Advances in Mathematical Finance and Application (AMFA)

Improving return forecasting is very important for both investors and researchers in financial markets. In this study we try to aim this object by two new methods. First, instead of using traditional variable, gold prices have been used as predictor and compare the results with Goyal's variables. Second, unlike previous researches new machine learning algorithm called Deep learning (DP) has been used to improve return forecasting and then compare the results with historical average methods as bench mark model and use Diebold and Mariano’s and West’s statistic (DMW) for statistical evaluation. Results indicate that the applied DP model has higher accuracy compared to historical average model. It also indicates that out of sample prediction improvement does not always depend on high input variables numbers. On the other hand when using gold price as input variables, it is possible to improve this forecasting capability. Result also indicate that gold price has better accuracy than Goyal's variable to predicting out of sample return.

The bank interest rate is one of the most important macroeconomic variable in each country economic. The purpose of this paper is find the relationship between the facility interest rate and three main variable of the money market.In Iran. this issue for equations the interest rate facility, the interest rate of deposit, inflation and credit risk utilizing the model simultaneous equation system and method three-stage least squares estimated. Results show that in this 32 year period the interest rate of the facility whit the interest rate on deposits is one of the most important macro- banking variable has a positive and significant relationship. So that with an increase in interest rate on deposits, the interest rate of facility also increases. It was also determined the interest rate of facility with inflation has a negative and significate relationship. This expresses with increasing inflation, the facility interest rate decreases. Because in Iran the rate of interest determined as an order, this result is not expected. The interest rate of facility and and credit risk have a positive and significant relationship, which represents it when the interest rate of facility increases, likelihood of non payment increased by borrowers. Also, inflation rate with liquidity and exchange rate has a positive relationship which is consistent reality.

Behavioral financial knowledge deals with the behavior of investors and other users in the capital market. According to the financial knowledge, it is no longer expected that only factors such as accounting information and macroeconomic variables will affect decision-making but also a variety of behavioral variables including manager’s optimism, information influence management, patience of major shareholders and other investors' biases can have an impact on the prices and stock returns. The purpose of this study was to investigate the impact of managers' optimism on the relationship between the patience of major shareholder and information influence management. This study attempts to investigate the relationship between these behaviors and information influence management by measuring the degree of major shareholders patience and managers’ optimism. This is a descriptive post-event research. The study population consisted of the companies listed on Tehran Stock Exchange (n = 87) between the years 2008 and 2017. Multivariate regression was used to test the hypotheses. The findings showed no significant relationship between the major shareholders patience and the information influence management (high, low, balanced). Further, it was found that managers’ optimism has a significant effect on the relationship between the major shareholders' patience and the information influence management (balanced level) and no significant relationship was observed between two other levels.

The forecast is very complex in financial markets. The reasons for this are the fluctuation of financial data, Such as Stock index data over time. The determining a model for forecasting fluctuations, can play a significant role in investors deci-sion making in financial markets. In the present paper, the Black Scholes model in the prediction of stock on year later value, on using data from mellat Bank and Ansar Bank shares in the year 2017-2018, in has been evaluated, and using a numerical method Euler Murayama and computer simulation with the Maple software, for simulated data, gained averages and Standard deviations, confidence interval and their normal histogram are plotted. Also, average of the answers obtained from computer simulations is compared with actual ones, and after ana-lyzing and reviewing the results, performance of the Black-Scholes model has been measured, in stock‌ value prediction. And in the end, this research is com-pared with internal article, and suggestions for future research are raised.

Since the seismic behavior of the earth’s energy (which follows from the power law distribution) can be similarly seen in the energy realized by the stock markets, in this paper we consider a statistical study for comparing the financial crises and the earthquakes. For this end, the TP statistic, proposed by Pisarenko and et al. (2004), is employed for estimating the critical point or the lower threshold, i.e. the point beyond that the market energy follows from the power law (Pareto) distribution. The results confirm the deviation of the energy from the Pareto distribution in the high quantiles of the energy data. The upper threshold that the energy's distribution is changed from the Pareto to another distribution is also estimated by TP statistic. A simulation study is employed for checking out the statistical behavior of the estimated thresholds. Finally, the magnitude of the financial earthquakes is studied. The results indicate that the domestic and the international events have caused the financial earthquakes in Tehran Stock Exchange. Also, the positive relation between the daily energy released and the daily magnitude of the shocks that was connected by Gutenberg and Richter (1956) is confirmed.

The effective role of capital in every country flows through giving guidelines for capital and resources, generalizing companies and sharing development projects with public, and also adding accredited companies stock market requires appropriate decision making for shareholders and investors who are willing to buy shares based on price mechanism. Forecasting stock price has always been a challenging task, since it is affected by many economic and non-economic factors and variables; therefore, selecting the best and the most efficient forecasting model is tough and essential. Up to now applying weighted mean called weighted mean price has been used to forecast industry average price for companies in the stock market and investors were forecasting based on this method. First we have identified 10 accredited banks in TSE and 10 banks in Iran Fara Bourse. In this article, by applying one of the mathematical optimizing techniques, industry means got calculated based on optimized parameters and compared with the industry average; in this statement we strived to find another variable that could forecast with less deviation.  In the following study, by calculating frequency level of deviations, average for price forecasting in banking industry during five years is examined. Finally, the research suggests that, instead of using mean of industry average, it is better to use mean average of golden number, which will lead us to more accurate results.

Recent accounting and management literature shows that demographic character-istics of top management and corporate performance are related. Accordingly, using a two-stage least squares regression model (2SLS), this study examines the relationship between some management demographic characteristics including CEO tenure, gender and level of education with earnings quality and auditor choice. Sample includes the 420 firm-year observations from companies listed on the Tehran Stock Exchange during the years 2013 to 2017 and research hypothesis was tested using multivariate regression models. The results show a significant and positive association between managers education level and higher auditor quality choice. In addition, we find that firms with female directors in the composition of the board of directors and with higher education levels, have higher earnings quality. The current study is almost the first study which has been conducted in Iran, so the findings of the study not only extend the extant theoretical literature in developing countries including emerging capital market of Iran, but also help investors, capital market regulators and accounting standard setters to make in-formed decisions.

The present research proposes an automatic system based on moving average (MA) and fuzzy logic to recognize technical analysis patterns including head and shoulder patterns, triangle patterns and broadening patterns in the Tehran Stock Exchange. The automatic system was used on 38 indicators of Tehran Stock Exchange within the period 2014-2017 in order to evaluate the effectiveness of technical patterns. Having compared the conditional distribution of daily returns under the condition of the discovered patterns and the unconditional distribution of returns at various levels of confidence driven from fuzzy logic with the mean returns of all normalized market indicators, we observed that in the desired period, after recognizing the pattern, all patterns investigated at the confidence level 0.95 with a fuzzy point 0.5 contained useful information, practically leading to abnormal returns.