FINANCIAL RESEARCH
Year:2018 | Volume:20 | Issue:3 |Papers Count:10

Objective: The aim of this study is to model arrival process of informed and uninformed traders into Tehran Stock Exchange (TSE) as well as to assess the interaction between two types of traders which is an important yet neglected topic. Methods: In this study, a sequential trade model was estimated based on trading data of 33 stocks belonging to 11 industries of TSE during the period from 2013 to 2016 using Nelder-Mead algorithm. Results: In TSE, an unexpected rise in unbalanced trades in current single day increases the expected arrival rate of both types of traders in the next day. The arrival rate of informed traders shows lower persistence compared to that of uninformed traders in the TSE. In addition, it showed negligible sensitivity to trading intensity. The presence of informed traders doesn't necessarily lead to a decrease in the number of uninformed traders in the TSE. Conclusion: Similar to previous studies about stock markets in different countries, the presence of informed traders in TSE mainly depends upon their information-related advantages. Unlike most prior studies, the arrival rate of uninformed traders in TSE is not significantly affected by the arrival rate of informed traders.

Objective: Nowadays, financial distress prediction is one of the most important research issues in the field of risk management that has always been interesting to banks, companies, corporations, managers and investors. The main objective of this study is to develop a high performance predictive model and to compare the results with other commonly used models in financial distress prediction Methods: For this purpose, sequential floating forward selection that is considered as the generalized form of sequential forward selection method and as one of the wrapper methods, and sequential forward selection method in combination with support vector machine were used. These models are combined models of feature selection and classifier. Logistic regression model which is a statistical classification models, has also been used in the present study. Results: After reviewing the important financial ratios, 29 financial ratios that were mostly used in previous researches were chosen. Paired T-test results showed that with a 95% confidence level. The proposed model provides higher accuracy than other models used in this study. Conclusion: Results showed that the proposed model of this research has significantly better performance in predicting financial distress than the sequential forward selection method and Logistic regression model in one year, two years and three years before financial distress.

Objective: The main objective of this research is to explain the accrual anomaly using accruals factor (CMA) and accrual-based factor-mimicking portfolios as well as checking whether accrual anomaly is risk-based or mispricing. According to rational frictionless asset pricing model, the ability of accruals to predict returns should come from the loadings on this accrual factor loading that predicts returns. Methods: In this research, to test the hypotheses of time series regression and also, the four-factor pricing model is used to analyze accrual anomaly. Results: Our tests showed that it is the accrual characteristic rather than the accrual factor loading that predicts returns. These results indicated that investors evaluate the accrual characteristic in an incorrect manner and cause doubts on the rational risk explanation Conclusion: We can conclude that there is a relationship between accruals and returns, and this comovement is attributed to mispricing of investors. In other words, accrual anomaly results from mispricing.

Objective: There is a large theoretical literature regarding stock market manipulation. However, empirical evidence of manipulation remains scare especially in emerging markets like Iran. So, it is vital to detect and prevent. Manipulation distorts prices, thereby reducing market efficiency and harms public confidence. Distorted prices increase market volatility and risk. This study empirically investigates which firms are more susceptible to successful deceptive manipulation. Methods: We collect the data set consisting of manipulation cases of entering spoofing order in Iranian stock market in two periods. In the first three-month period, the index was downward and in the second one, it was upward. Panel Logit regression was used to determine and interpret results. Results: The regression results showed that small firms, with high trade volume, low information transparency, high information asymmetry and high volatility are more prone to stock price manipulation. Indeed, there is an inverse relationship between manipulation and fluctuation index. Conclusion: Index change is an effective variable on stock manipulation. Manipulation is more probable in bear market, because most of the stocks are upward in bullish markets and there are fewer motives for stock manipulation.

Objective: One of the common tools used to make pre-trade transparency in financial markets is the Limit Order Book. In spite of several researches on the Limit Order Book, there is no consensus about the impact of increasing pre-trade transparency on market quality. Increasing the number of Limit Order Book levels from 3 to 5 levels, in May 2017, for online traders in Tehran Stock Exchange, gave us the opportunity to study the issue. Methods: In this paper, based on the even study, we examined the impact of transparency of Limit Order Book on market quality using Quoted Spread, Effective Spread, Market Depth and Return Standard Deviation. Results: According to the results, after increasing the number of Limit Order Book levels in Tehran Stock Exchange, there was a significant decrease among all of the selected criteria with the exception of Market Depth. Conclusion: The results indicated that increasing the number of Limit Order Book’ s levels for online traders in Tehran Stock Exchange led to deterioration in the market quality.

Objective: The present study aims at investigating the behavior of realized volatility for high-frequency data of Tehran Stock Index from April 28th, 2012 to August 8th, 2018. Methods: Three different types of HAR models including of HAR-RV-CJ, HAR-RV and HAR-RVJ were used to analyze the Realized Volatility. Results: The obtained results of three diverse models revealed that the estimated Realized Volatility in market was described appropriately by the traders who work daily and in the framework of HAR-RVJ model. Moreover, based on the Heterogeneous Market Hypothesis, we found out that in comparative performance for all of time horizons in this study, the results of four evaluative criteria (including of MSE, RMSE and etc. ) in HAR-RVJ model is lower than HAR-RV-CJ and HAR-RV. Conclusion: The in-sample forecasting performance of HAR-RVJ, in relation to Future Volatility of Tehran Stock Exchange Index, was better than the results we obtained from the alternative models in the study (HAR-RV and HAR-RV-CJ) and the best scores were observed among all the criteria. In addition, for the out-of-sample analysis, the simple HAR-RV model had superiority over the other two models only in the Monthly time horizon.

Objective: In general, financial time series such as stock indexes have nonlinear, mutable and noisy behavior. Structural and statistical models and machine learning-based models are often unable to accurately predict series with such a behavior. Accordingly, the aim of the present study is to present a new hybrid model using the advantages of the GMDH method and Non-dominated Sorting Genetic Algorithm II (NSGA II) to, more accurately, predict the trend of movement and volatility of Tehran Stock Exchange Price Index, and to compare its ability with the ARIMA model based on RMSE, MAPE, and TIC error assessment criterions. Methods: For this purpose, the data of Tehran Stock Exchange Dividend and Price Index (TEDPIX) was used during the period of October 2008 to September 2013. The hybrid model NSGA II-GMDH utilizes the GMDH network as a model resistant to nonstationary and noisy data for prediction and uses the NSGA II multi-objective optimization algorithm to minimize predictive error and select the optimal input variables. Results: The results of the proposed hybrid model in this study indicated a lower error and more prediction accuracy compared to ARIMA model for out-of-sample data based on all three error criterions. Conclusion: The empirical findings of the study showed that the proposed model has higher flexibility and capability in covering unstable changes in the total index movement trend.