داریوش فرید

The primary objective of this research was to analyze the relative importance of working capital management factors in predicting financial distress among companies. The study population consisted of 167 companies listed on the Tehran Stock Exchange (TSE) from 2019 to 2023. 7 key working capital management indicators were selected based on their potential impacts on financial distress. Using Zavgren’s (1985) financial distress prediction model, the sample companies were classified into distressed and healthy groups. In the first step, a random forest algorithm was employed to assess the predictive power of the seven working capital management indicators in classifying companies as distressed or healthy. The results indicated that these indicators could successfully identify and predict the financial distress status of the companies with up to 85% accuracy. In the second step, the unique feature of the random forest algorithm was leveraged to rank the importance of each working capital component in achieving this 85% classification accuracy. The findings showed that the Average Collection Period (ACP) was significantly more important than the other working capital components in predicting financial distress.Keywords: Financial Distress, Working Capital Management, Average Collection Period (ACP), Random Forest AlgorithmJEL Classification: G01, G30, C38 IntroductionIn recent years, financial distress and bankruptcy have become increasingly prevalent issues for business enterprises. The financial literature offers various definitions to describe the state of financial distress and bankruptcy. While some researchers equate financial distress with bankruptcy, financial distress is more accurately viewed as a precursor to bankruptcy – a stage of financial decline that may or may not ultimately lead to a company's bankruptcy. Simply put, financial distress reflects a business entity's inability or weakness in fulfilling its obligations to creditors (Gerged et al., 2022). Given the rapid growth of joint-stock companies and the emergence of severe financial crises at both micro- and macro-economic scales, it is crucial to identify the key factors that can predict a company's financial health before it reaches the stage of bankruptcy, i.e., during the financial distress phase (Pourheydari et al., 2010). Evidence suggests that working capital management is a significant factor influencing the financial distress of business enterprises (Geng et al., 2015). Companies experiencing financial distress and bankruptcy often exhibit weaknesses in working capital management, particularly in cash control. Therefore, the aim of this study was to evaluate the predictive power of working capital management components in forecasting financial distress and rank the importance of each component in this prediction process.Materials & MethodsThe raw financial statement data for this research were extracted from Rahavard Novin Database and the Codal website. These data were then systematically organized in Excel. After applying certain eligibility criteria, a sample of 167 companies was identified as the accessible statistical population. To classify the sample companies into distressed and healthy groups, which served as the target variable (label), Zavgren’s (1985) financial distress prediction model was utilized. Subsequently, the predictive power of 7 key working capital management components in forecasting financial distress was tested using Python software and the random forest algorithm.The random forest method is based on ensemble learning, wherein the data are split into training and testing sets. During the learning phase, the model attempts to identify the inherent pattern or the relationship between the dependent variable (financial distress) and each explanatory variable (working capital management components) with the validity of this learning measured by the testing data. The random forest method employs a bagging approach, creating subsets from the entire dataset and determining the final result based on the average outcomes of these subsets. This approach helps to significantly mitigate the overfitting problem.One notable feature of the random forest algorithm is its ability to rank the importance of the input features in determining the trend of the target variables. This capability was leveraged in this research to answer the second research question, which focused on the relative importance of each working capital component in predicting financial distress. Research FindingsThe model achieved an accuracy of 85%, indicating that it could correctly predict whether a company was in financial distress or not based on what it learned during the training phase. Additionally, the model's F1-Score metric was 0.89 for identifying healthy companies and 0.76 for predicting distressed companies. These scores, being close to 1, suggested that the model's estimations were performed with a high degree of accuracy.The analysis of the relative importance of each working capital management component in achieving this 85% accuracy rate revealed some key insights. The Average Collection Period (ACP) was identified as the most important factor in predicting financial distress. Following the ACP, the Current Ratio (CR) ranked second, the Average Payable Period (APP) ranked third, and the Inventory Turnover In Days (ITID) ranked fourth in importance.These findings suggested that the initial signs of financial trouble for a company often stemmed from its failure to collect receivables in a timely manner, leading to an increased collection period. If the company's management did not effectively address this issue, other problems could likely arise, ultimately pushing the business entity into a state of financial distress. Discussion of Results & ConclusionThe results of the data analysis using the random forest algorithm indicated that working capital management indicators had an 85% predictive power for identifying financial distress in companies. This finding is consistent with those of the previous studies by Habib and Kayani (2022), Morshed (2020), and Li et al. (2018). Regarding the second research objective, which aimed to rank the importance of each working capital management component in predicting financial distress, the analysis revealed that the Average Collection Period (ACP) was the most significant factor. This suggested that a company's inability to collect receivables in a timely manner was a crucial early indicator of impending financial distress.An increase in the ACP could lead to a serious risk of bad debts and liquidity problems for the company. As a result, the company's management might need to secure additional working capital to fund operations, which could potentially increase the Weighted Average Cost of Capital (WACC). However, if the company failed to generate adequate returns to cover these elevated financing costs, it might ultimately fall into a state of financial distress (Panigrahi, 2014). Given the notable importance of the ACP compared to other working capital management components, it appeared that many of the underlying issues leading to financial distress stemmed from poor performance in collecting receivables. Therefore, this research underscored the critical need for robust management practices of receivables to maintain liquidity and avoid the escalating costs and risks associated with financial distress.

