نشریه تحقیقات مالی
پیاپی 31 (بهار و تابستان 1390)

Data with high frequency have a particular type of none stationary that is called fractional none stationary. This property causes the emergence of long-term memory in financial time series with high frequency. The existence of long-term memory in cement industry time-series is studied in this paper at first and its presence will be confirmed in a high confidence level by two tests R/S and GPH. Next, the accuracy of financial time-series forecast models such as ARMA and GARCH which dont consider the feature of long-term memory in time series modeling and models such as ARFIMA and FIGARCH that take this feature into account are compared with presented new meta heuristic that is composed of algorithm (harmony search) and weighted fuzzy time series by the way of rolling window and by the use of Root Mean Square Error criteria (RMSE) in different time intervals. The results show that the presented Meta heuristic method submits better result of common econometric models in all time intervals.

Liquidity is the most important aspect of stock markets development. This research investigates the effect of Share Issue Privatization (SIP) on Tehran Stock Exchange (TSE) liquidity. Regarding the three assessable measures in SIP, three hypotheses based on the number of privatized firms, the volume and the value of the sold stocks were defined and tested. In this research, the TSE liquidity is used as dependent variable which is calculated by the use of turnover ratio and Amihud measure. However, the Amihud measure is calculated based on the return of TEPIX and TEDPIX. So, the dependent variable was calculated and used in final modeling by applying the three general measures, including turnover ratio, Amihud measure based on TEPIX and Amihud measure based on TEDPIX. It is generally concluded that SIP through the TSE has a considerable and meaningful effect on development and liquidity of this market.

Because of its extensive applications in financial analysis, stock market volatility modeling is a significantly important issue for stock market practitioners and academicians. Using GARCH models to formulate the conditional variance heteroskedasticity and the taking advantages of panel data technique such as higher degrees of freedom, more flexibility in the control of the omitted or unobserved variables effects, lead to a significant increase in the accuracy of estimations. In this paper, in order to examine the similarities and differences between the conditional variance structures of stocks in inter and intra industries sectors, we use a general to specific methodology of nested tests pioneered by Hendry. Our dataset consist of panel samples of sectors’ daily share prices and stocks returns of specific firms in each industry, in the Tehran Stock Exchange over (21 June 2006) and (22 July 2006). Any similarities are not observed in neither inter nor intra volatility structures of stocks and returns of industries.

The aim of this paper is analyzing the role of psychology of numbers in financial markets and in this area, investigates the Price Clustering phenomenon in trading prices of Tehran Stock Exchange. Price clustering is one of anomalies that have been observed in financial markets that representative psychological biases or tendency to specific numbers in the market. This phenomenon includes of density of prices in specific numbers or rounding the prices. This research is done in the period of 77 to 89 (including three period of 77-80, 81-84 and 85-89), that survey substantial daily prices. The results of the paper confirm the existence of price clustering. The results show that in Tehran Stock Exchange, tendency to trading at round numbers is intensive and tendency to numbers of 5 and 9 is to some extent more than others. For other numbers this tendency is almost similar and there are uniform distributions for them. Also attraction hypothesis, as one of the explanations of price clustering is accepted. Furthermore, this research is rejected the resolution hypothesis for explaining the price clustering phenomenon in Tehran Stock Exchange. Results of creation and comparison of portfolios are show that there isnt a significant relation between price clustering and stock prices, size of firms, Liquidity of stocks and volatility of prices.

Having knowledge about behavioral biases, investors are able to evaluate their decision process with more awareness and they can react much better when facing such biases, as a result decisions deviation will be minimized. In this study, 36 different behavioral biases have been categorized into 5 different groups to normalize the effective behavioral biases in the process of decision making of the investors in Tehran Stock Exchange Market. These biases have been normalized and prioritized through using the Fuzzy Analytical Hierarchy Process. In prioritizing the five groups, “cognitive” “emotional”, “unusual phenomena”, “Heuristic behaviors”, and “Errors of preference” groups were in the first to fifth positions.

This study examines the empirical validity of claims that value stocks (stocks with high ratios of book value to price) have higher average returns than growth stocks (stocks with low book-to-market ratios). The analyses are performed using data pertaining to 70 firms for the period 1381-1389 and used the Panel Data methodology. This paper contains significant and consistent results. The results of testing hypotheses for each of the nine years and the pooled sample show that price-to-book ratio and Size are positively related to Stock Return. The results demonstrated that On average, growth stocks have higher Total Return than value stocks, and growth stocks (especially big growth stocks) also have higher average rates of capital gain. The results also demonstrated that the average returns growth portfolios tend to fall in the years after portfolio formation. Conversely, average returns value portfolios tend to rise in the years after portfolio formation, as some value stocks restructure, their profitability improves.

Because of the heterogeneity in behavior, in the real world prices may deviate substantially and persistently from their fundamental values. Of course, if these heterogeneous elements play a rather minor role then asset prices and rates of return will be determined mainly by economic fundamentals and rational behavior. By observing actual behavior in the stock market one can seek to isolate profitable trading opportunities which persist for some time. This evidence is referred to as stock market anomalies. In this paper, nonparametric bootstrapping procedure is used to analysis average monthly seasonality returns. Evidence suggests several explanations for abnormal returns during 2000: M3 to 2010:M2. Tax-loss Selling, Window dressing anomalies explain the existence of these distinct patterns of returns. Also, existence of those calendar seasonalities as the most important financial market anomalies is often promoted as a conflict with the efficient market hypothesis.