Mohammad Rasool Chopani; Farzaneh Nassirzadeh; Mahdi saehi
Abstract
Earnings per share is one of the most important financial statistics, mostly used in the evaluation of profitability, the risk associated with earning, and the stock price. In many countries, the importance of this measure is to the extent that it is considered as one of the principal scales in determining ...
Read More
Earnings per share is one of the most important financial statistics, mostly used in the evaluation of profitability, the risk associated with earning, and the stock price. In many countries, the importance of this measure is to the extent that it is considered as one of the principal scales in determining the stock price and is widely used in stock evaluation models. Thus, to predict the earnings per share, different algorithms have been used, some of which make use of the statistical models and some smart models.
The studies recently conducted on the precision of smart models show that in comparison to the statistical models, the smart models have performed better in classification and finding of an efficient solution. Thus, those investors using these models will find the investment opportunities much more efficiently. Therefore, regarding this necessity, this study has compared the errors of such models as Support Vector Estimator, Minimum Degree Estimator, and Fuzzy Neural Network (which are among the smart models having the lowest error rates) in predicting the earnings per share for the firms enlisted in Tehran Stock Exchange during the years of 2005 to 2012.
Research Methodology
In this research, nineteen different independent variables have been used in the three financial, fundamental, and macro groups. The relationships between the first group of variables and the earnings per share in the paper by Zhang et al. (2004), and the relationship between the second group of variables and the earnings per share in the papers by Lexian et al. (1390, 2011) and Brid (2001) have been confirmed. The dependent variable in this research is the annual earnings per share. Therefore, the current research tries to find a model, which has the highest precision in predicting the earnings per share, using the independent variables, either individually or in groups.
The selected sample in this research include 171 firms in 27 active industries, during the years of 2005 to2012, through random sampling and using cluster sampling from among the active firms in Tehran Stock Exchange.
After being collected and standardized, the data was classified into training and experimental data, using the K-Fold Cross-Validation method. The percentage of training data to the experimental data is assumed as 30-70 or 20-80; in this research, the 20-80 composition has been employed. In this research, the amount of K has been determined as 10.
Then, the main process of modeling is conducted in a way that the prevalent patterns and relations between the data (independent and dependent variables) are extracted, using the techniques of Support Vector Estimator, Minimum Degree Estimator, and Fuzzy Neural Network. In this stage, the training data are used for modeling. After extracting the data patterns, the precision of the proposed model is estimated, using the experimental data, and finally, to explore the models’ precision, such error measures as mean square error (MSE), Median Absolute Deviation (MAD), and determination coefficient have been used.
Research Findings:
The results show that when all the fundamental and financial variables are used simultaneously, the precision of the estimator model is highest. When the fundamental variables are used in LARS, the MSE and MAD are 3.505 and 306.301 respectively. When the financial variables are used in LARS, the MSE and MAD are 0.921 and 206.669 respectively, and finally, when all the variables are used in LARS, the MSE and MAD are 3.414 and 392.081 respectively. The obtained results have been presented in sum in the following table.
Conclusion
In this research, the models have been evaluated annually; the models have been conducted on each year, and the results have been compared with each other. Finally, the average annual errors have been considered as the basis of determining a more precise model in every state. Exploring the models’ final error the Minimum Degree Estimator model predict the earnings better than the Fuzzy Neural Network and Support Vector Estimator. Also, exploring the average errors for the Minimum Degree Estimator model shows that using the financial variables has resulted in the increase in the predicting capabilities of this model.
Keyword: Tehran stock exchange, Earning per share, Support vector regression, Least angel regression, Adaptive neuro Fuzzy Inference system.
Farzaneh Nassirzadeh; Amin Rostami
Abstract
Appraising the profitability of the companies is very important in the decisions of
financial information users. In this study, the relationship between modern liquidity
indexes and cash flow ratios with profitability (financial and market-based
measures) are examined.
This study includes 108 companies ...
