FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT)
Year:2019 | Volume:9 | Issue:37 |Papers Count:18

risk & return are two main factors that affect financial decisions. The trade off between risk & return create different investment strategies. In other words investment decisions are all based on risk & return. In this research we used multiperiod selection method in order to maximize investors utility. In this model we used not only variance but also higher order moment – skewness-for optimization. For emprical test of the model we used return of first 50 companies stored by market capitalization in tehran stock exchange during 1386-1395. We used skewness & transaction cost to introduce a moltipriod model in asset allocation to minimize variance of investors utility. Comparing the result of this model with markowitz model & simpel model considering investor preferences shows that based on performance evaluation criteria, the suggested model perform much better than the two other.

In today's world, capital markets have increased the possibility to generate profits through high-frequency transactions due to the advancement of computer technology and the use of IT infrastructure. The main objective of this research is to examine and provide a model of paired trading algorithms that can generate higher returns than coherent algorithms. In the background study, one of the limitations of the paired trading algorithm is the use of constant limits and rules that cannot model the dynamics of the system. This research based on the purpose is applied research and in terms of collecting data in the form of descriptive research with the approach of presenting a model. In order to test the model, using the data of 1395, intraday stock prices was used. The statistical population of this study was the top 50 Tehran Stock Exchange companies, using filtering the stocks to create a pair of shares decreased to 33, these shares was selected as sample. The results of the study were presented by implementing two algorithms of paired trading algorithms and paired trading algorithms statistical process fuzzy control. Finally, the results showed that the modified algorithm during the similar period of the investment could produce 57. 95% of the return, while the base model had 46. 17% returns on investment.

The main objective of this study is to design corporate financial distress prediction models for the following three industries basic metals, non-metallic minerals and machinery and equipment, using the bagging model. Moreover, the prediction accuracies of the designed models are compared to the bayes and decision tree models. Aimed Statistical population of this research includes all the corporations of each of the industries. The financial distress criterion employed in this research is the criteria of article 141 in commercial code and the timeline of the research is from 2001 to 2016. The results shows that, comparing to the base models (i. e. decision tree and bayes), the bagging model has a better prediction accuracy average. Moreover, based on the obtained results, it can be concluded that the bagging, decision tree and bayes models are qualified models for the corporate bankruptcy prediction

Ensuring return of shares is one of the most important discussions on financial markets. In this research, the performance of the five factors Fama and Ferench model and the CANSLIM model in the pearl market selection in Tehran Exchange Market is analyzed. The necessary data are given by 105 companies active on the stock market during 2009-2015. To this end, in first step all variables such as premium market risk, profitability risk, value, profit and investment are calculated and in the second step according to the above variables, companies divided in two categories, and thirdly we made a 2*2*2*2 portfolio conform monthly returns of each factor of size, market, value, profitability and investment. Also in CANSLIM model after calculating the seven factors, all data’ s in Excel software were classified as zero and one. finally, for data analysis e-views and stata software were used. The result shows that there are significant differences between the two models in the stocks with higher returns, and the five-factor Fama-Ferench model has more power in explaining the stocks return than the CANSLIM model.

The agricultural industry, which is referred to as a mothers industry, faces many risks, which has reduced investors' willingness to invest in this industry. Some of these risks, including disaster risk, are considered to be the specific risks of this industry. Developed countries use a variety of instruments, such as weather derivatives, to cover these risks. Weather derivatives, like other derivatives used in financial markets, are based on their base assets, with the difference that the base asset in weather derivatives is the temperature index, rainfall, snowfall, and etc. This research, while reviewing the nature of the weather derivatives, has provided a juridical feasibility study for the implementation of this financial instrument. The research method used in this research is a multi-stage Ijtihadi model. The results of this research indicate that the lack of legal permission to use this instrument is due to the conflict with the principle of the prohibition of fake money and the existence of a dilemma of the underlying asset ineligibility. However fixing the drawbacks and develop Islamic financial instruments in accordance with the Imam's jurisprudence is not far from mind.

Portfolio Selection is one of the most important decisions that institutional investors have to face. Markowitz was the first to introduce risk into the portfolio selection decision by introducing the Mean-Variance Model. This created one of the most important fields in finance, that is Portfolio Optimization and finding the efficient frontier. In the next researches, adding real world constraints to the model broadened this field. With increasing the number of assets or the constraints, Portfolio Optimization becomes an NP-hard problem which is impossible to solve with derivative-based methods, therefore, numerical and metaheuristic methods should be used for solving it. The aim of this research is optimizing portfolio using Whale optimization algorithm. This metaheuristic algorithm is inspired by the behavior of Whales and was introduced in 2016. This research implements the algorithm in the top 50 index in Tehran Stock Exchange and tries to find the efficient portfolio in this index. We also compare the performance of this method to two other metaheuristic algorithms and explain the advantages of the proposed method in portfolio optimization.