The present research aims to identify the influential variables on stock portfolio selection, prioritize these variables, and estimate the risk and return of sample stocks using neural network algorithms.

Rationale: 

Stock portfolio selection has always been an intriguing and practical issue in financial matters and financial markets. In order to address the existing drawbacks in research related to stock portfolio selection, the idea of employing the fuzzy logarithmic preference programming method for analyzing factors affecting stock portfolio selection and utilizing neural networks for risk and return estimation is reinforced.

The present research offers a novel combined approach for stock portfolio selection consisting of two stages: In the first stage, by conducting interviews with experts and examining available documents and records, six primary criteria for selecting an optimal stock portfolio are identified. Using the fuzzy logarithmic preference programming approach, the weights of these criteria are determined. In the second stage, the risk and return of stocks are predicted using neural network algorithms.

The findings indicate that profitability, efficiency, and risk are the most important criteria in selecting an optimal stock portfolio, respectively. Additionally, the designed neural network successfully fitted the returns and risks of stocks.

Investment funds serve as a one of the cornerstones of economic development. They provide several key advantages that contribute to a thriving financial ecosystem Like Economies of scale, Professional management, Enhanced liquidity, Mitigated unsystematic risk. Case in point, the Iranian economy has long grappled with liquidity and financing constraints. The introduction of novel financial instruments, such as investment funds, holds the potential to alleviate these challenges. Within a capital market framework, various types of investment funds can be established to cater to specific needs Like Project funds, Venture capital, Construction Fund, Private equity. Investment funds play a pivotal role in mobilizing smaller investments and channeling them towards high-growth sectors, particularly technology, which can significantly propel economic expansion. Recognizing the critical role of investment funds, this research delves into the operational mechanisms of these various fund types.

The category of knowledge has always been the subject of human discussion, and its yield in the field of economics is the presentation of the knowledge-based economy concept, which emphasizes on the production and distribution of goods and services and takes into account the share of knowledge. Employment in the knowledge-based economy is characterized by increasing demand for highly skilled workers, and the member countries of economic cooperation and development tend to have high-tech intensive knowledge in the production and employment sectors. In developing countries, knowledge-based economy and knowledge-based employment are carried out in a scattered manner and a fixed and incomplete pattern. In Iran, the issue of educated unemployment in the provinces is one of the problems that has always attracted the attention of provincial officials. According to the statistics, 49% of the graduates of the universities in Yazd have bachelor's degrees, 26% associate degrees, 20% master's degrees, 2% professional doctorate degrees, and 1% specialized doctorate degrees in the fields of humanities, technology, engineering, art, medicine, basic sciences, agriculture and veterinary medicine. Most of the male graduates are in technical and engineering fields and humanities, and the female graduates studied humanities and arts. Most of the graduates are from the universities of the Ministry of Science, Research and Technology (20%), technical and vocational centers (20%) and Islamic Azad universities (19%). Most of the graduates of the province studied in Yazd, Meybod, Taft and Ardakan cities. According to the statistical yearbook of Yazd Province, the unemployment rate in the population of graduates or students studying in higher education is 14031, and the number of the unemployed is 45912. Therefore, one of the main problems in the province is the growing unemployment rate, especially among university graduates looking for a job. Also, the slow trend of sustainable employment and employment of students in the country hinders the full realization of the knowledge-based economy. In this regard, the present study seeks to provide solutions to increase the employment of knowledge-based people in Yazd Province.

