers' over self-confidence on the investment efficiency. The general method used in this study had been an applied one in terms of purpose and a correlational and fundamental experimental method type in nature. To achieve this purpose, financial information of 130 companies in the years 2012 to 2021 has been extracted and analysed as the research statistical sample. A linear regression method has been used to test the hypotheses. The research findings showed that social trust has positive, significant positive and negative impacts on the investment efficiency, over-investment and under-investment, respectively. In addition, managers' over self-confidence has positive, significant positive and negative impacts on the investment efficiency, over-investment and under-investment, respectively.

Listed companies are always affected by shocks and instabilities in parallel financial markets such as exchange rates and gold. Knowledge of how these impacts are useful for managing companies and investors to make optimal decisions regarding risk management, financing and investment. Therefore, in this study, the effect of investigating the asymmetric effects of parallel financial markets shock on stock returns and financial and commercial risk of 262 companies listed on the Tehran Stock Exchange during the period 2009-2010 using the Generalized Torque (GMM) approach. Been investigated. The results show that the negative and positive shocks of the exchange rate and the price of gold have an asymmetric effect on trade risk, finance and stock returns. These asymmetric effects apply in terms of size, sign and significance. Positive gold price shocks also have a negative effect on trade risk and a positive effect on financial risk, but these shocks do not have a significant effect on stock returns. In contrast, the impact of negative gold price shocks on financial risk is negative and market returns are positive (the impact of negative shock on trade risk is not statistically significant). Based on the above results, it can be stated that corporate operating costs and financing costs are affected by price shocks in the gold and foreign exchange markets.

Social responsibilities could meet the needs of stakeholders invariably. Financial transparency is a key component of social responsibility and the mechanism of corporate leadership principles. The present study aimed to assess the effects and levels of various influential factors in realizing social responsibilities based on the mechanism of corporate leadership principles with an emphasis on financial transparency. We attempted to validate and grade four main factors in this research, including mandating, monitoring, infrastructural, and structural factors. For this purpose, a questionnaire was developed to assess the data of 256 corporate managers and determine the associations between these factors in the form of four main hypotheses using path analysis. According to the findings, the four mentioned factors were confirmed in terms of their impact on the realization of social responsibilities based on the mechanism of corporate leadership principles with an emphasis on financial transparency. In addition, infrastructural factors had the most significant effect on the realization of social responsibilities based on corporate governance then, Monitoring, Structural, and Mandating factors are influential, respectively. Research findings are consistent with the theoretical framework of the stakeholder theory.