Crowdfunding is a new technology-enabled innovative process that is changing the capital market space. Internet-based applications, particularly those related to Web 2.0, have had a significant impact on sectors of society such as education, business, and medicine. The goal of this research is to fill a gap in the literature on mathematical modelling and prediction of ensemble learning in order to evaluate crowdfunding projects. The Mathematical model determines the cost of funding for the entrepreneur and the return investors will receive per period. A correct financial model is essential in order to keep all three stakeholders involved in the long term. The results show the designed model improved performance in predicting the evaluation of success or failure of Crowdfunding projects.
Ranking the Efficiency and Soundness of Business Banks Using a Combined Method of Data Envelopment Analysis and Fuzzy VIKOR
The present study provides a systematic method for assessing the efficiency and soundness of banks. The analysis is based on a set of benchmarks related to the financial performance of banks. In this regard, this research has explored a model for evaluating accepted banks in Tehran Stock Exchange using the data envelopment analysis method. One of the most important applications of this technique is measuring the efficiency of bank branches. In addition, paying special attention to the health of the banking system corresponds to the health of the society’s economics. It is essential to implement precise analysis benchmarks to evaluate banks and other financial institutions due to their importance in the economics. One of these benchmarks is efficiency. In this study, 18 Iranian business banks have been analysed using data envelopment analysis on the basis of financial ratios. Also, their efficiency has been computed using the output-based CCR envelopment model, assuming a constant efficiency-scale ratio. The results suggest that Gardeshgari bank has been selected as the most efficient and healthy bank between 2011 and 2015 on the basis of financial and non-financial criteria, and using the trapezoidal fuzzy number method.
The Effect of Macroeconomic Variables on Stock Portfolio Performance Based on Traditional and Modern Network
Evaluation of stock portfolio performance is considered one of the important issues in the capital market and investment management in stocks. Proper evaluation of portfolio performance requires recognizing the factors affecting it. The macroeconomic variables are important and effective factors due to affecting the systematic risk of companies. In this research, ordinary least squares method (OLS) was used to evaluate the effect of macroeconomic variables including inflation, interest rate, liquidity growth rate, oil price and currency rate (Rial versus Dollar) on the stock portfolio performance based on traditional and modern network theory. Performance of portfolios including growth portfolio, growth-value portfolio, and value portfolio, and offensive portfolio, indifferent and defensive portfolio was measured based on seasonal data from 2006 to 2016 using the Teriner Index. The research results show that at the error level of 5%, macroeconomic variables have an impact on the performance of both traditional and modern networks. However, the Akaike information criterion for the modern network model is equal to 5.822, which is less than the traditional network value with the value of 6.724. This suggests that the interpretation of macroeconomic variables in a modern network portfolio is better than that of traditional one. In addition, the effect of macro variables on the performance of the six portfolios will be different
The purpose of this paper is investigating the effect of JCPOA on the network behavior analysis of Tehran Stock Exchange indexes using the minimum spanning tree (MST) and hierarchical clustering. By simplifying a complex system, network analysis allows for the extraction of important and essential information from that system. In this paper, using network analysis the simultaneous behavior of 38 industry indexes in Tehran Stock Exchange in manufacturing, service and invest-ment sectors during 2012-2017 was investigated. These analysis included identi-fying the main indexes in the direction of moving other indexes using the MST, providing a classification using hierarchical clustering for the behavioral similarity of the indexes as well as examining the degree of integration (behavioral similarity) of market indexes over time. The results showed that investment, automobile, industry and medicine indexes in the research period had a major role in guiding other indexes and indexes can be classified into six groups in terms of behavioral similarity. The market has also been moving toward integration of indexes since early 2015 and beginning the executive steps of Joint Comprehensive Plan of Action (JCPOA). This reflects the investors' hope for the promotion of all indexes.
Identifying and Ranking the Factors Affecting Customer Financial Behavior Using Multi-Criteria Decision Making Technic (TOPSIS)
Customer relationship and recognition of the customer financial behaviour are considered as important strategic factors involved in successful performance of the banks. So, identifying these factors and prioritizing them help to make better decisions to complete the value chain of the banks’ financial services, especially in development banks that tend to have midterm and long-time presence in financial markets. So, the present research aims to identify and prioritize the mentioned factors in Bank of Industry and Mine as one of the main development banks of Iran. The present research has been performed in two phases. In the first stage, or qualitative part, In the first stage, during the review of the literature and the opinions of banking experts, the relevant indicators were identified. In the second stage, or quantitative part, the statistical data of variables in the period of 2012-2017 was collected. Then, the identified factors were prioritized by multi-criteria decision making in TOPSIS solver software. According to the findings, environmental factors are the main indicators affecting the bank customers’ financial behaviour. The most important customer-related factor was the definite profit paid to the customer; the most important bank-related factor was credit risk; and the most important environmental factor was economic growth.