Given the lack of theoretical foundations regarding the concept of knowledge-based employment and the need to recognize its dimensions and a comprehensive explanation of the factors affecting the development of knowledge-based employment in the field, this exploratory study is conducted on fundamental themes, dimensions and concepts through interviews with experts. In the target community, the other related sources were extracted and, at the same time, the data analysis and coding were performed using the Atlas IT8 software.

The process of analysis in theorizing began with the coding of the data, and then the relationship between the categories produced in the open coding stage was established. In this process, one category was recognized as the central or main category and then the other categories were set as sub-categories. Under the various headings of the paradigm model, the main category was related, and, in the last stage, the concepts were regularly related together. According to the results, the development of employment as a causal condition, the interaction of university, industry and government as a central category of information and communication technology, social capital, policy institution, innovation and marketing, education system and macroeconomic conditions of the country as institutional factors were identified as the context for regional development, economic growth, development of knowledge-based companies, and dissemination of collaborative learning based on mutual trust as strategies, interactions and actions. They provide a framework for the development of knowledge-based employment.

Interaction of university, industry and government in the context of institutional conditions such as institutional convergence, dissemination of institutional learning, cohesion and integration of the institutional structure of society under the influence of intervening factors such as information and communication technology, social capital, political institution, innovation and marketing, education system, and the macroeconomic conditions of the country lead to strategies of interaction and action such as regional development, economic growth and development, development of knowledge-based companies, and the dissemination of collaborative learning based on mutual trust. The result of these strategies is the development of knowledge-based employment with issues such as networking between academic institutions and knowledge-based companies, the consideration of science and technology policy as national assets, possession of a well-educated human resources, production of knowledge based on community needs in practical contexts, the activity of universities as scientific-economic institutions and academic groups as knowledge enterprises, improvement of technological capability in major industries, networking between academic institutions and private companies, activities and interdisciplinary learning based on real operational issues, and integration of science and technology by developing strategies and macro-policies.

The daily observations of the total index of the Tehran Stock Exchange show that in the last few years, stock prices have been very volatile. This volatility can harm the economic environment of Iran. Modeling and predicting price volatility in this market can provide important information about uncertainty and risk to actors and thus help with managing possible unwanted changes in the field of financial investments. This, along with the rise in the value of the stock market in recent years, has caused the investigation of the issue of volatility to become increasingly popular among academicians and financial policymakers. Volatility and risk measurements are essential parameters in risk management programs and can affect a country’s economic activity and public confidence. These are also key parameters in studies that examine the relationship between the stock market, economic growth, and other financial variables. Tehran Stock Exchange markets have been volatile in recent years. Controlling the negative effects caused by stock price volatility has made it necessary to predict and model price dynamics for participants in this market. In this paper, the class of Parameter-Driven volatility models (stochastic volatility models) is used to predict the price volatility and calculate the risk of the price index in the Tehran stock market. Therefore, four stochastic volatility models were used. To make a comprehensive review, the asymmetry in the volatility (leverage effect) and the heavy tail of the stock price return distribution (with t- student distribution and Skew normal) have been included in the models. To estimate the models, the Gibbs sampling method was used, and to accurately compare the models, the test based on the posterior distribution of the models and the Bayesian factor was used. The results indicated that the canonical stochastic volatility model with Skew normal distribution (SNSV) is more effective than other stochastic volatility models in predicting the price of stock market volatility based on the Bayesian factor. Therefore, to analyze stock market risks using stochastic volatility models, there is no need to include the leverage effect in the state space of the Volatility equation. The SNSV model makes it possible to observe volatility and make predictions related to it, thereby improving market transparency and ultimately making diversification and risk management easier to implement. Also, the backtests of VaR and CVaR market risk assessment using Kupiec and DQ tests do not show evidence that the estimation is over or under the risk limit. As a result, the calculation of volatility and pricing with this model will lead to more precision risk management for professionals, especially fund managers who intend to include Tehran Stock Exchange stocks for asset allocation.

Financial distress of banks and public and private companies is a serious issue for the economies of the countries. The individual and social costs of financial distress have raised the problem of financial distress for many managers, banks, investors, policy-makers and auditors as a major issue. Due to the development of companies, increasing economic activity, intensifying competition and cycles of inflation and recession in recent decades, the number of financially distress companies and the importance of helplessness is increasing. The issue of bankruptcy and financial distress has always been a matter of concern. Therefore, it is very important to study the causes of financial distress and evaluate financial distress with the help of common models.In this research, financial distress is modeled using the system dynamics method. For this purpose, the factors affecting financial distress were first identified after studying the research literature and using the opinion of experts, the most important of these factors were selected. Then, using the identified components, the causal diagram was developed and based on the causal diagram, the flow diagram was developed. Scenarios were proposed using the flow model.

