FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT)
Year:2018 | Volume:9 | Issue:35 |Papers Count:17

Asset management is one of the important topics in financial subjects, which used to be noticed just by professions. In recent years gossips about bankruptcy of some financial institutions panicked investors and ruined the banking system credence. Bank operation will be bounded by macro-economic status and their operation would be monitored by central bank. There are several methods in order to survey banks functions and suggesting better asset management. One of the most important is Goal programming which is used for multi goal subjects. The hypothesis of this research is to investigate the possibility of financial optimization (asset-liability) of Bank Keshavarzi Iran, using Goal programming. In this study, using the views of bank experts, the goals of the performance of Bank Keshavarzi Iran are being determined. Research variables are items of financial statements that are optimized to achieve the best performance of the bank, and then the essential items of financial statements are compared with the items obtained by the model and the results are analyzed. The offer is made for the optimal performance of the bank.

Systematic risk is always one of the most important indicators that investors and financial analysts attach importance in their financial decision making. The purpose of this research is to provide a new model based on accounting variables for estimating the systematic risk index (b). The period of study is from 2006 to 2015. The statistical population of the research is the companies accepted in Tehran Stock Exchange. Using the Cochran formula, 174 companies are selected as the research sample. For this purpose, systematic risk beta is first calculated through ARFIMA-FIGARCH, and then, estimated models are compared using stepwise regression econometrics (forward selection) and artificial intelligence (through combination of genetic algorithms and flying birds algorithms in selecting effective factors and its modeling by combining and implementing an evolutionary dynamic data estimator algorithm on the above algorithms). In order to analyze the data, three software of Oxmetrics, Eviews, and MATLAB are used. The prediction accuracy of two models based on econometrics and artificial intelligence is evaluated by calculating the correlation coefficient between estimated betas and beta of ARFIMA-FIGARCH. The AI-based model with a correlation coefficient of 94 percent shows a higher predictive accuracy.

One of the main goals of governments in privatization implementation, along with goals such as increasing revenues and improving economic efficiency, is the development of stock markets. In this study, using the Bay and Peron technique, structural break is observed in the liquidity variable as one of the signs of the stock market depth. The model implies the existence of at least two effective and valid structural breaks and shows that in many related events, the implementation of privatization led to increased market liquidity as one of the principles of market development and led to a stock market has more depth. Also, using the MRS-GARCH method, it was determined that privatization on the emergence of regime change in the yield variables of stock market index has affected the duration of the third, fourth and fifth development plans in the form of dynamic models.

Different investors with different investment levels have a goal in common which is to reach a portfolio of assets which further to meeting the expected rate of return would have the least possible level of risk. In this study we aim to help an investment company to determine an optimized combination of assets containing the stocks of its subsidiary companies as well as other lower risk assets. One of the main challenges in investing in private companies’ stocks, is the lack of data related to their return and risk compared with public companies. In this paper we apply a simulation approach which is able to generate valid random numbers in data insufficiency condition to calculate the return and the risk of the private assets. Furthermore, defining the problem as a bi-objective optimization problem and regarding the fact that portfolio selection is an NP-Hard problem, we use a multi-objective covariance-based artificial bee colony algorithm to solve our problem. The results show that efficient portfolios are the ones have both high risk and low risk assets simultaneously.

The main goal of this research also explains the relationship between the types of HR risks with technological innovation in pharmaceutical. The statistical population of this study consists of two knowledgeable sections of the subject and managers of knowledge based pharmaceutical companies. The methodology used to examine the documents, interviews, and methodology for obtaining expert opinions and extraction of the native model in the knowledge-based companies studied were used. The results of this research indicate that among the most effective components of human resource risks, technological innovation has had the greatest impact on the risks of skills gaps and then financial risks, human resource operational risks, human capital risks and behavioral risks that were tested by tests Different stepwise regression and structural equation modeling with smartpls software were investigated and verified. Also, in the research phase, the greatest risk associated with skill skeletons, at the development stage, is the highest risk associated with human resource operational risks, and at the engineering stage, the greatest risk is related to behavioral risks. Knowledge companies that are in the engineering phase, in terms of the growth cycle of the company in the maturity stage, are likely to begin to grow at this stage of maturity in the face of future risks and reengineering of the system. For these companies, behavioral risks have shown their importance, paying attention to current values ​​and reviewing them, paying attention to current behavior among managers and employees, has shown great importance at this stage subsequently.

