seyed mahdi sadat rasoul

Despite the limited financial resources available for technological entrepreneurship in Iran, particularly in terms of bank-oriented financing, venture capital (VC) plays a relatively minor role in financing knowledge-based and technology-driven companies. Strengthening the legal infrastructure through appropriate incentives could promote VC growth. This article, after reviewing the theoretical foundations, performs an importance-performance analysis of the legal components affecting VC in Iran at two levels: macro policies and specific regulations related to VC funds. This analysis was carried out by distributing a semi-structured questionnaire among CEOs and vice presidents of VC funds, non-governmental research and technology funds, and other private companies. The current state of the legal infrastructure for VC was then examined by analyzing relevant laws and comparing them with prioritized legal components. The aim was to suggest solutions to address legal gaps and enhance relevant laws. The findings indicated that, except for tax incentives, other key legal components necessary for VC development are not adequately addressed in the existing laws. To address this deficiency, it is recommended that the social security law be revised to consider the nature of startups and technology companies, and that the intellectual property system be organized to increase demand for venture capital. Additionally, policymakers are advised to continue and streamline processes related to existing tax exemptions, provide tax exemptions for profits reinvested in production rather than distributed among shareholders, and impose taxes on speculative activities outside the innovation ecosystem to channel financial resources into technological entrepreneurship.

In the recent years, science and technology in urban green space have largely focused on technologies that facilitate infiltration and reduce runoff (such as rain gardens and permeable sidewalks). Trees in urban green space reduce the net rainfall by interception, and on the other hand, their extensive root systems allow them to store and direct significant amounts of water into the soil. The present study investigates the effect of rainfall amount and tree species on rainfall interception in Hashtgerd city of Alborz province during two seasons of winter 2017 and spring 2018. For this purpose, during seven rainfall events, the amount of throughfall was measured by the number of five rain gauges installed under each tree. In order to record rainfall events, a rain gage container was installed in a location that was sufficiently distant from buildings and trees, and rainfall events ranging from 2.1 to 6.8 mm were recorded. The results showed that the percentages of rainfall interception for spruce, apricot, fig, willow, walnut, and oak species were 44.6, 42.6, 36.4, 35.1, 33.6 and 30.4 percent, respectively. The results of statistical analysis showed that there is a significant difference among the values of rainfall interception in different tree species (P <0.01). Also, there is a significant difference among the rainfall interception in the rainfall classes (low (lower than 4 mm), medium (4-6 mm), and high (higher than 6 mm)) (P <0.01). Among the studied species, sparrow and apricot species have the highest rainfall interception, which it is possible to make more use of these two types in the control of runoff with urban planning.

Companies have different considerations for using smoothing in their financial statements, including annual general meeting, auditing, Regulatory and Supervisory institutions and shareholders requirements. Smoothing is done based on the various possible and feasible choices in identifying company’s incomes, costs, expenses, assets and liabilities. Smoothing can affect credit scoring models reliability, it can cause to providing/not providing facilities to a non-worthy/worthy organization orderly, which are both known as decision errors and are reported as “type I” and “type II” errors, which are very important for Banks Loan portfolio. This paper investigates this issue for the first time in credit scoring studies on the authors knowledge and searches. The data of companies associated with a major Asian Bank are first applied using logistic regression. Different smoothing scenarios are tested, using wilcoxon statistic indicated that traditional credit scoring models have significant errors when smoothing procedures have more than 20% change in adjusting company’s financial statements and balance sheets parameters.

Creating demand for technological products, financing innovation, outsourcing research and development, etc. are among the effective actions of large firms in interaction with knowledge-based firms. On the other hand, knowledge-based firms can also play an effective role in meeting the technological needs of large firms with their scientific and technical ability do However, the interaction of these firms is facing problems due to various reasons and the cooperation of these two pillars of Iran's knowledge-based economy is far from its desired position. In the meantime, government policies can play a significant role in facilitating these connections. South Korea is one of the governments that has taken effective steps to develop interaction between firms by passing the Law on Mutually Beneficial Cooperation between Large Firms and Small Firms and forming the Ministry of Small and Medium Enterprises and Startups. This research, with a comparative analysis and a comparative study of the policies for the development of technological cooperation between South Korea and Iran and the division of the policy tools of these two governments into three categories of tools including tools for stimulating demand, tools for stimulating supply and tools for improving cooperation infrastructure, looking for solutions to develop these cooperations in Iran Is. The focus on supply stimulation tools and cooperation infrastructures is remarkable in Iran, but unlike South Korea, which has used important tools to stimulate demand, there is a serious gap in demand stimulation tools in Iran.