One of the important factors affecting the participation of investors in the stock market is the existence of information about the trends and price volatility of this market. In the past years, the economic sanctions and the Covid-19 epidemic have affected the Iran stock market. The continuous decrease of the stock market index is one of the consequences of volatility persistence waves caused by these events. Some financial theories have shown that the decline in stock prices can be caused by the existence of a unit root in the volatility of the market's price returns. In this research, the hypothesis of the drop in stock prices due to the presence of a unit root in the volatility was investigated with the data of the Tehran Stock Exchange index between 2016.September.21 to 2022.March.19 and using the stochastic volatility model introduced by Su and Lee (1999). The findings of this paper indicate that the posterior estimate of the coefficient of ϕ in the Stochastic volatility model is equal to one, therefore, it is not possible to reject the inappropriate performance of the market and the hypothesis of the fall of the Tehran Stock Exchange index as a result of the volatility persistence.

In recent years, with the increase in the penetration rate of the capital market, more people have invested in the stock market. Predicting the stock prices accurately with the least error can reduce investment risk and increase investment return. Due to nonlinear fluctuations, stock prices prediction is often described as a subject of nonlinear time series that is influenced by many factors. In this study, the bidirectional long short-term memory (BiLSTM) method for predicting stock prices is evaluated. In this regard, several machine learning techniques are applied to predict stocks prices using time series, and finally two deep learning methods including a recurrent neural network algorithm (LSTM) and a bidirectional neural network algorithm (BiLSTM) are implemented and their results are compared. Time series data of price characteristics including open, closed, high and low prices for the selected value stocks listed in Tehran stock exchange and the OTC, are used as a case study to implement the mentioned methods. Considering the evaluation criteria of RMSE and R-Square, the results of this study indicated that the combined PSO-BiLSTM algorithm, predicts the stock prices more accurately and has a better performance than the BiLSTM, LSTM, SVR, CART and MLP algorithms.

Achieving a strong and efficient monetary cycle is of great importance and necessity due to the dependency of corporates on banks in Iran. Owing to the importance of cash management in firms, this study assesses the impact of bank health on Cash flow sensitivity of cash of listed corporates on Tehran Stock Exchange (TSE) by analyzing 102 firms which have received facilities from 20 active Iranian banks during 2015 to 2019. To calculate bank health, CAMELS indicators have been used. Two main hypotheses along with related sub hypotheses have been considered and tested by estimating panel data models. Results show that the quality of assets, as an indicator of Bank health, has a negative effect on sensitivity of cash held which is consistent with the bank power hypothesis ؛and the liquidity index as another bank health indicator negatively affects the sensitivity of the cash held, which is consistent with the hypothesis of financial constraint.

Modern investment management began with Markowitz's theory on how to select an optimal portfolio based on the mean-variance model, and then continued with various optimization methods such as conditional value at risk model, half-variance model, and the mean-absolute deviation model. One of the most important weaknesses of these models is not paying attention to investors' views and relying on past information. The Black Literman model has largely eliminated this weakness by combining a matrix of investor views and past returns. In this research, using fundamental analysis, the matrix of investor views is formed and then the performance of the optimal portfolio of the Black Literman model is compared with existing models. The research period is between 2016 to 2021 and MATLAB software has been used to obtain the optimal portfolio. The results show that using different evaluation criteria, the fundamental Black Literman model performs better than other existing optimization models.

Since portfolio optimization models are based on past information, the efficiency of these models has always been questioned. In this study, first, an optimization model based on investor views is introduced and then the performance of all optimization models are compared with the performance mutual funds to both measure the effectiveness of these models and to achieve a practical model for this purpose. The research period is between 2016 and 1400 and MATLAB software has been used to obtain the optimal portfolio. The results show that using different evaluation criteria, the optimal portfolio of Black Literman model performs better than other optimization models and mutual funds; Also, the returns generated by all optimization models at the market risk level were significantly higher than the average returns of equity mutual funds and top mutual funds.