In this study, an Adaptive Neuro Fuzzy Inference System (ANFIS) based on Principal Component Analysis (PCA) is proposed for predicting the financial distress of companies. This system not only has the ability to adapt and learn, but also reduces the error, because it avoids additional parameters when input variables are too high. In order to confirm the effectiveness of this model, 181 listed companies in the Tehran Stock Exchange (905 companies-years) were selected by using systematic samples from 2011 to 2015, which 58 of those were distressed and 847 companies-years were healthy. These companies were randomly divided into two sets: a training set for designing model and a check set for validating the model. The results of the research show that the Adaptive Neuro Fuzzy Inference System based on Principal Component Analysis is capable for predicting the financial distress of companies accepted in Tehran Stock Exchange and when the proposed model is combined with Dove Swarm Optimization metaheuristic algorithm, Reducing the error value increases the accuracy of the model. Therefore, it can be seen that the use of a complementary algorithm can increase the predictability of the PCA-ANFIS model.

Asymmetric information can have a huge impact on financial markets. One of the important effects of asymmetric information in the market is the Inclination of market performance towards disruptions and inefficiencies, as the way information is input in the fluctuation of market prices and determining the final price and asymmetric information can reduce efficiency. In this regard, the main objective of this research is to measure the risk of information asymmetry using theProbability of Informed Trading (PIN) model in Tehran Stock Exchange companies. The results show that the information asymmetry risk of Tehran Stock Exchange companies is not the same and there is a significant difference between the information risk asymmetry indexes.

The most common criterion used to measure market risk is the value at risk method. The value at risk is the maximum loss which may occur over a specified time period and considering a specified degree of confidence in a portfolio of assets. In the current research, data related to coin future market price indicator have been considered. The daily data used were gathered over a time period from 2008 to 2017. According to the results obtained from this study, coin future price data did not have a normal distribution; accordingly, the value at risk in this market was estimated using TGARCH method. Time series was analyzed using wavelet analysis over 7 time periods of 2-128 days. In short time periods the normal distribution outperformed other distributions, but in longer time periods, the skewed-t distribution outperformed other distributions. In the sales situation, this threshold behavior is observed over a longer time period. The hope of increased future price by investors can somewhat justify these behavior changes over time.

The purpose of this study is to investigate the relationship between free float stock and return, liquidity and the value of listed companies in Tehran stock exchange. For analyses data collected from 134 listed companies over seven years of 2010 to 2016 has been used in the study. To analyze the data and testing the hypotheses, the study has employed multiple regression models with several variables using panel data structure. The results show that based on testing the first hypothesis, the relationship between free float and stock return is significantly positive. Moreover, based on the second hypothesis, whereas the effect of current ratio is insignificant but the significance of the total model is accepted and the relationship between free float and stock liquidity is significantly positive. In the third hypothesis the total regression model is also significant, so there is a positive and significant relationship between free float with companies value.

Investor, stockholders, managers, beneficiaries, with broke of company, would lost their assets. Thus, existence of mechanism which could be evaluating and expecting of financial crisis of companies, is essential. A lot of research, implemented about expecting of bankrupt, which using of smart intelligence algorithms and ultra-discovery, were of the models of recent decay. In this study, with using of information of stock exchange center of TEHRAN (1390-1395), 45 successful firms and 25 bankrupt firms, have been researched. Financial ratios were of the variables of this study, which these variables with using of ultra-discovery algorithm of glowworm, identified as one of the models of smart intelligence and effective ratios in bankrupting. It includes of 9 financial ratios and in long with this process, valid and broke firms have been ranked. Two hypothesis have been codified for this study, which the result of them, in order to justified of these hypothesis, indicating of 95/12 correct expecting of first year, 5/36 for second year, and 80/48 for third year.

The ability to choose the most optimal change in the composition of the portfolio of assets, brings the investor to the highest level of investment in terms of efficiency and effectiveness in the dynamic and changing market. Rebalancing the portfolio occur through a change in the composition of assets weights, remove the assets, bought and sold assets and etc. Therefore, in this study solving the multiobjective portfolio rebalancing model with fuzzy parameters. The return, risk, liquidity and uncertainty as a key financial criteria are considered. Due to its importance as well as transaction costs, the net return of the portfolio are adjusted. the multiobjective portfolio rebalancing model with fuzzy parameters is solved by fuzzy goal programming and a hybrid intelligent algorithm that combines fuzzy simulation with a genetic algorithm. The results demonstrated the effectiveness of the solution approach and effciency of the model in practical applications of rebalancing an existing portfolio.