Banks as one of the most important and crucial economic sectors in each country play a significant role in economic growth and development and they face various risks one of which is liquidity risk. Managing liquidity risk is of great importance and identifying its effective factors is more vital. The present study aims to pre-sent a dynamic model to manage liquidity risk. System dynamics is used to find a risk making structure and present the most effective solution to manage it. In this method, by providing a mathematical model, simu-lating the results of various scenarios is possible. The results of implement-ing four scenarios on the model were simulated and analyzed. The results revealed that reducing legal deposits and nonperforming loans and increasing attraction of deposits is influential in banks liquidity risk.
Developing a Measurement Model for the Sensitivity Analysis of Asset Returns with Regard to Beta Index of Exchange Rate in the Context of the Modified Capital Asset Pricing Model
With increasing trade among different countries The exchange rate fluctuations, consumption, inflation, and market portfolios are considered as major risk factors in financial markets. Hence this study aimed to examine the relationship between the exchange rate fluctuations and asset returns within a theoretical and empirical model, i.e. Consumption-based Capital Asset Pricing Model (CCAPM). To this end, a basic CCAPM was extended and imported consumables were included in Epstein and Zin’s recursive utility function. The research sample encompassed eight portfolios and monthly data from 2003 to 2014. The pricing model parameters were estimated using Euler's equations and Hansen and Singleton’s generalized method of moments (GMM). An estimation of the parameters of Euler's equations indicates the risk aversion and tolerance of economic factors, low elasticity of substitution for domestic consumables and imported consumables, and high elasticity of intertemporal substitution. In the next step, using Euler’s linearized equations as asset pricing model and Fama and Macbeth's two-step regression method, the effects of exchange rate risk premium, inflation, market efficiency, and consumption growth on return premium on assets were investigated. The results indicated the positive impact of the exchange rate risk premium, inflation, and market returns on the return premium on assets.
Factors Affecting Stock Prices Regarding Uncertainty and Asymmetric Information in Tehran Stock Exchange
Two main issues occurring in the economy and largely affecting stock prices are uncertainties and asymmetric information which are influenced by many factors, and along these factors, affect the stock prices. In this study, using Cox, Ingersoll & Ross (CIR) model, we tried to investigate the relationship between theoretical price and stock price under the conditions of uncertainty and asymmetric information. Then, using the GLS panel method, the stock price relationship with these variables and the factors related to the firm performance, economic factors and industrial factors were investigated in Tehran stock exchange. The results indicated that a large part of stock price changes can be explained by two variables of uncertainty and asymmetric information, while other factors had significant effects on stock prices. The difference was that the factors related to the firm's performance and industry index had a positive effect and the macro-economic factors had a negative effect on stock prices. Finally, according to CIR model, asymmetric information and uncertainty in market lead to delays in stock price adjustment, which can affect quality of corporate governance principles.
Financial Performance Evaluation of Companies Using Decision Trees Algorithm and Multi-Criteria Decision-Making Techniques with an Emphasis on Investor’s Risk-Taking Behavior
Evaluating the performance of companies using their financial ratios is a challenging task that is expected to become more straightforward by reducing the dimensionality of the data. The purpose of this study is to evaluate the performance of companies using a hybrid model for investment-related decision making through which the mean value of various financial ratios are calculated based on the investor's risk-taking behavior so that the number of all criteria is reduced to one single value for each alternative. To do so, a sample of 172 companies listed in Tehran Stock Ex-change was selected from 2008 to -2018. Firstly, the financial ratios were prioritized using decision trees regression analysis (type CART) and TOPSIS Technique. The results showed that Gross Profit Margin and Debt to Equity Ratio are the most and the least important factors, respectively. Then, using OWA (Ordered Weighted Averaging Aggregation) operator, the role of investor’s risk-taking behavior was investigated, and the results showed that investor’s risk-taking behavior changes the outcome of the decision-making process significantly.
