Mir Faiz Falah Shams, Shahryar Azizi,
Volume 8, Issue 4 (1-2009)
Abstract
For identifying factors effect Individuals Investment intention in Tehran Stock Exchange, we employed a 28-items questionnaire among 450 persons. Cronbachs alpha was equaled 0.871 and depicted a good reliability. Based on SEM methodology via using LISREL8.7, we found that proposed conceptual model and the entire hypothesis confirmed. Findings indicated that financial-accounting information (FAI) and general information (GNI) directly and indirectly via investor expectation (EXP) effect investing intention (INTENT). Investor need (NEED) directly effects investing intention. The order of total effects of factors effect Individuals Investment intention in Tehran Stock Exchange is (from high to low): GNI (0.5461), FAI (0.4702), EXP (0.31) and NEED (0.24).
Mirfieyz Falah Shams, Mahmood Mohammadi,
Volume 11, Issue 2 (8-2011)
Abstract
Price manipulation in the Tehran Stock Exchange has been one of the most widely discussed issues among academic and professional practitioners in recent years. In this article, we first calculated the abnormal Returns- significance difference between actual and risk-based adjusted expected returns- by using an autoregressive test, for all 130 accepted firms in the Tehran stock market during 2002-2006, which seemed to be manipulated, since they had experienced great fluctuations in their stock prices. For any firm, if changes in share prices are not at random and/or its stock prices are autocorrelated with the past ones, it can be concluded that the firm is under a price manipulation. In the next stage, we have developed a binary logit regression model for predicting the firms' price manipulation based on four factors i.e. the information transparency, the liquidity of the shares, the size (capital) of the firm and the P/E ratio. Finally, the model efficiency for predicting price manipulation in the Tehran Stock Exchange is validated by using appropriate statistical tests such as, The Wald, Likelihoods Function, and the Wilk's Lambda tests. The results showed that the model is efficient and robust for predicting the price manipulation (P<0.05, Wilk's Lambda=0.205; Cox & Snell R2=0.792 ,0.799; -2Log likelihood= 27.49).
