FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT)
Year:2020 | Volume:11 | Issue:42|Papers Count:18

Profit smoothing can be seen as a deliberate reduction in profit fluctuations, so that the activities of the company appear to be normal. Managers are making profit smoothing to reduce this volatility. Some experts believe that investors are more willing to invest in smoothing companies and are willing to pay more for them. Researchers believe that some of the characteristics of the company influence the motivation of managers to smooth profits. This study attempts to explain the theoretical foundations of the research, the relationship between earnings smoothing and company characteristics such as earnings quality, P/E and ROE and ROTA. Check the securities. In order to investigate the relationship between earnings smoothing and firm characteristics, data related to the period 2010-2017 were collected and analyzed. Logistic regression was used to test the research hypotheses. The results show that companies with higher price to earnings (P/E) ratios have more incentive to report earnings. And companies with higher earnings quality are more motivated to report earnings smoothly. Finally, it was found that larger ROTAs had a greater incentive to report earnings.

Portfolio optimization is one of the most important issues in financial sciences. Various strategies have been used to manage stock portfolios, which can be categorized into types: active and passive strategies. One of the most important passive portfolio management approaches is to form an index tracking portfolio. The purpose of index tracking portfolio is to its performance replicate market index as benchmark as closely as possible with a limited stocks in portfolio which will result in lower transaction costs for investor. In this research, a model for index tracking problem is proposed which aims to minimize undesirable deviations and maximize desirable deviations. Finally, a genetic algorithm is used to solve the leading model. To evaluate the performance of the model, data from four major industries of Tehran Stock Exchange has been used. The results show that the proposed model has a suitable performance in tracking the relevant index and achieving excess return over the benchmark.

The purpose of this study was to develop Bayesian modeling of returns volatility and turnover volumes. On this basis, the volatility of the Tehran Stock Exchange index returns and its trading volume have been studied with daily, weekly and monthly frequencies during the period of 21 April 2015 to 27 February 2019. The research findings show that the CCC model assumption of constant conditional correlation between the variables is violated and it is observed that the existing relationship is a dynamic conditional correlation type of DCC model and a negative one which implies that with increasing returns, investors due to optimism is less reluctant to sell their stock and by failing to sell it reduces the volume of transactions in the market and vice versa. On the other hand, the research findings show that considering the skew student t distribution for errors with a wider tail than the normal distribution and skewness application, it has a better performance than the other distributions.

Given the importance of gold prices in financial markets and the economic effects of price fluctuations, the trend of gold price changes in the national and global economy has attracted the attention of many researchers and economic analysts. Therefore, the main purpose of this study is to predict the trend of the global gold price movement. The purpose of this study was to introduce a combined model of the GARCH-Classic and the GARCH-Copula models and to compare them with the Garch family models in order to predict the global gold price trend in the period 01/04/2002 to 26/06/2018. The forecast horizons are 1, 5, 10, and 22 days. The prediction accuracy of these models has been evaluated and compared using RMSE error criterion. Results showed that in short-run prediction horizons, the normal Capula model with GARCH-t distribution and in long run prediction horizon, Capula-t model with the distribution of GARCH-t performs better than competing models. The hybrid model presented in this study has a high potential for predicting the trend of global gold price movement, so using this model for different sector investors, economic analysts, as well as country macro planners, can have valuable results.

As societies transition from the industrial age to the information age, the importance of intangible assets has increased in the business world. The wave of companies moving to invest in intangible assets and the formation, encouragement, and inclination to create knowledge-based and technology-driven corporations have been indicative of changing business models, strategies, and moves toward a developed economy. However, accounting standards for intangible assets appear to have been neglected, resisting the shift to alignment with economic change. The purpose of this research is to design a model based on the views of key users for reporting intangible assets. This research is a qualitative research using a grounded theory approach. The statistical population consisted of auditors, official justice experts, bank creditors, managers of investment firms and managers of knowledge-based corporations, using a purposive sampling approach in total of 15 individuals selected as research participants. Data were collected by interview and semi-structured and then, using content analysis, the main themes were identified and their network was drawn using NVIVO software. The categories of causal, strategic, and consequential were introduced, and financial reporting was identified as an intervening variable, "useful decision making information"a component that could be seen in all categories. It was useful to know how to make decisions about intangible assets. The title of the pivotal category was introduced.