Imposing sanctions on some industries in Iran over the years have led many large industries to seek supplies from domestic companies. In these circumstances, and given that many needs are met through the collaboration of companies, the development of technological collaboration seems essential. Therefore, many large companies are looking for technological collaboration with other companies. Despite the need for technological collaboration in the current situation, technological collaboration faces serious challenges and in some cases this collaboration does not take shape. Hence, the government could develop collaborations by establishing policy tools. So, this research aims to identify the policy tools for the development of technological collaboration between industries and knowledge-based companies to achieve the desired situation in country. In this regard, by conducting purposeful interviews with experts in the field, the policy tools for the development of technological collaboration were extracted and examined by content analysis. These tools can be classified into three categories: supply stimulation tools, demand stimulation tools and infrastructure improvement tools.

One of the most important risks that the banks and financial institutes face, is credit risk which is related to not-paid instalments or the instalments paid with delay by borrowers. Banks use credit scoring models In order to prevent this type of risk. The goal of this research is to investigate the joint effect of economic time cycles and the industry sector on credit scoring models we are seeking to answer the key question: “when should bank change their credit scoring models based on economic time cycles and for which industry sectors?”. The dataset of the research involves all companies that were applied for a loan in one of the Iranian major banks during the years 2008-2011. The companies have been divided into four industry sectors including “Industry and Mine”, “Oil and chemical”, “Service and Infrastructure” and finally “Agriculture”. Based on the sector of the industry and year, 54 explanatory variables, both financial and non-financial, 12 distinct industry sectors and time-specific data sets are built then classification methods were used to classify customers into two groups of defaults and non-defaults. Finally, we compared the results by Wilcoxon Test. The results show that the companies that are in the groups of Industry and Mine and Agriculture, need their own special credit scoring based on industry type model and time but two other groups don’t need of course in the studies dataset duration. Finally, the study concluded by introducing the credit scoring strategies for different four-cycle of economies

Credit risk is one of the most important risks which banks and financial organizations face. It is related to unpaid and delayed installments. Banks evaluate their customers' credit in order to prevent this hazard. Development banks, which are the focus of this research, fund facilities based on working capital, so customers sometimes do fraud in declaring working capital. Considering fraud consequences and making a credit scoring model with sensitivity to fraud are the main aims of this research.  The statistical population of this research includes companies who have referred to branches of an Iranian Bank. This research includes 55 financial and non-financial variables based on the credit scoring model. In the first step, fraudulent companies have been realized. Finally, in order to offer an optimized and sustainable model through merging machine learning methods and reporting performance evaluation indicators, the impacts of fraud have been considered.

After the success of Digikala as the largest online retailer in Iran, many online retailers emerged to achieve their appropriate market share. It seems that examining customer behavior in online retailers needs to be further explored and enhanced to achieve competitive power. The purpose of this study is to investigate the impact of pre-buying, buying, and post-buying processes on customer experience and the effects of customer experience on customer satisfaction, then customer satisfaction and customer experience on Repurchase behavior in online retailers. The current research is descriptive and applied. The community of this study is all those who have experience in buying from Digikala retailers. To determine the sample size, the Cochran formula was applied, and because of the infinite statistical population, a sample size of 384 people was selected. A researcher-made questionnaire was created, and a combination was used to collect data. In order to analyze the data obtained from the questionnaire, the structural equation modeling method was used. The hypothetical test was performed using descriptive and inferential statistics. The results suggest that pre-buying, buying, and post-buying processes affect the customer experience in Digikala online retail. Customer experience also affects customer satisfaction and re-purchasing from Digikala online retailers.

Nowadays, fundraising is one of the most important issues for both Fintech investors and startups. The pattern of fundraising in terms of “number and type of rounds and stages needed” are important. The diverse features and factors that could stem from Fintech business models which can influence success are of the key issues in shaping these patterns. This study applied the top 100 KPMG Fintech startups’ data to extract clusters and fundraising pattern using sequential pattern discovery for each cluster. This led to the extraction of seven distinct clusters using 3 different clustering algorithms from 3 to 7 different rounds of investment for each cluster. The proposed frequent patterns can assist both investors and Fintech startups to show the future fundraising pattern based on their new or current startup cluster type and the ongoing stages of development.

