JOURNAL OF FINANCIAL MANAGEMENT STRATEGY
Year:2018 | Volume:6 | Issue:3 (22)|Papers Count:8

In this research, it is attempted to present a framework for estimating and predicting systemic risk in Iran capital market using the marginal expected shortfall approach (MES), which has recently been considered in systemic risk literature. On this basis, MES as a systemic risk measure, will be analyzed in terms of assumptions for market and firm returns as a function of mean, volatility, correlation, and tail expectations and its components will be measured using an ARMA-GJR-GARCH-DCC framework and a nonparametric tail expectation estimator. In this way, a weekly panel will be created from the company's MES. On the other hand, the systemic risk is built up in a period that looks calm and low fluctuations, and is accumulated until activation. In other words, systemic risk potential increases as fluctuations decrease. In this study, it was attempted to predict systemic risk by taking advantage of the panel structure of the data and the relationship between MES and firm-specific variables that are available in certain sections.

Generally, it is believed that disrupted trades would cause market failure, and understanding the causes of inefficiencies has always been a real challenge in financial literature. In this regard, the present study investigates the existence of noise traders in Iran stock market and tests a quantify model for measuring noise trader risk, overreaction, underreaction and mispricing. For the first time, the most viewed stocks were used to construct a behavioral index. This new index can be used to identify noise traders and applying this index can be obtained more accurate estimate of the beta in compared with market index. Also by using CAPM and BAPM models during the period from 2011 to 2016 for 96 companies, it was shown that Iran stock market has a significant behavior error. Furthermore, the results of information-adjusted noise model (IANM) show that noise traders are active 100 per cent of the time on the Iran stock market and make it inefficient. The most type of inefficiency in this market is overreaction in 46. 67 percent of the time and then information pricing errors and under reaction are in 45. 63 percent and 7. 71 percent of the time, respectively. The findings of this study are useful for understanding the market atmosphere and new behavioral index can be used as a proxy of Iranian investors` sentiment.

Stock price forecasting is one of the main challenges in stock market which investors and analysts are faced with. To forecast the future prices and future trend, different tools have been used among which we can refer to technical and fundamental analysis. It is noticed that technical analysis has good performance in short-time forecasting. Hence, in this paper, technical analysis has been used to estimate the probability function of stock prices. To forecast the direction of stock price movement in the following day, artificial neural networks (ANN), Logit, Probit, and extreme value models are utilized. To evaluate the performance of proposed models, daily values of Iran Khodro company stock are considered as a real case study. The nonparametric test of equality of ratios shows that the difference between the forecasting results of different models is not statistically significant. However, according to forecasting error criterion, the Probit model is more efficient than other mentioned models.

The development of guides and information resources in this area is one of the effective measures for capacity building and the elimination of possible ambiguities and misunderstandings about public-private partnerships. In this study, we rank the participatory financing methods and identify the best ones based on four indicators including risk, duration, efficiency and working capital for investment in urban projects, as well as the importance of each method of participation in various urban areas. The sample consisted of municipality experts and private sector investors. The data gathering tool was a questionnaire and the AHP method, descriptive statistics; confirmatory factor analysis and Friedman test were used. The results of the analysis of AHP hierarchy showed that the best financing methods from the experts’ view are construction, operation and transfer, concession, lease, purchase of services, operations and management.

The purpose of this study was to compare the predictive power of fuzzy neural network with fuzzy wavelet neural network in predicting stock prices of banks in Tehran Stock Exchange. The period of this research was from 2011 to 2016. In this research, the fuzzy logic system with the use of a multi-layer neural network system with an error-optimized back-propagation optimization structure and a Maximum Overlapping Discrete Wavelet Transform for exchange rate variables, opec oil, each ounce of gold, the total stock index as well as the volume of trades were used in order to predict stock prices. The results of the model were done by using the updated cost function. The results of the research in comparison of fuzzy wavelet network and fuzzy neural network showed that the reliability of banks with fuzzy wavelet neural network is over 90% and with fuzzy neural network above is 80%. As a result, fuzzy wavelet neural network is more reliable than fuzzy neural network.

This paper investigates the effects of margin changes on trading volume and open interest of Bahar-e-Azadi gold coin future contracts in Iran Mercantile Exchange (IME). The estimation method in this research is Ordinary Least Squares (OLS) technique simply. An approach was used in which margin changes effect on trading activities through opportunity cost and transaction cost. For this purpose, time series data of three successive maturities were used as first maturity, second maturity and third maturity. The results show that there is a positive significant relationship between margin changes and trading volume for all three maturities, while there is no significant relationship between margin changes and open interest. Therefore, according to findings, margin changes cannot decrease trading activities through increasing opportunity and transaction costs. So, if our purpose of reducing the margin is to decrease in trading volume and affect open interest, according to the research finding, it cannot act as an appropriate tool.

The purpose of this paper is to study the three momentum strategies based on a sample of the Real Estate Investment Companies. The profitability of the momentum strategy has been extensively investigated in several financial markets, including foreign currency, commodity, corporate bond, American depositary receipts, residential real estate and Real Estate Investment Trust markets. The momentum premium that involves buying past winner stocks and short selling past loser stocks is one of the most pronounced and prevalent anomalies documented in asset-pricing literature. The sample consists of six companies listed in Tehran stock exchange from April 2011 to March. Comparing across three momentum measures, it was empirically found that the residual momentum strategy plays a dominant role in generating momentum profits. The profitability of these strategies, however, varies with the state of the investor sentiment. Specifically, it was found that the price momentum and the 52-week high momentum earn significantly positive returns following optimistic period.

Financial distress analysis is considered as an important phenomenon for investors, creditors and other users of financial information. Determining the factors affecting the probability of a company’ s financial distress before occurrence of the distress is considered a very interesting and attractive issue and can be useful for managers and investors and creditors. In this study, using the information of 1488 year/companies during the period 2011 to 2016 in Iranian stock market, and through structural equations approach and simultaneous use of Iranian and global models and PLS software programs, the factors affecting financial distress have been investigated. The results of the research indicate the significant impact of risk criteria and corporate governance, compared to financial and accounting performance criteria and macroeconomic variables, on financial distress.