This study investigates the dynamic relationship among diversification, capital structure and profitability of firms listed in Tehran Stock Exchange. The research sample selected using systematic elimination method, consists of 140 firms listed in TSE from 2011 to 2017. The hypotheses are analyzed using the panel VAR approach. Impulse response functions illustrate significant dynamic interrelationships in the financial sector. The results indicate that any coincident increase in finance diversification and capital structure of a firm optimizes a its profitability, and any increase in profitability results in increase of diver sification and capital structure. With increasing diversification, profitability will increase and capital structure will decrease. Further, if capital structure increases, the profitability will increase and diversification will decrease in firms listed in Tehran Stock Exchange.

The stock market is especially important as a capital investment instrument both for the investor and for the investor. Stock markets are affected not only by large parameters but also by thousands of other factors. The high number and unknown factors affecting stock prices, as well as the complexity of the relationship between these factors and the stock price, have caused uncertainty in the field of investment. One of the tools to reduce uncertainty and the most important discussion of investing in the stock exchange is the selection of the stock portfolio optimally. In this study, through interviewing experts and reviewing existing documents, 6 main criteria for identifying the optimal stock portfolio selection were identified. Using logistic fuzzy preferential programming, weighing these criteria and, finally, weighting the indexes related to We defined each criterion. The results show that the criteria for profitability, efficiency and risk are the most important criteria in choosing stock portfolios

Volatility in companies’ capital structure has disrupted managers’ concentration required to ‎create, maintain, and increase return and, consequently, attempts to improve the company’s ‎performance. One of the managers’ tasks is to decide on an appropriate mix of financial resources ‎of the company, i.e., the capital structure. This research aims to study the role of macroeconomic ‎variables in the capital structure volatility for companies listed on the Tehran Stock Exchange. ‎The sample consisted of 89 companies listed in the Tehran Stock Exchange during the period ‎‎2014-2018, chosen by the systematic random sampling method. The results indicate that the ‎volatility of the short-term and long-term debt structure increases with an increase in the inflation ‎rate. Moreover, the volatility decreases as the interest rate increase. Furthermore, the long-term ‎debt structure of the companies rises slightly as the economic growth rate increases.‎

The aim of this study is to compare the New Hybrid Method with the usual Markowitz method in creating an optimal portfolio. To this end, at the first stage, the future stock prices were predicted using a deep DNN learning method and stock technical variables for the period 1397/4/2 to 1397/6/2. Then, based on future stock prices, stock return and risk were calculated and, by using Gravitational Algorithm, portfolio profits were maximized. This results in creating low risk to high risk portfolios on the Pareto efficient frontier. After that, the future return of portfolios was calculated for the next two months, and the process was repeated for 30 weeks in the form of weekly Rolling Window. These results were compared with the results of usual Markovitz method for 30 periods. The results indicated that both Markowitz and New Hybrid methods showed only better performance in predicting stock prices of risk averse portfolios than average market index.

Informativeness by companies is of great importance for managers to transfer information about financial performance to investors, creditors and other stakeholders, and is one of the significant reasons for requesting information on stock prices, informativeness issues, and information asymmetry. Several factors affect the transparency and informativeness of stock prices – including managers’ reputation motivation– which results from their potential effort to display the benefit of operation in terms of goals such as credibility and trust-building.

Thus, the purpose of this study is to explain the effect of managers' reputation motivation on stock price disclosure. To do so, the evidence of 910 firm-year (130 companies for 7 years), collected from annual financial reports of companies listed in Tehran Stock Exchange from 2011 to 2017, was tested.

Research findings show a significant relationship between the managers' reputation motivation and stock price disclosure. Further, the size of the company has a positive and significant impact on stock price disclosure.

Nowadays, bank deposits are one of the most important economic and social tools that depend on various business, service and economic conditions and have an important role in sustainable economic growth and development in countries. In order to provide financial assistance to depositors with privileged positions, equip the existing resources, and provide funds and create planning opportunities for the banking system, it is especially important to have access to adequate bank deposits.With a regard to the necessity of investigating and measuring the impact of economic variables on the amount of attracted deposits, we have used panel data to study the impacts of several factors on the amount of deposits in the banking system. These factors include the number of the employed, value added in various economic sectors and provinces, paid loans, active population, provincial household expenses, inflation rate, liquidity and weighted interest rate. The results are indicative of the fact that independent variables including service sector value added, paid loans and household expenses have positive impacts on the dependent variable. In contrast, the other variables including inflation and active population have a significant negative impact on the dependent variable. Different statistical tests were also conducted using the panel data to choose the random effect model as the final model. According to this model, there is no need to estimate separate regressions for each province.