The purpose of this study was to investigate the relationship between different levels of management ownership (high, medium and low), free floatation, volatility of stock returns and age of the company with the synchronization of stock prices in companies admitted to the Tehran Stock Exchange. Also, variables of size and financial leverage are also considered as the control variable. This research is applied in terms of its purpose and its period is from 2012 to 2016, and the sample is composed of 89 companies. To test the hypothesis of the research, multivariate regression was used in Eviews software. The results of the research hypothesis test show that the relationship between managerial ownership and price synchronization as high management (negative and significant), middle management ownership (negative and significant), free-floating shares (negative and significant), age (Positive and significant), volatility of stock returns (positive and significant) and financial leverage (positive and significant). As a result, it is necessary to pay more attention to this issue.

The stock exchange is one of the financial instruments of countries around the world. The recession in this market can have important effects, for example reducing liquidity, reducing the profitability of companies admitted to the stock exchange, and reducing economic growth. In this paper, we are looking for extraction and prediction of time cycles in the stock market. Initially, using the total stock index and the MSI (3) AR (2) model, three cycles of recession, medium prosperity and high prosperity are extracted in the stock market. Then the most important predictor variables are determined by using the integration of the NSGA (II) algorithm and the three types of neural network models and predicted the market situation for the next three months. Finally, the performance of three types of multilayer perceptron neural network, radial basis and probable network were compared in terms of feature selection and prediction of future market situation. The results indicate that all three models have acceptable error rates, accuracy, and Kappa coefficients, and the probable network model has lower error rate, more accuracy and kappa coefficient than other models.

The aim of this study is to present a mathematical model according to mathematical programming models also to weave the agriculture bank and in the year of 2016 which is the best year of combination of loans and deposits that while customer satisfaction, the most profitable for banks are to be followed. In this research, the sample size is equal to the statistical population and including all branches of Agricultural Bank of Kerman province. Also the model is estimated by using of 3 programming methods: linear, goal and fuzzy. Some soft wares like Excel and Win QSB were used for analyzing this data. Due to the results, linear and goal programming are more allocation and the proceeds of the grant increased by using linear and goal programming than fuzzy programming and the bank current allocation. The results suggest that using linear and goal programming can help the managers for allocating resources so that higher yields will be achieved.

Finding the most optimize way to make a portfolio “ feasible “ has been, and always will be a challenge and concern for those active in investment management industry. For several decades, Markowitz's (1952) Mean Variance Theory (MVT) has been considered as the cornerstone of modern portfolio theory. The Behavioral Portfolio Theory (BPT) developed by Shefrin and Statman (2000) is often set against Markowitz's (1952) Mean Variance Theory (MVT). In this paper, we compare the asset allocations generated by BPT and MVT without restrictions. Using Tehran Securities Exchange stock prices from the TSE database for the 2012– 2017 period, A sample of 247 companies are listed on the Tehran Stock Exchange data for a period of 5 years was used for statistical analysis. stock prices contained in the TSE database to generate a possible asset allocations via bootstrap simulation. this paper is the study that empirically determines the BPT optimal portfolio. We show that Shefrin and Statman's (2000) optimal portfolio is Mean Variance (MV) efficient inmore than 70% of cases.

Exchange rate prediction is an important economic variable of interest to the economic actors. Technical approach is one of the commonly used approaches to forecasting, which uses the past behavior of the exchange rate for prediction. However, given the chaotic and non-linear structure of financial markets, the market forecasting cannot be done using a certain and simple method obtained by combining different technical tools and more sophisticated methods are required. In recent decades, neural networks have been employed as one of the most widely used methods in classification, pattern recognition and prediction of complex time series. In this research, a multilevel neural network model was provided to predict the euro-dollar exchange rate, which predicts the price on the next day with an appropriate accuracy by utilizing the data and variables derived from the technical analysis. The results demonstrated the proper function of this method versus other conventional methods of technical analysis and neural network.

Construction projects in Guilan Water and sewerage has high levels of risk due to their complex and dynamic environment. There are no suitable risk allocation models for Guilan construction industry based on article 23 processing budget and planning law 1352 clause about (EPC). There is the cost of project and plan for design risk allocation model. This work endeavors to propose and apply a Risk Allocation Model (RAM), based on a simple mechanism for allocating critical risks to the responsible party in the project. In addition, the RAM aims to compare among projects, which is more risky. The construction of RAM is based on Delphi method for 41 risk factors, over 10 groups, are identified and used in the model development. All factors are analyzed and weighted by deploying Weighted Risk Factor (WRF) which combines the effect of a risk factor probability and its effect on time and cost. The model results identified the most important risk factors to be allocated to owner, contractor or shared between them, as well as the suitable risk action for each factor. The model is applied on a real case study through two construction projects in Guilan to test the validation. A complete comparison between the two projects is presented and a decision is introduced for contractor. The results emphasized that the model is easy to understand and use by the parties involved in construction projects. Further, it is flexibility in the event of variables.