Jamal Barzegari Khanagha; habib ansari samani; lida razzazzadeh
Abstract
Severe economic fluctuations and adverse consequences for investors. The need for predictive models has made corporate finance necessary. In this regard, This research is intended by examining the relationship between financial ratios and the content of the board's reports open a new way to predict company ...
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Severe economic fluctuations and adverse consequences for investors. The need for predictive models has made corporate finance necessary. In this regard, This research is intended by examining the relationship between financial ratios and the content of the board's reports open a new way to predict company status. For this purpose, were extracted frequency of words and phrases with positive and negative semantic load using the word extraction algorithm, From the text of the board's reports 219 non helpless companies and 81 helpless companies. Results of regression estimation positive and negative words ratio on financial performance indicators, shows in companies with financial health, negative words have a significant relationship with functional criteria. Evidence showed in these companies, managers are not trying to hide their financial crisis. The results for the helpless companies were different In general, there was no meaningful relationship between the words with the performance indicators of these companies. It looks like this group of companies by confusing the, they try to prevent to selling from their shares by stakeholders.
Mohammadreza Mehrabanpour; Malihe Habibzade
Abstract
The intense competition prevailing in the world today and investors should be more cautious about their decision given the prevailing conditions. But this information alone is not useful, so it is necessary to use data mining techniques to analyze and interpret data so that more informative information ...
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The intense competition prevailing in the world today and investors should be more cautious about their decision given the prevailing conditions. But this information alone is not useful, so it is necessary to use data mining techniques to analyze and interpret data so that more informative information will be available to users. Therefore, the purpose of this study is to cluster and forecast the profitability of companies. For this purpose, Tehran Stock Exchange companies were considered as the statistical population of the research and 888 companies in the period of 1387-1395 were selected as the research sample. So, in the beginning after the initial preprocessing of the data, with Matlab and Clementine software, using SSE criteria and K-Means method, the companies were converted to 3 clusters and the result of these clustering were measured by the standard quality measures. Finally, by using the C5 decision tree, cluster analysis and variables affecting profitability were identified; so that from the 32 considered variables only 8 includes: Gross profit to total assets, sales to total assets, profit to equity, operating profit to net sales, accrued profit and loss to equity, net profit to net sales, total liabilities to total assets and current assets to total assets affect the profitability of companies. At last, by taking these variables into account, prediction of each cluster was done, and the accuracy of the predictions sequence was 86,34%, 88,15% and 68.81%
S.M Shariat Panahi; J. Ebadi; M. Peimani
Volume 8, Issue 31 , October 2010, , Pages 101-119
Abstract
Maximizing of wealth or better say, end of period expected utility is the main goal of investors. But because of uncertainty of price changes, investors act in an unsafe environment and any risk reduction will redound to decreasing in expected return. Because of this, determining of the best measure ...
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Maximizing of wealth or better say, end of period expected utility is the main goal of investors. But because of uncertainty of price changes, investors act in an unsafe environment and any risk reduction will redound to decreasing in expected return. Because of this, determining of the best measure of risk is so important in finance.
There are many measures to quantify risk of investment. In this paper, we compare some of these measures of risk based on their ability to predict return in various time horizons. Therefore, four measure of risk (standard deviation, mean absolute deviation, semi standard deviation and value at risk) are selected from common and downside risk family and their abilities to forecasting return in one, two and three months periods are examined. Our analysis method is panel regression and results are conducted based on R-squared and nested regressions method. Our sample contains 66 Tehran Stock Exchange listed companies in time period of 1383 to 1387. Our results depict that semi standard deviation and value at risk have a better performance especially in one month prediction.
