FINANCIAL ENGINEERING AND SECURITIES MANAGEMENT (PORTFOLIO MANAGEMENT)
Year:2018 | Volume:9 | Issue:34 |Papers Count:19

The main purpose of this research is to create tools for financing the monetary system. Due to credit card instructions issuance by the Money and Credit Council, credit cards are expected to play significant role in enhancing demand in the future of the economy. The design of credit card asset-backed securities through the sale and transfer of accounts receivable on credit cards, results in the release of banking resources, liquidity management in the productive part of the economy, lowering the amount of bank claims and improving the capital adequacy ratio of the bank which will help banks to be able to increase loan. The operational model of the issuance credit card asset-backed securities in the form of a structured questionnaire under the aspects of laws and regulations for the issuance of the securities, the impact of the issuance of securities on the financial structures, the consequences of the issuance of securities on the risks associated with credit card issuer and its operations, as well as how to discover the rates and prices of the securities asked by experts. The results are based on statistical tests of the model being appropriate.

In this paper, mathematical applications in balance-sheet management were examined, based on mathematical word problems, to provide asset-liability management reporting in a developmental bank. After reviewing the importance of balance-sheet management in banks, introducing development bank, the literature ant theoretical basis about mathematics word problems and applications of mathematical modelling in assets and liabilities management, two case of word problems (arithmetic and algebraic) were examined in balance-sheet management in which mathematical representing and modelling were used to solve problems, respectively. Then the results were interpreted in financial field. The results revealed mathematical representing and modelling helps to interpret balance-sheet information for managing assets and liabilities in a developmental bank. Regarding the results, balance-sheet management can be observed from the perspective of mathematical word problems. Therefore, it can result a more accurate interpretation of balance-sheet data in banks, based on accurate expressing mathematics and analysing problems in the real world. This sheds light on the necessity of giving attention to mathematical applications in balance-sheet management. Finally, it is recommended that in future research, conceptual understanding mathematics examine in solving word problems as balance-sheet management among users in such a way that understanding those concepts led to an increase in applying mathematics in finance affairs and consequently, accurate analysis and evaluation of financial data.

In this paper, we first present the pricing equation for American and European type of standard and power exchange options. Then we tested 501 price of gold and dollar from first day of Persian month Farvardin of year 1391 to the first day of Persian month Tir of year 1394, by using time series and ARCH, GARCH, MAR-ARCH, ARMA-GARCH, GJR-GARCH models in order to determines power of assets exchange to calculate value of power exchange option dollar on base of gold in near future.Finally, by comparing the mean square error of observation and conditional variance by considering AIC and BIC measures, we choose an appropriate model of above models. Also Christoffersen and Kupiec, as an appropriate model tests are used for forecasting and analysis the behavior of gold and dollar power option pricing.

We applied Random Matrix Theory making a portfolio enables to beat the market. On the basis of previous findings, the largest eigenvalue represents the influence of the entire market that is common to all stocks. We analyzed cross-correlation between returns of different stock market indices (S & p500, DJ USA, DAX Germany, FTSE100 England, HSI Hong Kong for efficient markets and TSE Iran, SSE180 China and MXX Mexico for emerging markets) for 730 trading days from May 2012 to October 2014 by using Random Matrix Theory (RMT). Looking at the largest eigenvalue and components (stocks) of the largest eigenvector (demonstrating market mode or trend) and calculating share of every stock in market trend, we could categorize stocks in terms of their impact on the trend in 3 groups: high, middle and low or no impact on market trend. Then we created 3 portfolio in this respect for Tehran stock market. The results shows the portfolio consisting of high impact stocks can beat the market return.

Stock return forecasting is one of the most important question for investing in Stock markets. Because of the effects of policy, economic, etc., we need moderns and intelligent models to forecast the returns.The main idea in this research is classifying the stocks into high and low return groups, for this purpose support vector machine (SVM) was used. To elect the best variables for models we used sequential feature selection and in order to evaluate the accuracy of SVM we do the same forecasting with diagonal quadratic discriminant analysis (DQDA). By using paired t-test, we conclude that models have no significant difference.Equal weighted portfolios were created for each models with and without feature selection also, we used posterior probability to weight the portfolio of DQDA with feature selection. The returns were calculated for each portfolio during the years 1388-1391. The simulating results are satisfying and all portfolios’ returns are better than market portfolio.

