FINANCIAL KNOWLEDGE OF SECURITY ANALYSIS (FINANCIAL STUDIES)
Year:2021 | Volume:14 | Issue:50|Papers Count:12

Exploration of knowledge from database and data mining is one of the most important tools for customer relationship management Which can help the organization to find useful information or their interesting knowledge. Today, banks and insurance companies have numerous and extensive databases that contain information about exchanges and other details related to their customers. Valuable business information can be retrieved from these data warehouses. However, support for such analyzes and decision-making will not be possible using traditional reporting languages. Therefore, considering the importance of the common customer’ s information of the bank and insurance, they should be analyzed as carefully as possible. In this research, by collecting and analyzing the information of joint customers of the bank and insurance, a methodology based on the data mining is presented to evaluate customers according to their functional indicators in the field of banking and insurance. We will also predict the behavior of new customers by analyzing historical customer behavior using a two-step approach based on unsupervised learning and supervised learning.

In this study, the long-term memory of the stock market index and exchange rate (dollar) was estimated using detrended fluctuation analysis. In order to detrend the data, the GARCH approach was proposed and the long-term memory estimation model was implemented separately for both conventional and GARCH methods. For this purpose, daily data of stock market index and dollar exchange rate in the market during the years 2014 to 2020 were used. The results showed that the conventional method in calculating the detrended fluctuations is not able to estimate the long-term memory of the exchange rate, while the results for the stock index showed the existence of short-term memory. The results showed that the proposed method in detrending data and calculating detrended fluctuations based on Garch model has a higher power in controlling changes in market fluctuations and according to the findings of this method, stock index and dollar exchange rate have long-term memory. The results showed that these two methods provide significantly different estimates of long-term memory of the market and according to the results of the correlation test between the values ​ ​ of long-term memory of data and the value of parameter q in detrended fluctuation analysis; it was observed that the stock market index and exchange rate in Iran have multifractal properties.

The purpose of this study is to explain the role of firm characteristics on corporate merger and acquisition financing based on Pecking Order Theory during the years 2014 to 2018. In this study, the company's characteristics are measured from the variable company's size, asset structure, profitability, ratio of interest cost cover, liquidity and Return on assets, and to measure the variable financing methods, using a Pecking Order model of accumulated profit, debt ratio And financial leverage has been used. The results of this study, using regression of combined data, show that the larger the company and the more profitable it is, the company uses less profit from financing its investment projects and turns to external resources. While increasing the return on assets, the company uses more accumulated profits and financial leverage than debt. In other words, companies do not follow a Pecking Order Theory in providing all the funding they need. Also, in this study, no significant relationship was observed between asset structure, ratio of interest cost cover and liquidity with financing sources.

The goal of this study is analyzing investors’ decision based on different levels of investment opportunities based on risky and no risky approach on share return prediction ability. In this study, concentration is on using three regression models with replacement ability on variable coefficient of investment opportunities levels that can analyze share return prediction ability from viewpoint of risky and no risky investors. Study sample includes 113 firms from accepted companies in Tehran stock exchange that includes a time period of ten years from the beginning of 2008 to the end of 2017. Results show that only when investment opportunities level is average, share return prediction ability based on risky approach exists, and against, when investment opportunities level is high and low, share return prediction ability is performed based on investment no risky return. Thus, investment opportunities level has important roles on basis of no risky approach on share return prediction ability.

Development of capital markets, the dissemination of corporate risk information is considered as an important responsibility for companies in a competitive market that helps shareholders and investors to make more effective decisions in the market. But due to the lack of norms and companies' actions regarding risk disclosure, investors today are skeptical about the capital market. In this context, the development of perceptual symbols in investors in the form of cultural theories; Economic and social is considered as one of the dimensions of strengthening the court accounting model, which can help strengthen the attractive effectiveness of foreign investments in the capital market. The purpose of this research is Forensic Accounting Paradigm in Changing Approach to Risk Disclosure of Capital Market Firms: Development of Homology Theory to Symbolize Investors Perception. This research is applied in terms of purpose and descriptive-correlational research in terms of data collection due to the study of data related to a specific time period, the method of data analysis is cross-sectional and based on the method of path analysis/partial least squares. In this study, in order to measure the external variable, Forensic Accounting model, a standard questionnaire has been used and to collect data related to risk disclosure based on the two present criteria, the data of Tehran Stock Exchange companies have been used. The statistical population is 102 CEOs of companies listed on the Tehran Stock Exchange that were surveyed over a period of one year. After ensuring an acceptable fit of the measurement and structural models of the research, in accordance with the development of homology theory to symbolize the perception of investors, the results showed that the pattern of consolidation of court accounting has a positive and significant effect on the risk disclosure approach of capital market companies.

The present study examines the economic dimensions of quality of life and its effect on financial stress. The research method is mixed (qualitative-quantitative). Qualitative data have been collected through Grounded Theory. In-depth interviews with experts have been conducted with a snowball approach. The results show that the indicators of lack of planning and rapid economic change as the most important causal conditions, physical health, lifestyle and occupation as the most important intervening factors, materialism, stability in society and no change in the work policies of active organizations as a prevailing context, abilities and the structure of community resource distribution as the most important action strategies and the lack of security and the lack of a sense of social justice as the most important consequences are affected the economic dimension of quality of life. Quantitative data were collected in a survey of capital market participants. The results show that with the improvement of quality of life in terms of actions and reactions, prevailing context, consequences and intervening factors, the amount of financial stress among capital market participants will decrease but will increase in terms of causal conditions.