Given the development of machine learning models in predicting financial data in recent years, this study introduces a combination of Deep Learning Network and selected GARCH family models to predict short-term daily returns of the Tehran Stock Exchange Index. The most important feature of the deep learning network is that it can adapt and adjust itself to the volatility of market variables without being limited to specific models. In this study, short-term and long-term memory based neural network (RNN-LSTM) models are used for deep learning network models and GARCH and EGARCH models are used in its structure. Also, the two independent variables of oil price and dollar rate in the structure of the hybrid model help to predict the financial data more accurately. Comparison of the results of hybrid model prediction error with individual models shows that the RNN-LSTM-EGARCH hybrid model has higher prediction accuracy than competing models. competing models.

With the growth of private banks, financial and credit institutions, competition for better services has increased. Given the importance of the issue, it is necessary to develop a comprehensive model for evaluating banks. Every organization needs to evaluate its performance to understand its strengths and weaknesses, especially in dynamic environments. The issue of performance appraisal is so widespread that even management experts say: "What cannot be evaluated cannot be managed". Banks, like other organizations in Iran, need performance evaluation to provide more diverse and faster services as well as their development. [6] This study aimed to present a model to evaluate the performance of banks listed in Tehran Stock Exchange using data mining approach. In this research, four data mining models of decision tree C5. 0, decision tree C4. 5, Naive Bayes classifier, and random forest were implemented and compared to evaluat the performance of banks. To this end, 28 financial ratios (e. g., profitability ratios, liquidity, quality management, asset quality, and capital adequacy) in 18 banks of Tehran Stock Exchange during 2014-2017 were selected as independent variables. In addition, the performance of banks in three categories of acceptable, unacceptable, and moderate was selected as the dependent variable of the study. According to the results, the decision tree C5. 0 with the accuracy of 94. 4% was the most efficient model proposed in this research.

Valuation and pricing of securities the process of estimating the value of securities is one of the initial shares of companies. Because, on the one hand, investors need to know in a conscious investment that knows the true value of the stock they are interested in investing in and on the other hand, the owners of companies that are going to sell their securities have to evaluate and value their assets in a proper manner. Therefore, the purpose of this study is to explain the optimal model of evaluation and pricing of the initial public supply of shares of companies accepted in Tehran Stock Exchange using fuzzy multi-criteria decision-making techniques, stepwise regression, neural network and genetic algorithm. To this end, data on 421 companies were collected that during the years 2006 to 2018 launched a public offering of shares on the Tehran Stock Exchange. Fuzzy AHP method, forward regression, neural network and genetic algorithm are also used to analyze the data. The results of the research showed that the genetic algorithm model is the optimal pricing model and initial stock valuation.

The purpose of this study is to identify and validate the antecedents and consequences of blockchain acceptance in Iranian financial markets in order to provide the necessary background for assessing the readiness of financial markets for accepting blockchain technology. The implications and implications of blockchain acceptance at four levels, 12 variables, and 53 indices have been extracted from similar research literature in e-commerce and mobile banking. To validate the research indices, in addition to the documentary study, a fuzzy Delphi technique was used to refine the indices, the survey was carried out in three stages, and the results of each step were refined. Data analysis has confirmed 39 indicators. In fact, the results of this study provide useful insights for financial market researchers and policy makers in Iran to evaluate these factors so that they can utilize blockchain applications in the Iranian financial markets by changing the business model used in financial markets.