In this research, we study the effects of overconfidence of managers (one of behavioral bias) and its impact on companies’ risk policy from operational, financial and market perspectives. Due to weakness of existing modeks in describing psychological variables influencing overconfidence, first a justified model of overconfidence has been presented. To estimate model's parameters, random effects statistical model has been used. In this regard, a total of 98 companies listed on the Tehran Stock Exchange have been studied. Years 1389 to 1394 (Hijri calendar) is the period of investigation.Since the t-test value of financial and business risk is greater than 1.965 and its significance level is also less than 0.5, linear correlation between the business and financial risk and overconfidence is approved. Also t-test value of market risk is lower than 1.965 and its significance level is higher than 0.5. So linear and significant relation between the market risk and manager overconfidence is rejected. As the final conclusion, results confirm significant relationships between overconfidence and financial risk management and also business risk management. In the meantime, a significant relationship between overconfidence and market risk has not been observed.

In this paper we explored the relevance of asymmetry and long memory in modeling and forecasting the conditional volatility and market risk of equity market in Iran capital Market (Tehran Stock exchange (TSE) and Iran Fara Bourse (IFB)). A broad set of the most popular linear and nonlinear GARCH (generalized autoregressive conditional Heteroskedasticity) -type models is used to investigate this relevancy of asymmetry and long memory. Our in sample and out-of-sample results displayed that volatility of commodity returns can be better described by nonlinear volatility models accommodating the long memory and asymmetry features. In particular, the FIAPARCH (Fractionally Integrated Asymmetric Power ARCH) model is found to be the best suited for estimating the VaR forecasts for both short and long trading positions. This model given a risk exposure at the 99% confidence interval level have Several implications for equity market risks, policy regulations and hedging strategies can be drawn from the obtained results of this paper.

In the present study the effect of the quality of corporate earnings announcements reaction ratio was investigated. Features strategies may lower the marginal cost sales with higher sales volumes, economies of scale and a major investment in facilities and physical assets named. Meanwhile, the differentiation strategy combined with high sales margins and the quality of the product and branding realized through investment in intangible assets such as investment in research and development and advertising is obtained. Characteristics of the above-mentioned double impact on investor reaction to the information published on the company's profits. The response rate in this study, a comparative study of companies with business strategies of cost leadership and firms with different business strategies to the announcement, has been using the equations of structural failure. In this regard, the 147 companies listed companies of Tehran Stock Exchange during the period 2009 to 2015 were studied and estimated using econometric software testing hypotheses to be explored. Using the equations necessary structural failure was that if during the review announcement, In each sample is decreasing, breakpoints and found it to be rectified. The system indicates the sustainability Ascending announcement, the company adopted a business strategy that is cost leadership. The other results of the present study is that companies that strategy cost leadership to follow, announcement, they are generally only to companies that method of differentiation to adopt the changes better earnings your users of financial information Company to showcase. As a result, companies that adopt distinct strategies with diverse interpretation and fewer changes in investors' beliefs are associated. This cross-sectional study to improve the level of knowledge about the market reaction to the announcement, the changes will help. In addition, in this study the convergence of market reaction to the announcement, through changes in business strategies in a predictable situation is shown.

Nowadays, due to the rise of turnover and pace of trading in financial markets, accelerating of analysis and making decision is unavoidable. Humans are unable to analyze big data quickly without behavioral biases. Hence, financial markets tend to apply algorithmic trading in which some techniques like data mining and machine learning are notable. Online Portfolio Selection (OLPS) is one of the most modern techniques in algorithmic trading. OLPS allocates capital to a number of stocks and updates portfolio at the beginning of each period by some techniques. Actually, individual has no role in portfolio selection and the algorithm determines the way of investing in each period. In this article, an algorithm which follows pattern matching principle has been introduced. In pattern matching principle, the portfolio is selected based on identical historical patterns and in this article these patterns are found by spectral clustering in data mining. At the end of article, there is a numerical example which uses the most 20 active stocks in New York Stock Exchange (NYSE) data and its results has been compared with other algorithms in this topic.