The purpose of the present research is to provide a multi-period portfolio optimization model in a fuzzy credibility environment, aimed for end-of-period wealth maximization and risk minimization. The investor’s risk was measured using the Average Value at Risk (AVaR) as a coherent risk measure.The model is designed in such a way that, in addition to considering transaction costs, the investor will have the opportunity to allocate part of his wealth to a risk-free asset. In designing the model, in addition to the cardinality constraints, constraints such as the minimum “proportion entropy” (as the portfolio of diversification degree) and the expected returns of the portfolio in each period are considered.The results of the model running by MOPSO algorithm indicated that the model objectives in the optimum portfolios were better suited than those when the model was run with random weights. The results also indicated that an increase in the portfolio diversification degree reduced the amount of the final wealth.

In this paper a stock trading system based on the combination of six technical indicators is designed. The indicators are combined using an artificial neural network and their parameters are optimized using convex combination-based optics-inspired optimization (COIO) algorithm. In the proposed model the technical indicators’ optimized parameters are obtained using both COIO and genetic algorithms with the aim of maximization of modified Sharpe ratio. The presented paper uses stock intra-day prices as input data and considers the transaction costs. The designed strategy is compared against several other approaches including: using the indicators’ default parameters, buy and hold strategy and optimization using genetic algorithm, for both daily and intra-day prices and due to a greater modified Sharpe ratio for the proposed model, its superiority is shown in all cases. Moreover, in a comparison based on end- of- period returns, it is shown that without considering the transaction costs the results of the intra-day data beats the results of the daily data while no superiority is observed when considering the transaction costs. So reducing the transaction costs is recommended to motivate traders to trade on an intra-day basis.

According to the findings of financial econometric researches, oil prices as one of the most important macroeconomic variables affect financial markets and economies of oil exporting countries. In this study, the price of OPEC oil basket has been used with daily frequency. The period under review is from August 3, 2013 to December 26, 2016. The course includes various developments such as unrest and war in the Middle East, a sharp and unexpected decline in oil prices for reasons such as a decline in demand, an agreement 27, and the agreement of OPEC members to reduce oil production in order to increase oil prices, is located.Initial studies indicate cluster fluctuations, ie, independent and uniform distribution characteristics and variance consistency. The Breusch Godfrey test confirms the effects of ARCH and GARCH. Also, a generalized test with the estimation of kernel density based on the Monte Carlo rule indicates Parson’s weight on the effects of ARCH in the variable.The results of the study of oil price fluctuations using the MS-GARCH model of single and multiple regimes indicate that the three regimes model is suitable for explaining the behavior of the variable in the reviewed period.

Herding behavior of investors is one of the main behavioral fears and important biases in financial markets that has attracted a lot of attention. The existence of herding in behavior is a term for explaining how individuals within a group can work together without the prior plan. This research identifies and measures this important bias and examines herding behavior in Tehran Stock Exchange. This study, using a Weighted Cross-Sectional Variance analysis method, which is a new method based on the theory of pricing arbitrage, has been used to identify herding behavior. This is a combination of models based on return dispersion and beta-based models. The statistical population included all companies accepted in the Tehran Stock Exchange during the period of 1389-1394. In this research, the Chang, Chang and Khorana return distribution model and also the CAPM-based cross-sectional variance model are estimated and compared with the original model, which results indicate the superiority of the original model compared to other models. Also, the results of the research indicate that herding behavior is poorly observed in all years, while during the research period of 1392, there are more strong herding points and therefore, more than other years, faced with herding behavior phenomenon.

One of the key issues for investors is the issue of creating an optimal stock portfolio. In the issue of choosing an portfolio, the decision maker faces different and sometimes conflicting goals such as rate of return, liquidity, dividend, and risk. In portfolio optimization, the main issue is the optimal choice of assets and securities that can be made with a certain amount of capital, but on the one hand, the uncertainties associated with each share, and, on the other hand, the multiplicity of the optimal portfolio selection model, on the complexity of the problem increases. In this paper, the portfolio optimization under uncertainty has been studied. A randomized approach to converting uncertainty into a state of definiteness and agreeing to plan for a single objective is used in combination. Information about 20 pharmaceutical companies from the Tehran Stock Exchange has been used and the validity of the model has been investigated. The results show that the stock portfolio offered has a high performance.

The purpose of this paper is to investigate the effect of value premium factor and implied equity duration factor on excess stock returns in Companies Listed in Tehran Stock Exchange. For testing the research hypotheses, the Fama- French Model (1993) is used. The evidence of previous research shows the relationship between the timing of firms' cash flows and value premium. Therefore, in this research, the effect of value premium factor on excess stock returns (similar to the Fama- French Model) are examined and then value premium factor replaced with implied equity duration. The statistical society is companies listed on Tehran Stock Exchange and the research sample includes 145 companies using the systematic elimination method, in the period of 2007-2016. The results indicate both value premium factor and implied equity duration factor have the significant and positive effect on the excess stock returns.