This study examines whether the firms’ leverage adjustment speed is influenced by real and accrual-based earnings manipulation over the period 2006-2019. We find evidence suggesting that the leverage adjustment speed in firms with a higher level of real and accrual-based earnings manipulation is slower than that of other firms. Specifically, we show that under-levered (over-levered) firms with a higher level of earnings manipulation tend to adjust their actual leverage toward an optimal level, faster (slower) than that of other firms. These results are robust to different metrics for real and accrual-based earnings management, an alternative set of leverage determinants, alternative sample periods, and various estimation methods.
The Role of Earnings Management in Theoretical Development and Improving the Efficiency of Accounting-Based Financial Distress Prediction Models
Examining the theoretical foundations of earnings management shows that companies have stronger incentive to use earnings management at the pre-bankruptcy stage. Consequently, accounting-based determinants retrieved from financial statements may be biased factors for financial distress. In this paper, we investigate whether taking into account real earnings management improves specification of accounting-based financial distress prediction models. We test whether the inclusion of such attributes in bankruptcy prediction models improves their predictive ability. We use a sample of listed manufacturing companies in the Iran Stock Exchange during 2008 - 2017. Our findings suggest that the inclusion of earnings management significantly increases the predictive ability of accounting-based financial distress prediction models. Our results show that the real earnings management can provide predictive signals concerning a financial distress and that an abnormal cash flow which proxies for real earnings management can play a relevant role in early warnings of financial distress. These results are of interest to market participants, auditors, regulating authorities, banks and other financial institutions that are interested in financial distress assessment
Application of Resource-Based View Theory in Assessing of Efficiency of Companies Accepted in Tehran Stock Exchange by Data Envelopment Analysis
Resource-based view (RBV) theory analyzes and interprets company's resources in order to find out how organizations gain a competitive advantage. This theory fo-cuses on the implications of the complicated features of a company as the resources for excellent performance and comparative advantage. According to this view, capa-bilities and resources of the company are the main factors in explaining the func-tional results or competitive advantage. Resources can be considered as inputs which enable companies to do their activities. The purpose of this research is to introduce a resource-based theory for calculating the efficiency score of accepted companies in Tehran Securities Exchange by using Data Envelopment Analysis (DEA). In this regard, the financial statements of 190 companies accepted in the exchange for the 2009 – 2018 period have been analysed. Efficiency indicators, which include 4 categories of resources (10 inputs) and 5 outputs, have formed the axis of the mentioned technique. The results of implementing this model for compa-nies with the efficiency score of one, indicates first a minimal input consumption compared to competing companies in the same industry while producing more out-put, and second, using the resource-based view theory (integration of tangible and intangible resources) enables the company to push the boundaries of efficiency. Finally, it can be said that utilizing minimum and maximum resources simultaneous-ly leads to a focus strategy-type competitive advantage.
Oil Price estimating Under Dynamic Economic Models Using Markov Chain Monte Carlo Simulation Approach
This study, attempts to estimate and compare four different models of jump-diffusion class combined with stochastic volatility that are based on stochastic differential equations, and their parameters latent variables are estimated by Markov chain Monte Carlo (MCMC) methods. In the Stochastic Volatility with Correlated Jumps (SVCJ) model, volatilities are scholastic, and the term jump is added to both scholastic prices and volatilities. The results of this study showed that this model is more efficient than the others are, as it provides a significantly better fit to the data, and therefore, corrects the shortcomings of the previous models and that it is closer to the actual market prices. Therefore, our estimating model under the Monte Carlo simulation allows an analysis on oil prices during certain times in the periods of tension and shock in the oil market
Successful trades in financial markets have to be conducted close to the key recurrent points. Researchers have recently developed diverse systems to help the identification of these points. Technical analysis is one of the most valid and all-purpose kinds of these systems. With its numerous rules, the technical analysis endeavors to create well-timed and correct signals so that these points are identified. However, one of the drawbacks of this system is its overdependence on human analysis and knowledge in selecting and applying these rules. Employing the three tools of genetic algorithm, fuzzy logic, and neural network, this study attempts to develop an intelligent trading system based on the recognized rules of the technical analysis. Indeed, the genetic algorithm will assist with the optimization of technical rules owing to computing complexities. The fuzzy inference will also help the recognition of the total current condition in the market. It is because a set of rules will be selected based on the market kind (trending or non-trending). Finally, the signal developed by every rule will be translated into a single result (buy, sell, or hold). The obtained results reveal that there is a statistically meaningful difference between a stock's buy and hold and the trading system proposed by this research. In other words, our proposed system displays an extremely higher profitability potential.