The purpose of this study is to investigate the clustering of fluctuations in financial markets, including the stock market, with the underlying model of simulation. Time series of financial asset returns show the clustering of volatility, which shows that large changes in prices tend to form clusters together And these clusters will last for a long time. Time series of financial asset returns often exhibit the volatility clustering property: large changes in prices tend to cluster together, resulting in persistence of the amplitudes of price changes. After recalling various methods for quantifying and modeling this phenomenon, we discuss several economic mechanisms which have been proposed to explain the origin of this volatility clustering in terms of behavior of market participants and the news arrival process. A common feature of these models seems to be a switching between low and high activity regimes with heavy-tailed durations of regimes. Finally, we discuss a simple agent-based model which links such variations in market activity to threshold behavior of market participants and suggests a link between volatility clustering and investor inertia.

Today the importance of evaluating performance and factors affecting corporate performance is one of the most important areas of the financial Economics So that research has been done on this. In this regard, the main purpose of this research is to examine the factors affecting the performance of Bourse companies with an emphasis on monetary policy. Contrary to previous research that for corporate performance has been captured only an indicator such as profit or income In this research, FPT Comprehensive Index has been used to measure the performance of companies. The data used in this research is annual data for the period from 1385 (2006) to 1392 (2013) and for 94 companies admitted to the stock exchange. And monetary policy instruments include the growth rate of banks' loan facilities, liquidity growth rates, and legal deposit rates. The results of model estimation using GMM dynamic data panel show that there is a Significance relationship between monetary variables and firm performance. With the increase in bank facilities and the increase in liquidity, the Piotroski F-Score index is also increasing which reflects an increase and improvement in the company's performance. Therefore, given the direct impact of monetary policies on corporate performance indicators, policymakers should be more careful about acting any other policy.

In many of the world’s stock exchanges, legislators specify price limits in order to reduce market volatility and reduce investors’ loss as a result of emotional decisions. One of the disadvantages of such an action is magnet effect of price limits, leading to a behavioral bias. In this study, the effects of information asymmetry on the presence of magnet effect of price limits were examined with regard to daily data obtained from 25 companies listed on the Tehran Stock Exchange during 2013-2015. First, the presence or absence of magnet effects in stock prices of these companies was examined using conditional volatility models. Then, the effect of information asymmetry on the presence of magnet effect was studied using the logit regression model. The study results revealed that the magnet effect of price limits exists in the studied companies and there was a significant relationship between information asymmetry and magnet effect.

Predictability in financial markets is very complex, and the reasons for this complexity can be summarized as non-standard data, nonlinear data flow, and large variations in data. Determining the proper pattern for forecasting volatility can play a significant role in decision making. In the old econometric models it is assumed that the component of error constant during the sample period. But in many financial time series it is observed that during periods of volatility is very sever. Under these conditions, the assumption of the exictence of the equivalence of variance is no longer reasonable. In the present paper, the GARCH, IGARCH, EGARCH, GJR-GARCH, FIEGARCH, HYGARCH, and MRS-GARCH two-regime models were evaluated in prediction of OPEC crude oil price volatility during 2010-2016 based on their RMSE error criterion. The results of this evaluation show the superiority of the Markov Switching GARCH Model on the 5 and 22-day horizons. Also, the long-term FIEGARCH memory model in predicting horizons of 1 and 10 days has better performance in predicting oil price volatilities than other competing models.

Government intervention and existence of politicians on boards may affect companies’ decision making and business trajectory. A stock market should display informational efficiency and, therefore, should appropriately reflect the value of political connections, if any value exists. This study is aimed to investigate of the relationship between political connections and the cost of equity capital in listed firms on Tehran Stock Exchange. This research in terms of method is descriptive - correlation. The sample consists of 114 companies listed on Tehran Stock Exchange for the period 2009 to 2015. Multiple linear regression analysis for hypothesis test is used.The findings show a significant negative association between political connections and the cost of equity capital. In other words, by increasing political connections on firms, cost of equity capital is reduced. These results suggest that political connectedness could represent an important determinant of the cost of equity capital.

Time pricing is a tool for traffic management. Traffic management looks after increasing trip quality by reducing direct and indirect cost (wasting time, air pollution, noise pollution, accident cost and…). So, in this paper, at first, it is implemented financial derivatives for defining travel time derivatives. At the second stage, it is used Monte Carlo method for pricing traffic congestion.Since, derivative pricing fluctuated is an index for predicting deriver behavior in that time or at least their expectation about future traffic volume: low (high) price is likely to be light (heavy) congestion at that time and in the specific route. So, it is an intelligent method that indirectly dependent to drivers’ decision. In addition, in the usual method, the price is fixed, however, in the new method it is used market mechanism, so it is changed. Furthermore, in travel time derivative, two factors, traffic volume and diversion is implied in this forecasting. At the end, the introduced method is run for Karaj-Chalos route at 10 first of 31 July 2017. The derivative determined that the travel time will be 72 minutes. The result shows that, buying opportunity price is more than celling, so the travel time will be less than threshold time 72 minutes.