This paper survey beliefs of investor on Tehran stock exchange at three break point date (BPD). at first three BPD with several criterion: average of the share price indices, average value of the stock market turnover, average value of the stock market capitalization. according this three BPD are: the election of Mahmood Ahmadinejad at 2005, financial crisis at 2008 and the election of Hassan rouhani at 2013. In addition this paper is base of Brock and Hommes heterogeneous agent model (HAM) framework. Samples are the shares of companies that 40 days before and 40 days after was traded. then with MATLAB software code was writhed and simulation done. Finding shows that strategy of contrarian trend chaser is the best and we can with genetic algorithm optimize average and standard deviation of coefficient of investment strategy and adaption with real market at break point dates.

During financial stress, the impact of financial stress shocks on economic activity may differ from what is usually observed at normal times. Therefore, it is appropriate to consider the effects of financial stress on economic activity and inflation during the period of financial instability. In this paper, hence, the effect of the deterioration of financial conditions of the Iranian economy on macroeconomic variables between 2012 and 2017 has been investigated. For this purpose, in this research, we intend to study the impact of the fluctuations of the financial stress index on inflation, interest rates, liquidity, and industry index by developing the financial stress index using representatives from different markets. Therefore, using the GARCH two-variable BEKK model and also the VAR model, the effects of shocks and fluctuations between them were tested and then the relationship between them was investigated by Granger's causality test. The results indicate that there is a two-way relationship between the financial stress index and inflation, interest rate, and liquidity, but in examining the causality between the financial stress index and the industry index, the results of the causality test indicate that the industry index itself, in the long run, triggers changes in the financial stress index, but the financial stress index has no effect on the industry index.

In this research, the GARCH-EVT-COPULA method is investigated to determine the dependency structure and portfolio risk estimation on the foreign exchange market data in Iran. GARCH-EVT models are used to mariginal distribution of each of four currency returns series. For the joint model, we choose five copuls with different dependence structure such as Frank, Clayton, Gumble, Normal and t-Student copulas. In this research portfolio risk is measured using VaR and CVaR. The statistical sample of this study is the daily exchange rate of USD, EURO, Pound and AED for the free market with 5 working days from September to the end of 1396. Based on the results of the research, using the Akaike information criterion values, the t-student function is the best fitted copula model for investigating the dependency structure. Exchange rates have the same upper and lower tail dependencies. Accordingly, in the markets for boom (severe positive) and stagnation (severe negative), the dependence between the two exchange rates is the same.

The purpose of this study was to investigate the effectiveness of financial health indicators as indicators of banking financial crisis by applying multivariate logit models (case study of banks accepted in the stock exchange). For this purpose, among the banks accepted in the exchange, 9 banks were selected for sample. This research is a correlation research and the methodology of the present research is post-event type. A combination of two methods of field and library was used for collecting data. We used logistic regression to analyze the findings. We also used the fuzzy technique for the AHP technique to prioritize the main criteria. According to the findings of the research, according to the choice of the Enter method for data entry, based on the sig statistic, the 4 variable variables (LQ4, LQ1, CA1 and LQ2) were significant. Based on the results of the Logit model, only four financial ratios among the ratios of Kaml introduced in the correct ranking of the banks studied are based on Camel's combined value. Also, according to Delphi technique, the quality of management with the normal weight of 0. 221 is the highest priority. The asset quality with a normal weight of 0. 104 in the second priority, a confidence with a normal weight of 0. 085 in the third priority, capital adequacy with a normal weight of 0. 075 in the fourth priority and profitability with The normal weight of 0. 070 was in the top priority.

Several studies have examined the relationship between investor's mood and economic conditions. This paper examines the effect of investor sentiment on expected return on capital by considering changes in Lucas's model, considering equity and equity market prices in the context of pricing models and in the context of minor changes. How the slight changes in the mood factors (time preference and risk aversion affect stock prices) are determined and calculated. The results show that: a) The expected rate of return on stocks is inversely related to the investor's mood state; b) When the investor has a good mood state, the effect of the mood state on the expected return on capital increases; c ) The variables of the mood state of affairs are more influential on the investment markets than on the securities market; d) The investor's mood state is an essential factor in the stock return; e) Integrating the investor's mood state with asset pricing patterns can help interpret existing evidence of growing maladies related to investor behavior.