In the current economy and entrepreneurship situation in developing countries, experts maintain that establishing startup companies proves an efficient and promising approach. In spite of the fact, many startup companies are failed. However, there are key factors, which can help such companies follow the path to success. This research aims to identify the factors, which led to success of startup companies in Iran. Accordingly, one of the important roles of data mining is exploring the relationship between datasets, and getting the result of a series of association-based rules for identifying the strong relations between business activities. The data of this study is collected from a total of 165 Iranian startup companies. The feature's columns included 41 success factors of startup companies addressed in domestic and foreign studies. For the prediction phase, we use the help of support vector machine algorithm, decision tree and k-nearest neighboring to classification. The feature selection technique in order to come up with the most efficient success factor of startup companies is Cuckoo search. Finally, rules are extracted by means of Apriori Algorithms. The results indicate that factors such as, namely, entrepreneurship experience, working duration, skills, type of service or product, target market, Blue Ocean or Red Ocean strategy, flexibility, scalability, customer loyalty, presence or lack of presence in an accelerator, and first-stage of investor are the most important factors that have the greatest effects in a startup's success. Moreover, the extracted results reveal that flexibility, and scalability are considered as two key factors contributing to success of Iranian startup companies.

Credit risk management is a process in which banks estimate probability of default (PD) for each loan applicant. Data sets of previous loan applicants are built by gathering their data, and these internal data sets are usually completed using external credit bureau’s data and finally used for estimating PD in banks. There is also a continuous interest for bank to use rule based classifiers to build their default prediction models. However, in practice the data records are usually incomplete and have some missing values and this make problems for banks, especially in credit risk portfolios which are low default and makes model rule based building complex. Several strategies could be used in order to handle the missing data issue. This paper used five missing value handling strategies including; ignoring, replacing with random, mean, C&R tree induced values and elimination strategies in a real credit scoring dataset. Experimental results show that ignoring strategy consistently outperforms other methods on test data set, and suggest that the CHAID is a useful classifier for handling low default portfolios with missing value.

The Basel II Accord pointed out benefits of credit risk management through internal models to estimate Probability of Default (PD). Banks use default predictions to estimate the loan applicants’ PD. However, in practice, PD is not useful and banks applied credit scorecards for their decision making process. Also the competitive pressures in lending industry forced banks to use profit scorecards, which show the profitability of customers. Applying these scorecards together makes the loan decision making process for banks more confusing. This paper has an obvious and clean solution for facilitating the confusion of loan decision making process by combining the credit and profit scorecards through introducing a matrix sequential hybrid credit scorecard. The applicability of the introduced matrix sequential hybrid scorecard results are shown using data from an Iranian bank.

Credit allocation through the usage of Portfolio optimization mainly seeks tomaximize return and minimize the risk of the portfolio; but there are other importantissues including sustainable development which is important for government/public sectors. This paper presents a novel credit allocation approach based on portfoliooptimization and investigates the effects of selected indicators of sustainabledevelopment on credit allocation. In order to evaluate this case study، constraintmean-variance was used as the extension of Markowitz portfolio theory. Selectedindicators were modeled as the mathematical model’s objectives and constraints. Inorder to show the applicability of the model، experimental results were given basedon credit allocation data for National Development Fund of Iran (NDFI). The resultsshow that sustainable development selected indicators exacerbate the return of NDFIportfolio from one side and from the other side، its effect on NDFI risk is somewhatsimilar but lighter.

Credit scoring is an important topic, and banks collect different data from their loan applicant to make an appropriate and correct decision. Rule bases are of more attention in credit decision making because of their ability to explicitly distinguish between good and bad applicants. The credit scoring datasets are usually imbalanced. This is mainly because the number of good applicants in a portfolio of loan is usually much higher than the number of loans that default. This paper use previous applied rule bases in credit scoring, including RIPPER, OneR, Decision table, PART and C4.5 to study the reliability and results of sampling on its own dataset. A real database of one of an Iranian export development bank is used and, imbalanced data issues are investigated by randomly Oversampling the minority class of defaulters, and three times under sampling of majority of non-defaulters class. The performance criterion chosen to measure the reliability of rule extractors is the area under the receiver operating characteristic curve (AUC), accuracy and number of rules. Friedman’s statistic is used to test for significance differences between techniques and datasets. The results from study show that PART is better and good and bad samples of data affect its results less.

This paper presents a comprehensive review of the works done, during the 2000–2012, in the application of data mining techniques in Credit scoring. Yet there isn’t any literature in the field of data mining applications in credit scoring. Using a novel research approach, this paper investigates academic and systematic literature review and includes all of the journals in the Science direct online journal database. The articles are categorized and classified into enterprise, individual and small and midsized (SME) companies credit scoring. Data mining techniques is also categorized to single classifier, Hybrid methods and Ensembles. Variable selection methods are also investigated separately because it’s a major issue in credit scoring problem. The findings of the review reveals that data mining techniques are mostly applied to individual credit score and there are a few researches on enterprise and SME credit scoring. Also ensemble methods, support vector machines and neural network methods are the most favorite techniques used recently. Hybrid methods are investigated in four categories and two of them which are “classification and classification” and “clustering and classification” combinations are used more. Paper analysis provides a guide to future researches and concludes with several suggestions for further studies.