While available models to measure the risk don’t consider the positive side of stock return probability distribution, this research tries to optimize the portfolio based on adjusted lower partial momentum (ALPM) with upside potential and behavioral variables to compare the result with modern portfolio theory model which is one of the basic models in this area.This research studies 144 monthly portfolios of industry indices in Tehran Stock Exchange within 12 years and compute realized rate of return for those portfolios in next month. In the next stage the research make use of variance analysis between realized rates of return for portfolios made by two models.The present research determined that realized rate of return for portfolios made by ALPM are higher than modern portfolio theory model when investors are downside risk averse and upside potential lover. However in condition that investors are downside risk averse and upside potential averse there is not any difference between two model as well as when investors are downside risk averse and upside potential neutral.

Price forecasting is one of the most challenging issues that the speculators, traders and brokers are faced with. On the other hand in interval analysis it is supposed that observations and estimations in the real world are not complete and reliable so to increase the accuracy we should describe the data as the intervals that includes real quantities. Various methods are used in order to model the time series such as price. Autoregressive integration moving average (ARIMA), which is known as box-Jenkins method is one of the most commonly used models in forecasting of time series during the past three decades. But the main assumption is that there is a linear relationship between the values of the series therefore nonlinear relationships cannot be explained completely by using autoregressive integration moving average (ARIMA). Another method in time series forecasting is neural network which can estimate the various nonlinear relationship (called neural network universal estimating) but according to the literature, using network will have complicated results. Since it is difficult to understand the linear and nonlinear data pattern in reality, this idea will come to mind that the combination of linear and nonlinear models could increase the accuracy of forecasting. So in this research the linear part will be estimated by ARIMA and then the non-linear residuals will be modeled by neural network and finally the predicted result will be added to ARIMA in order to forecast the low, high and close price of gold .comparing the accuracy of the hybrid model to ARIMA and neural network by pair compared, Diebold-Mariano and Harvey-Newbold –Leybourn test and two criteria (MSE and MAE) showed that the hybrid model presented better performance.

By development of economic enterprises and the expansion of trade, risk management has been particularly important in financial studies. The nature of risk management requires that these studies need to be multifaceted; it means that, in addition to recognizing the enterprise’s economic and commercial functions, risk managers need to be familiar with statistical and mathematical issues and risk control methods and models. Its output is the same rewards for accepting risk in an economic and commercial activity, so in managing economic enterprises, instead of eliminating risk, it is necessary to create the right environment for optimal risk management.The present study seeks to study the pattern of different stock option exchanges profitability and determine the appropriate solution to reduce the risk of price manipulation when the transaction is negotiated. For this purpose, the price of European, American and Asian transaction option shares of 48 companies after the estimation of volatility has been calculated and analyzed by Deriva Gem software. Since the closing date price in applying Asian contracts is not limited to the end-of-day closing date and it is an average of final prices during the lifetime of transaction option contracts, so the results of research after studying the various patterns of profit show that closing date price of Asian transaction option is less manipulated, and the holder enduring lower risk. The values ​​of risk sensitivity parameters calculated for each transaction option also prove this issue.

In financial studies, portfolio can be defined as a set of investments that are selected and accepted by an individual or institution. Portfolio selection is one of the main concerns of investors in financial markets. The average-variance model with bound restrictions is considered as one of the main models in solving the portfolio optimization problem. In terms of complexity, this model is a polynomials NP-hard non-linear problem that cannot be accurately solved. In this study, an Antlion optimizer- Genetic algorithm (ALOGA) and a population based incremental learning and differential evolution algorithm (PBILDE), which are modern meta-heuristic models for solving optimization problem, are used to optimize the investment portfolio through increase the return and reduce the risk. Among 591 companies listed on Tehran stock exchange from April 2012 through March 2015, 150 companies were selected as the final sample using screening method. The data of these companies were analyzed using the applied algorithms in this research and their efficiency was compared together. The results indicate that ALOGA and PBILDE algorithms both are suitable for solving the portfolio optimization problem. In addition, using the ALOGA algorithm, it is possible to create an optimal portfolio with high accuracy and efficiency.