According to capital market research, the negative shock of stock price in any market is a function of environmental factors and specific characteristics of the company and any insight into how to describe and predict the shock can influence the decisions of investors and stakeholders. In this study, based on the data related to 96 financial ratios of 140 companies listed on the Tehran Stock Exchange during a period of 9 years between 2010 and 3012, we have predicted a negative shock of stock price based on the meta-heuristic approach. In this research, in order to extract the optimal financial ratios, genetic algorithms and particle swarm optimization have been used. The proposed model is then tested using these extracted features by a support vector machine with a radial core and an artificial neural network. The results showed that the variables extracted from the particle swarm optimization algorithm, together with the support vector machine learning algorithm, create better results for predicting shocks (temporary and permanent) and their number.

Selecting the most accurate method of risk measurement is the main challenge in risk estimation. This study aims to measure value at risk (VaR) and conditional value at risk (CoVar) in Tehran Stock Exchange (TSE) using the Frechet distribution (FD) and the generalized Pareto distribution (GPD). It used the data from 21 and 63-day time series of TEPIX, free-float, and the indices of the top 50 TSE companies between 2012-3-20 and 2020-3-19. It used COPIC post-test and Christoffersen’ s conditional coverage test for models statistical confirmation. It applied Lopez and Blanco-Ihle’ s second loss functions for model comparison. The CoVaR models were ranked by two loss functions, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). Results indicated according to the statistics measured in Lopez’ s second loss function, GPD performed better than FD in measuring VaR of TEPIX and the return index of the selected 50 companies, however, FD performed better for the free-float index. Blanco-Ihle’ s loss function results contradicted the ones derived from Lopez’ s second loss function. MAE and RMSE results indicated FD is better in estimating CoVaR.

In the research, Explain the role of financial and economic variables on stock returns With Markov-switching Model. Based on information collected from the two codal. ir and TSE. ir websites, availability and momentum. contrarian strategies have been dealt with in the market. From among 318 companies from those listed in Tehran Stock Exchange for the period of 2011-2017 and based on restrictions, 108 companies have been selected as research samples. Then, using various statistical models, three statistical models of autoregressive time series (with no help from auxiliary variable), linear regression, and Markov-switching have been applied. Using the model’ s goodness of fit criterion, these three models have been compared and the best one has been selected. Based on selected model, stock trading strategy for the next 12 months has been predicted. To collect data and perform statistical analyses, Excel spread sheet, as well as SPSS and R software packages have been used. According to the findings, from among minor variables (base volume, institutional investment, and free float) and major variables (currency and inflation rates), only three variables from minor types (base volume, institutional investment, and free float) have been effective on stock trading strategy; and, these variables can be used as auxiliary variables to predict return on stock and to specify stock trading strategy in future, as a result.

In firms where language tone of management in risk disclosure is conservative, more verification is required to disclose good risk news and a lower standard of verification is required to disclose bad risk news. This type of risk disclosure can reduce the likelihood of negative news accumulating in these firms and decrease the stock price crash risk. Accordingly, the purpose of this research is to investigate the effect of conservative tone in risk disclosure on the stock price crash. The research period is from 2009 to 2018 and the sample number included 167 firms listed in Tehran Stock Exchange. Panel data approach and multiple linear regression were used to test the research hypotheses. The findings show that Conservatism in Risk Disclosure(CRD) reduces the stock price crash risk by requiring asymmetric verification to disclose good and bad risk news. Also, given the high information asymmetry, the role of CRD as a limiter to managers' motivation to accumulate negative news and prevent the stock price crash increases.

This article has studied several fluctuations in the Iranian currency market and multiple turmoils in the economy that have not only wiped out Iranians private savings but also affected financial market activists to provide a better understanding of fluctuations' movement between markets. To doing so, we used the exchange rate in the open market (in some periods, the black market) as one variable and the Tehran Stock Exchange Index (TEDPIX) as a second in the form of multivariate conditional heterogeneity variance (MGarch) model. According to the results, the time series suggests multiple structural breaks from Dec. 2018 to Jan. 2020. Using the so-called GLS-Based unit root test, we observed five structural breaks that produced stationary problems at the level and no evidence of stationary problems at the return of the data. Also, by using DCC and FDCC models we confirm that there is a fluctuation between the two markets during the period. This overflow shows a different performance if structural failures are considered.

This article has studied several fluctuations in the Iranian currency market and multiple turmoils in the economy that have not only wiped out Iranians private savings but also affected financial market activists to provide a better understanding of fluctuations' movement between markets. To doing so, we used the exchange rate in the open market (in some periods, the black market) as one variable and the Tehran Stock Exchange Index (TEDPIX) as a second in the form of multivariate conditional heterogeneity variance (MGarch) model. According to the results, the time series suggests multiple structural breaks from Dec. 2018 to Jan. 2020. Using the so-called GLS-Based unit root test, we observed five structural breaks that produced stationary problems at the level and no evidence of stationary problems at the return of the data. Also, by using DCC and FDCC models we confirm that there is a fluctuation between the two markets during the period. This overflow shows a different performance if structural failures are considered.