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classification algorithms

در نشریات گروه حسابداری
تکرار جستجوی کلیدواژه classification algorithms در نشریات گروه علوم انسانی
تکرار جستجوی کلیدواژه classification algorithms در مقالات مجلات علمی
  • KAMEL Ebrahimian, Ebrahim Abbasi *, Akbar Alam Tabriz, Amir Mohammadzadeh
    This study predicts Price of stocks in the short term by using the analysis of investors' opinions of the social network. The predictability of stock markets, due to having a complex, dynamic and nonlinear system that it has always been one of the challenges for researchers. The effect of users' feelings on the social network and its combination with 20 technical indicators on the accuracy of stock price forecasting. The study period is from the beginning of April 2016 to the end of March 2017 (two years). To access sufficient data, a sample of 14 active companies that had the most comments and posts. Data mining of technical indicators was performed and support vector regression was used to predict. The results show that the use of technical indicators is more accurate compared to combining it with the aggregation of users' emotions and has less RMSE errors. The number of comments has a significant correlation and the results of Granger causality test showed that it is possible to use the aggregation of users' daily emotions to predict stock prices.
    Keywords: Technical Analysis, classification algorithms, support vector regression, Text mining
  • Mahmood Mohammadi, Shohreh Yazdani *, Mohammad Hamed Khanmohammadi, Keyhan Maham
    In the last decade, high profile financial frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement fraud. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements between the years 2011 and 2016. The results show, that artificial neural network perform well relative to a Bayesian network, Discriminant Analysis, logistic regression and Support vector machine. The results also reveal some diversity in predictors used across the classification algorithms. Out of 19 predictors examined, only nine are consistently selected and used by different classification algorithms: Employee Productivity, Accounts Receivable to Sales, Debt-to-Equity, Inventory to Sales, Sales to Total Assets, Return On Equity, Return on Sales, Liabilities to Interest Expenses, and Assets to Liabilities. These findings extend financial statement fraud research and can be used by practitioners and regulators to improve fraud risk models.
    Keywords: financial reporting fraud, Fraud detection, fraud predictors, classification algorithms
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