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 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

Abstract Classic asset pricing models assume that distribution of information is symmetric and suppose similar trade-off between risk and return among investors. In terms of information asymmetry that some traders have private information, investors will be faced with information risk; this is the risk of trading with informed traders. Easley et al. (2002) introduced probability of information based trade (PIN) as information risk measurement and developed a microstructure model for estimating PIN. This paper examines information risk pricing in Tehran Stock Exchange to see whether PIN can explain stock return in TSE. In the other hand we investigate relationship between firm size and probability of information based trade (PIN). Our results show that probability of information based trade (indicator of information risk) can explain stock return. A10 percent increase in PIN leads to 2.8 percent increase in stock return. Also negative relationship found between firm size and probability of information based trade (PIN).

Abstract Mean-Semivariance Portfolio Optimization Using Harmony Search Method Reza Raei1, Shapoor Mohammadi2, Hedayat Alibeiki3 1- Associate Professor, Department of Management, Faculty of Management, University of Tehran, Tehran, Iran 2- Assistant Professor, Department of Management, Faculty of Management, University of Tehran, Tehran, Iran 3- M.S. student, Department of Management, Faculty of Management, University of Tehran, Tehran, Iran Received: 29 /11/2010 Accept: 9/3/2011 Academics and practitioners usually optimize portfolios using the mean-variance approach than the mean-semivariance approach. Due to the fact that semivariance is often considered a more plausible measure of risk than variance, in this paper, semivariance was measured as the main indicator of risk. The portfolio optimization problem is a mixed quadratic and integer programming problem for which efficient algorithms do not exist. This study presents a heuristic approach to portfolio optimization problem using Harmony Search Algorithm (HS). The HS method is inspired by the underlying principles of the musicians’ improvisation of the harmony. The test data set is the daily prices of 20 companies from March 2006 to September 2008 from the TEPIX in Iran. The results showed that Harmony search approach is successful in constrained portfolio optimization to find the optimum solutions at all levels of risk and return.

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