maryam hashempour; reza raee; mohammadreza rostami
Volume 18, Issue 1 , May 2014, , Pages 83-100
Abstract
Appropriate methods for prediction of future trends in capital markets lead to a better decision making for market participants. Classic methods don not perform well in prediction of financial markets due to the nonlinear and chaotic nature of these markets. Moreover, information extracted from ...
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Appropriate methods for prediction of future trends in capital markets lead to a better decision making for market participants. Classic methods don not perform well in prediction of financial markets due to the nonlinear and chaotic nature of these markets. Moreover, information extracted from data disappear quickly, so these method are not workable in the long run.
The goal of this paper is using ant colony optimization algorithm for prediction of Tehran Stock Exchange's total return index (TEDPIX) data. First, we used the largest Lyapunov exponent to the consider chaotic nature of TEDPIX and then the ant colony optimization paradigm we employed to analyze topological structure of the attractor behind the given time series and to single out the typical sequences corresponding to the different parts of the attractor. The typical sequences were used to predict the time series values.
Eventually with respect to MSE , RMSE and MAE, ACO has lower error than GARCH and EGARCH models; however, Diebold Marino test shows that there is no difference if we use ACO or GARCH models for prediction; this represents that differences of error for different models in this article are very little. This article with detachment of typical sequences allows a structural method for prediction of chaotic data. So in prediction of data with many fluctuations and in long term, it can result to a better predictions. The algorithm of this paper is able to provide robust prognosis to the periods comparable with the horizon of prediction.
Keywords
amin rezaeemoghadam; mohammad rostami
Volume 17, Issue 3 , September 2013, , Pages 113-127
Abstract
In asset pricing and portfolio management the Fama-French three factor model is a model designed by Eugene Fama and Kenneth French to describe stock returns. The traditional asset pricing model, known formally as the Capital Asset Pricing Model, CAPM, uses only one variable, beta, to describe the returns ...
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In asset pricing and portfolio management the Fama-French three factor model is a model designed by Eugene Fama and Kenneth French to describe stock returns. The traditional asset pricing model, known formally as the Capital Asset Pricing Model, CAPM, uses only one variable, beta, to describe the returns of a portfolio or stock with the returns of the market as a whole. In contrast, the Fama–French model uses three variables. Fama and French started with the observation that two classes of stocks have tended to do better than the market as a whole: small caps and, stocks with a high book-to-market ratio, customarily called value stocks, contrasted with growth stocks. They then added two factors to CAPM to reflect a portfolio's exposure to these two classes Based on Fama and French about short term abnormal return in IPO's investors may fall in to traps by involving IPO's without considering fundamentals of stocks which would cause their loss, so this survey is conducted to study the liquidity and leverage effects beside Fama-French three factors on IPO's. this survey uses Amihood illiquidity measure and leverage ratio to explore the long run return (one year) considering (3 month) as short term. regression analysis showed among 5 major variables only market premium and size had significant relation with long run return.
azam jafaridargiri; mohammad rostami
Volume 17, Issue 1 , February 2013, , Pages 95-110
Abstract
Competitiveness and availability of transparent information are necessary in capital market. Information quality effects decision making and causes better resource allocation, which in turn leads to efficiency. Information transparency results in long term vision, reaching to new sources of capital, ...
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Competitiveness and availability of transparent information are necessary in capital market. Information quality effects decision making and causes better resource allocation, which in turn leads to efficiency. Information transparency results in long term vision, reaching to new sources of capital, lower cost of capital, accountable management and finally, increasing of the shareholders, wealth. This project investigated Benford’s Law as an indicator of data quality in Tehran Stock Exchange. The data used in this research included the first digit of daily close price and indexes .The study period was from the beginning of 1999 to the end of 2009.The top 25 percent of companies with regard to trading days were selected as a sample of the research. Kolmogorove-smirnov’s and Goodness of Fit tests were used to estimate the model.The results showed that the investigated data was inconsistent with Benford’s Law.
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Volume 15, Issue 3 , November 2011, , Pages 129-147
Abstract
The Assessment of Financial Distress in Tehran Stock Exchange: A Comparative Study
Between Data Envelopment Analysis
(DEA) and Logistic Regression (LR)
Mohammad Reza Rostami1, Mirfeyz Fallahshams2,
Farzaneh Eskandari3
1- Assistant Professor, Department of Management, Faculty of Social Sciences ...
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The Assessment of Financial Distress in Tehran Stock Exchange: A Comparative Study
Between Data Envelopment Analysis
(DEA) and Logistic Regression (LR)
Mohammad Reza Rostami1, Mirfeyz Fallahshams2,
Farzaneh Eskandari3
1- Assistant Professor, Department of Management, Faculty of Social Sciences & Economics, Alzahra University, Tehran, Iran
2- Associate Professor, Department of Management, Faculty of Social Sciences & Economics, Alzahra University, Tehran, Iran
3- Msc., Department of Management, Faculty of Social Sciences & Economics, Alzahra University, Tehran, Iran
Received: 5 /9/2010 Accept: 13/8/2011
Financial distress evaluation is important because firm failure imposes significant direct and indirect costs on a firm’s stakeholders. Hence, using financial ratios has been considered by bank loan officers, creditors, stockholders, financial analysts, and the general public in order to provide them with timely and accurate assessment.
Timely evaluation can help decision makers to find the optimal way and predict bankruptcy. There are different models for financial distress evaluation, which are mainly applied in decision making by financial market players. It has been attempted to improve the accuracy of these models by more developed techniques.
The main goal of this research is to examine the capability of the additive model of Data Envelopment Analysis (DEA) model in assessing corporate financial distress by comparing it with logistic regression (LR). The results showed that in within-sample evaluation, LR outperforms DEA (Additive model) in correctly identifying the default firms significantly.
