جستجوی مقالات مرتبط با کلیدواژه « multi » در نشریات گروه « مالی »

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.

Given the importance of ranking of brokerage firms for customers, this paper analyzes the impact of relationship marketing factors on the ranking of brokerage firms and compares the obtained results with the results of the ranking conducted by the Securities and Exchange Organization (SEO). For this purpose, in the first stage, seven indicators of relationship marketing indicators which have the greatest impact on the ranking and performance evaluation of brokerage firms from the viewpoints of the experts, from among the indices derived from the theories and models of relationship marketing including customer confidence in the organization, customer commitment, empathy with the customers, financial interests, competence of the organization, conflict management, communication and financial interests was determined by questionnaire. The study population will consist of two parts: Active Brokerage firms in Stock Exchange and the customers of these brokerage firms in the cities of Tehran and Isfahan who have used the services offered by the brokerage firms at least twice. The data were collected from a sample of 873 customers of brokerages and 2 experts of capital market. To analyze the research data, the main influencing criteria on relationship marketing were evaluated in the form of questionnaire. Finally, the ranking algorithm was designed using Fuzzy AHP and Fuzzy TOPSIS and ranking was conducted. Correlation test between the ratings indicate that there is a direct correlation and integration between both tools.

Abstract: With regard to the Basel Committee’s emphasis on the necessity of using 10-day Value-at-Risk (VaR) internal models in order to determine minimum market risk capital requirements, and downsides of the square-root-of-time rule, our purpose is to produce more accurate forecasts of the multi-period VaR using sixteen models for three stock indices, the TEPIX, NASDAQ, and FTSE. The results, based on the sum of the loss function and efficiency criteria indicate that the bootstrapped historical simulation (BHS) model performs the best for the TEPIX. Also, at the 95% confidence level the parametric EGARCH model with the Student’s t innovation and at the 99% and 99.5% confidence levels the EGARCH model with the normal innovation clearly outperform other models in estimating the 5-day VaR for both the NASDAQ and FTSE indices. In addition, our findings indicate that the best model based on the conditional coverage test is not necessarily the most economical model in estimating the 5-day VaR.

In this paper, we propose automatic stock trading system which combines technical analysis and adaptive neural fuzzy inference system to predict the stock price trend to increase return of investment. In this trading system, at first the optimal value of technical indicator's parameters is determined by using multi-objective particle swarm optimization and according to these parameters; technical indicators are calculated to predict stock price changes with the help of adaptive neural fuzzy inference system. We have chosen eight different stocks from Tehran stock exchange to test our trading system for two months. A computational experience is carried out in order to analyze the proposed algorithm and the obtained results are compared with usual conventional methods which have been proposed in previous researches. The computational results show our proposed method performs better than other previous methods and obtains superior results.

Deciding about portfolio selection and composition are the main challenge of investment institutions. The nature of analysis and effective criteria in industry and company are different. They should rank the industry and companies options with regard to multiple and conflict criteria that they have different weights and priorities. This article seeks to provide two-stage approach for stock selection and portfolio composition. We enriched promethee (compensatory MCDM method) by weight of criteria. Then we used it in intended approach. The expert opinions and objective data used to criteria ranking. An ANP was used to determining weight of criteria that merge in promethee method. After designing we tried to validate our approach with real data. The results show we have 7% (simple mode) and 11% (enriched mode) return more than actual performance. Using of Porter five forces analysis for indentifying and verify industry criteria, to include some indices from corporate governance and technical analysis, weighting the criteria and integrate them in the promethee are our research novelty.

Insurance companies are important economic institutions in each country and support other economic institutions and good performance of them has an effective role in economic boom. Insurance companies to achieve strong and effective performance must be aware of the environment، the competitor and their performance and carry out necessary actions with regard to this information. In this study، the performance of insurance companies in Iran during 2007-2009 has been evaluated. The weight of each index has been determined using Shannon entropy and then insurance companies have been ranked using four methods of multi attribute decision making including SAW، TOPSIS، VIKOR and LA. The results of the different methods have been combined using Copeland method. The results showed that Iran insurance company has had the best performance during these years.

The mutual funds industry has recently grown drastically and is now considered as an alternative channel investment in financial markets. Many of these funds are relatively risky in that for higher returns they aim to select growth companies which are expected to post large stock price increases. There are many traditional indices to assess the relationship between returns and risks in financial economics. The main drawback of these indices is being one-dimensional. This paper is to introduce and apply a new multi-criteria method based upon multi-directional inefficiency analysis to assess the relationship between return and risk and to measure risk excesses of a sample of Iranian mutual funds in the year 2010. The findings of this study show that the sampled founds can acquire the same returns by merely accepting 66 and 64 per cent of the received unsystematic and systematic risks, respectively.

Investment managers of financial institutions are favor to identify superior companies to select those in their stock portfolio and hedge probable risks and achieve predetermined return. The main question is how we can sieve companies and basically which companies are superior. Therefore in this thesis we designed a novel sieving model by the use of multi criteria decision making and analytic network process. The model contains of 5 clusters including operating efficiency, profitability, risk, growth and market, which are consist of 21 nodes and subcriteria that indicate companies performance. After consulting with experts and applying analytic network process technique, subcriteria reduced to 18 effective criteria and used to score each company. The model ran for 10 active car manufacturing companies in Tehran Stock Exchange (TSE). The consequences of proposed stock sieving model compared with results of Iran Tahlil stock ranking model which is popular in (TSE). It showed on the 95% confidence level, results of two models have weak correlation. To assess validity and performance of each models, we defined profitability index because of stock yield importance for investors, and assessed two models respect to index. Results indicated performance of proposed stock sieving model is greater than Iran Tahlil ranking model.