The aim of this study was to evaluate the impact type of finance on investment efficiency in the company is listed on the Tehran Stock Exchange. What distinguishes this research from other research in the field of investment efficiency is variable financing policy Using the Hierarchical model that the debt ratio, financial leverage and accumulated earnings have used is measured and to measure the efficiency of investment have used biddle (2009). sample Statistical has been selected using systematic elimination and restricting the number of variables 85 years (1390-1394). hypotheses, were tested and analyzed using regression model and control variables taking into account the value of the company, ROA, ROE and net working capital using panel data regression technique with E'views9 software. The results suggest that financial policies have significant impact on investment efficiency. In addition, financial policies and investment efficiency have different effects in companies with different values.
To users of financial statements including investors recommended to choose companies with higher debt ratio and financial leverage and less accumulated earnings.

Modifications are an integrated part of economic evolution in financial section which include reforming the capital market structure, innovating in tools and an environment with more comprehensive discipline. Two dominant schools of thought in the literature on stock markets are fundamental and technical analysis. Selection the portfolio would be so important. So we use both fundamental and technical analysis to evaluate companies' stock and then in order to form a portfolio which consider different risk states and investor’s preferences utilize Mamdani Fuzzy and mixed integer linear programming model. The reasons for the use Mamdani Fuzzy system are its capability of working in vague environment and using human knowledge and for mixed integer linear programming model is its capability in finding the optimum solution among the several available ones. The results of evaluating the performance of formed portfolio for three cases of Risk averse, Risk neutral and Risk prone investor show that the performance of proposed portfolio is positive and proper, but in a more accurate scale the formed portfolio has a more proper condition for the risk averse investor.

Profit is one of the most important factors that encourage investors to invest in the Stock Exchange. Because of the uncertainty in the stock market and its impact on investment, in this study, the importance of momentum strategies (momentum) was discussed. To this end, 48 companies listed in Tehran Stock Exchange for a period of 6 years from 1387 to 1392 were studied. The results of the analysis show that the majority of the securities portfolio trading strategies winner than a loser portfolio shows systemic risk. Also in the six-month period the highest excess returns from deployment strategy has created momentum in the course of the relationship between systemic risk and efficiency is very strong.

This study investigates the relationship between working capital and corporate performance and keeping in mind that financial situations may have an important role in this regard, the relationship between the optimal level of working capital with financial constraints was examined. The sample consists of 82 companies listed in Tehran Stock Exchange during the period from 1388 to 1392. Tobin’s Q is used as a proxy for corporate performance, while net trade cycle (NTC) is used as proxy for working capital and dividend (DIV), Interest coverage (IC), ww index (WW), kz index (KZ), cash flow (CF) used as a proxy for financial constraints.The panel regression analysis (with fixed effect) is utilized to test the hypotheses. The results show that there is an inverse u-shaped relationship between the working capital and corporate performance and the optimal level of working capital for companies that have greater financial constraints (except Interest coverage) is lower.

All of investors in the Stock Exchange face risks. Thus, the measurement of risk is among the most important matters to investors. This study deals with the measurement of risk by criterion of Value at Risk. VaR was measured by using a GARCH model. The stock of the listed companies in Tehran Stock Exchange for the cement industry and the time period of the study is the price of cement companies between the years of 1389 to 1391. The results show that the GARCH model has good performance in estimating VaR in stock of listed companies in Tehran Stock Exchange for the cement industry and it can be concluded that the GARCH (1,1) model with the t-student distribution for the majority of listed companies is optimal. The results of this research can lead to favorable outcomes for risk management decisions.

Considering the economical situations and the sanctions on our country, attracting internal assets is very important to boost the national economy and to increases the capacity of financial agencies. On the other hand it's necessary to know that one of the ways to attract assets and to supply the capital is to sell the stock of companies in the exchange market. The driving factors for investment in exchange market and to sell stocks are divided into two categories; financial and non financial. The purpose of this study is to identify, categorize and present a method for the nonfinancial factors affecting the purchase of a company's stock in the exchange market by an investor.
To identify these factors, we have used sources and field studies. To categorize the factors and respond the obligatory ones, Kano model of Quality Function Development is used and finally by the Pareto rule 20% of factors are considered. The statistical societies of this study are the investors in the stock market of Yazd, 350 of who are chosen randomly. This study has a practical approach. It’s a survey considering its research strategy and it has single frame for its time run. The results of this study show that organizations can improve their stock selling by informational transparency, using media advertising and publishing magazines and bulletins.