Objective: In this study, the changes of the stock price of Iran Khodro Company listed in Tehran Stock Exchange (TSE) has been studied on the issue of prediction modeling during of 9/13/1387 to 13/12/1396 based on Geometric Brownian Motion (GBM) model generalized by the Markov switching regime (MSR). Methods: The research model was designed by system dynamics (SD) approach and Vensim DSS software in the causal-loop diagrams (CLD) firstly and then after specifying the flow-state variables, mono-loop and two-loop stock– flow diagrams (SFDs) was designed and daily final stock price was simulated. Two-parameter of noise seed and time step were identified and applied as sensitivity analysis parameters. Results: The simulation error was estimated for the random variations of the noise seed and the time step configured by default user parameters up to 22/74 and 30/35 percent, respectively. Both parameters were calibirated due to higher simulation error than acceptable error of 15 percent. Trial-error and field observation methods was performed in order to appropriate estimation of the calibration parameters range. The post-calibration accuracy of simulation per noise seed parameter increased from 77/26 to 91/5 percent and per time step from 69/65 to 96/37 percent. Conclusion: Findings indicate that the error roots have reached to the ideal mode by optimizing of the calibration parameters as covariance inequality error approached to one unit and base inequality error and variance inequality error approached to zero and indicate functionality accuracy of the GBM generalized by the MSR in stock price simulation.

The disclosure of risk elements contained effective messages in assessing the impact of future events in the company. Therefore, the conscious judgment of consumers is the usefulness of providing supplementary information from the risk elements of categorized factors on the different functions of firms and improving the quality of accounting information. thus, the purpose of this study is to evaluate the usefulness in decision makers with disclosure of risk elements and economic consequences of companies performance. for this purpose, the research with 87 sample companies and in the 7-year period of 1396-1390 in the descriptive-correlation method showed that the disclosure of data elements of firm-level risk elements contained useful information in predicting the risk of fall of stock market prices but no significant relationship was found at the industry level. The disclosure of risk elements at the firm level of the industry contains useful information in predicting the synchronization behavior of firms ' stock market prices. The greater the disclosure of the risk elements has greater advantage, making a greater impact on the prediction and explanatory power of stock price and stock price appreciation.

Banks, as levers in macroeconomic policies, by regulating and adjusting the bank's interest rates, enforce monetary policies and controls inflation and unemployment, which is one of the most important macroeconomic goals. One of these tools is asset-debt management. Therefore, the purpose of this research is to develop the Asset Correlation Risk Model (ACR) with the Asset-Liability Management approach (ALM). This research is descriptive in nature and in terms of its purpose. The statistical population of the research is the companies accepted in the Tehran Stock Exchange and the sample of the banks accepted in this collection, which can be extracted from the research data. The research period is from 1391 to 1396, with 20 banks selected as research samples. This research has a theoretical model and a vector error correction model was used to test the hypotheses. According to the t-statistic and the coefficient of estimation of the VECM model, it is determined that the effect of using the debt-asset management approach on the asset-liability correlation risk in a long-term equilibrium is decreasing.

Minimize supply chain costs as one of the essential issues in support activities such as financial planning systems, How to manage supply chain A set of ways to integrate Effective suppliers, manufacturers, warehouses and stores used to minimize total supply chain costs and meet customer service needs with a high level of service. In this study, the design of a robust cement supply chain dynamic network model was designed to reduce supply chain management costs after a crisis. Principal and efficient design of cement grid infrastructures, given the strong demand fluctuations at different times of the year, can significantly reduce financial costs on the one hand and reduce the potential for high-speed, high-cost corruption by correct prediction. Other leads. From the following tool The nose has been analyzed using artificial neural networks. The purpose of this study is to use artificial intelligence methodologies such as Grid Clustering, Subtractive Partitioning, FCM to explore fundamental and technical patterns and relationships in historical data. Used. To this end, a genetically-based inference fuzzy multilayer fuzzy neural network is introduced to prevent technical and economic unpredictability. The basic model of this paper presented by the researcher is a robust and multi-periodic planning for multi-product state under uncertainty.