List of Articles Iman Dadashi

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  1 - Presenting a New Bankruptcy Prediction Model Based on Adjusted Financial Ratios According to the General Price Index
  Naimeh Jebelli Iman Dadashi Mohammad Javad Zare Bahnamiri
  10.22034/amfa.2020.1884838.1339
  In a volatile economic environment, financial decision making is always associated with risk. Bankruptcy, as one of the most important risks, has a significant impact on the interests of the firm's stakeholders, so presenting appropriate bankruptcy forecasting patterns More

  In a volatile economic environment, financial decision making is always associated with risk. Bankruptcy, as one of the most important risks, has a significant impact on the interests of the firm's stakeholders, so presenting appropriate bankruptcy forecasting patterns is of the utmost importance. In this study, after reviewing the theoretical literature and selecting the financial ratios used in previous bankruptcy prediction models as the variable input of the initial model, the financial ratios were adjusted based on the price index and then, using the LARS algorithm, the ratios that have the highest ability to differentiate between bankrupt and non-bankrupt firms were identified, and finally, using the SVM and Naive Bayesian algorithms, the final bankruptcy prediction model was developed. For this purpose, the data of 50 companies listed in Tehran Stock Exchange who had experienced bankruptcy for at least one year from 2008 to 2018 under Article 141 of the Commercial Code were used. The results show that the adjusted financial ratios based on the price index in the model presented by SVM algorithm can be a very good predictor for bankruptcy of companies with an accuracy of 99.4%. Manuscript profile
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  2 - Stock price analysis using machine learning method(Non-sensory-parametric backup regression algorithm in linear and nonlinear mode)
  Aliasgar Davoodi Kasbi Iman Dadashi Kaveh Azinfar
  10.22034/amfa.2019.1869838.1232
  The most common starting point for investors when buying a stock is to look at the trend of price changes. In recent years, different models have been used to predict stock prices by researchers, and since artificial intelligence techniques, including neural networks, g More

  The most common starting point for investors when buying a stock is to look at the trend of price changes. In recent years, different models have been used to predict stock prices by researchers, and since artificial intelligence techniques, including neural networks, genetic algorithms and fuzzy logic, have achieved successful re-sults in solving complex problems; in this regard, more exploitation Are. In this research, the prediction of stock prices of companies accepted in the Tehran Stock Exchange using artificial intelligence algorithm (non-sensory-parametric support vector regression algorithm in linear and nonlinear mode) has been investigated. The results of the research show that the PINSVR algorithm in nonlinear mode has been able to predict the stock price over the years, rather than linear mode. Manuscript profile
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  3 - Stock price prediction using the Chaid rule-based algorithm and particle swarm optimization (pso)
  Aliasghar Davoodi Kasbi Iman Dadashi
  10.22034/amfa.2019.585043.1184
  Stock prices in each industry are one of the major issues in the stock market. Given the increasing number of shareholders in the stock market and their attention to the price of different stocks in transactions, the prediction of the stock price trend has become signif More

  Stock prices in each industry are one of the major issues in the stock market. Given the increasing number of shareholders in the stock market and their attention to the price of different stocks in transactions, the prediction of the stock price trend has become significant. Many people use the share price movement process when com-paring different stocks while investing, and also want to predict this trend to see if the trend continues to increase or decrease over time. In this research, stock price prediction for 1170 years -company during 2011-2016 (a six-year period) of listed companies in stock exchange has been studied using the machine learning method (Chaid rule-based algorithm and Particle Swarm Optimization Algorithm). The results of the research show that there is a significant relationship between earnings per share, e / p ratio, company size, inventory turnover ratio, and stock returns with stock prices. Also, particle swarm optimization (pso) algorithm has a good ability to predict stock prices. Manuscript profile
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  4 - Investigating the Asymmetric Models of Cash Holding Adjustment Speed: Dummy Variable, Quadratic and Threshold Regression Models
  Milad Emamgholipour Archi Seyed Ali Nabavi Chashmi Iman Dadashi Maryam Shafiee Kakhki
  10.22034/amfa.2022.1947856.1670
  Proving helpful in an efficient management of cash in order to reach optimal cash and clearly explain relevant optimization policies, this study examined the adjustment speed of cash holding using asymmetric models. The sample consisted of 117 firms listed in Tehran Sto More

  Proving helpful in an efficient management of cash in order to reach optimal cash and clearly explain relevant optimization policies, this study examined the adjustment speed of cash holding using asymmetric models. The sample consisted of 117 firms listed in Tehran Stock Exchange and their financial details over the 2009-2018 period. Once the optimal level for cash holding was identified, asymmetric models such as the dummy variable approach, the quadratic model, and the threshold regression model were employed to test the adjustment speed of cash holding. The results revealed that cash-rich firms are moving toward optimal cash at a greater speed than cash-poor firms. In addition, the results from the quadratic model showed a non-linear, skewing effect of the cash holding adjustment speed in terms of the different cash levels. Therefore, there is an optimal level of cash holding that enables firms to deviate from the cash target. Should firms fall outside the optimal cash range, cash adjustment will occur at a greater speed, and it will be both partial and asymmetric. Manuscript profile