Read More
Appraising the profitability of the companies is very important in the decisions of
financial information users. In this study, the relationship between modern liquidity
indexes and cash flow ratios with profitability (financial and market-based
measures) are examined.
This study includes 108 companies of Tehran Stock Exchange during ٢٠٠١-٢٠٠٩.
The statistical method used to test the hypothesis is panel data approach; and three
models (based on the dependent variables) are estimated for each hypothesis. The
results show that modern liquidity indexes and cash flow ratios have correlation with
together but they have different information contents and they can not be substitutes
for each other and presenting both of ratios together will provide better results. In
addition the results from testing hypothesis show that modern liquidity indexes and
cash flow ratios (except the CFOTCL) have significant association with all
profitability measures (Financial and market-based measures) and can present a
good picture of profitability and firms return.
Farzaneh Nassirzadeh; Farhad Karimipour
Abstract
This study seek to examine performance of Feltham - Ohlsen (1995)valuation model at the Tehran Stock Exchange and is trying to identify relation between intrinsic company value and return. This model is trying to determine the intrinsic value of company using their book value. Statistical community ...
Read More
This study seek to examine performance of Feltham - Ohlsen (1995)valuation model at the Tehran Stock Exchange and is trying to identify relation between intrinsic company value and return. This model is trying to determine the intrinsic value of company using their book value. Statistical community of this study includes all TSE accepted companies and Statistical sample is including 37 TSE accepted companies from 3 different industry (Food and beverage industry other than sugar, Food industry and pharmaceutical products, Automotive Industry) and data for these firms are analyzed interval 1377 to 1386 using Time series-Cross sectional (panel data) regression model. Results show that the model above boldly dose’nt able to determine the intrinsic value of companies. But evidence shows that the companies that have more intrinsic value, earn more profits . According to the results of performance of this model can not completely rejected, but now using of this model in Tehran Stock Exchange is not recommended.
farzaneh nasirzadeh; Danial Bihoodizadeh
Abstract
برق به عنوان صنعت زیربنایی در فرایند توسعه اقتصادی کشور، نقشی ارزنده و اساسی دارد که بستر لازم را برای پویایی کشور در زمینههای مختلف فراهم میسازد. تنوع بخشی به منابع ...
Read More
برق به عنوان صنعت زیربنایی در فرایند توسعه اقتصادی کشور، نقشی ارزنده و اساسی دارد که بستر لازم را برای پویایی کشور در زمینههای مختلف فراهم میسازد. تنوع بخشی به منابع تولید برق نیز از برنامههای اولویت دار این صنعت است. از اینرو تحقیق حاضر درصدد تعیین بهای تمام شده تولید برق در واحدهای گازی و سیکل ترکیبی در نیروگاه شریعتی مشهد است. برای این منظور اطلاعات لازم از طریق مطالعه کتابخانه ای و بررسی اسناد و مدارک موجود در نیروگاه شریعتی، شرکت برق منطقه ای، شرکت مدیریت نیروگاه های گازی و دفتر فنی برق خراسان برای سالهای 84-1382 جمع آوری گردید. پس از بررسیهای به عمل آمده، هزینه های این نیروگاه در شش گروه طبقه بندی گردید که شامل هزینه های سوخت، استهلاک تاسیسات تولید، تعمیرات، واحد پشتیبانی فنی، بهره داری و متفرقه است. سپس از طریق مصاحبه و استفاده از نظر کارشناسان، تحقیقات کتابخانه ای، استفاده از اطلاعات مالی و غیرمالی نیروگاه، روش مناسب جهت محاسبه بهای تمام شده تولید برق در هر یک از دو ساختار تولیدی فوق الذکر تعیین گردید. نتایج حاصله موید این مطلب است که طی سالهای مورد بررسی، بهای تمام شده برق تولیدی در واحدهای گازی نسبت به بلوک سیکل ترکیبی بیشتر بوده است.