The main purpose of this study is to derive the price of American put option, when the interest rate follows a stochastic process. For this purpose, first underlying asset model is expanded to CIR stochastic interest rate model. Then, the problem of American option pricing under CIR stochastic interest rate model is formulated as a two-dimensional linear complementarity problem (LCP). We propose a two-cycle component wise splitting method for solving this two-dimensional LCP. In this method, the two-dimensional LCP, obtained for the valuation of an American option, is decomposed into six one-dimensional LCPs in several fractional time steps, and then each LCP is solved numerically in two steps. So that in the first step, the tridiagonal systems of equations are solved, and then in the second step (update step), the obtained values of option prices are modified and updated according to the conditions of the American option pricing problem. Finally, the numerical results obtained by splitting introduced method are compared with the Monte Carlo simulation results.

With considering the importance of the information along with the stocks bulks order of companies are presented in the Tehran Stock Exchange, We analyze the stock price effects of bulk order announcement published publicly in Tehran Stock Exchange over the period years1394–1396. We use an event study model to show the significant impact of bulk order announcement published publicly on the share prices changes then we observe that cumulative returns being very high around the announcement published day for Tehran Stock Exchange companies. Bulk order announcement has significant positive cumulative abnormal returns, indicating that Bulk order on average increases firm value. Next, we regress cumulative average abnormal returns of different Event windows on dummy variables to show that positive cumulative abnormal returns created under the effect of the bulk order. Finally, we conclude that bulk order announcement information leakage before event day on average cause positive abnormal returns for insider.

Stock market activists are the acquiring and using methods to predict future stock prices, increasing their capital gains. Therefore, it seems necessary that appropriate, correct, and scientific principles are used to determine the future price of the stock of investor stock options. stock price prediction is an important part of investment, and in most cases it is the field of research for researchers, because it ultimately leads to the choice of appropriate investment. Different methods have now been developed to achieve this goal. Have been introduced that are often statistical methods and artificial intelligence. In this research, using a randomized approach approach that is among artificial intelligence classification methods, along with technical indicators that include: power index Relative Price, Stochastic, Equilibrium Balance, Williams R%, Daily Returns, and Mac.d Series Markets, are looking for stock price trends. This model is compared with logistic regression method and completely randomized method (dice throw). The results of the research on daily data of Tehran Stock Exchange Index from 1393 to 1395 indicate that the accuracy of the proposed method in estimating market trend is 64%, which is more than two methods of logistic regression and completely randomized method of accuracy Has a higher rate.

Today in the financial markets there are different factors that can help investors to allocate their assets satisfactorily. The main concern for asset allocation is that if any return and volatility from a stock market spillovers into, return and volatility of another market. Spillover expresses shock transfer to other markets or countries regardless of the basic links exist between them. This paper investigates the existence of spillover effect in Tehran Stock Exchange. Specifically, we study the return and volatility spillover effects between 6 indices from August 2011 to March 2016 and Dynamic conditional Correlation model (DCC) has been employed in our study. Base on the results of this research, we reveal that the return and volatility of selected industries are impacting on each other. Some results suggest that Pharma - Industry has the highest impact and Oil, /coke and nuclear fuel Industry has the lowest impact on other selected industries.

Remittances are payments made by immigrants who work in other countries and send money to their relatives and friends in the country of origin; which they may transfer through formal or informal channels. In order to investigate the effect of these funds on the development of the financial sector in Iran, the present study uses the data from 1980 to 2015, to find the answer of this question. In this regard, after defining the theoretical framework, the criteria for measuring the financial development index are introduced, which includes 3 criteria for the allocation of credits to the private sector to GDP, the ratio of bank credits to GDP, and cash to be paid. Then, after specifying the model, the effect of the remittances considered on each of these three criteria separately, by using the ARDL econometric method. Results show that there is a negative but small effect on financial development in short-run. But on the other side, there is a positive and significant effect on the financial development in long-run in Iran.

Understanding and the investigating the behavior of stock prices, has always been one of the major topics of interest to the investors and finance scholars. In recent years, various models for prediction using neural network and hybrid models have been proposed which have a better performance than the traditional models. Here a hybrid model of neural network and wavelet transform is proposed in which genetic algorithm has been used to improve the performance of wavelet transform in optimizing the wavelet function. Daily stock exchange rates of TSE from April 21, 2012 to April 19, 2017 are used to develop a prediction model. The results show that it is possible to find a wavelet basis, which will be appropriate to the intrinsic characteristics of time series for prediction and the prediction error in this model is reduced comparing to the neural network and hybrid neural network and wavelet models.