جستجوی مقالات مرتبط با کلیدواژه "stocks price" در نشریات گروه "مالی"

This paper investigates the behavior of the differences between bid and ask prices and spread and the factors affecting them. The bid and ask price differences are one of the criteria for assessing the liquidity risk of stocks and the selection of stocks in the investment portfolio. Therefore, this study seeks to provide a solution to reduce the bid-ask price differences in an investment portfolio, by estimating the effect of companies' financial variables on the amount of the bid and ask price differences in the form of the linear Panel Data model. Accordingly, among the companies listed on the Tehran Stock Exchange, 129 companies have been selected for the period 1386-1396. The findings show that the Average Daily Stock Turnover and Liquidity Rating of the company, respectively, had the most significant effect on the bid and ask price differences of stocks. Also, based on our results, the first hypothesis, that stock price volatility has a positive and significant effect on the dependent variable BAS, is confirmed. On the other hand, coefficients of stock percentage in stock block holders, total company shares, and financial leverage in the linear model are not statistically significant, and asset liquidity rating does not have the greatest impact on the BAS dependent variable. So, the second hypothesis is not accepted. Also, the effect of the variable of company size (volume of company's assets) was not statistically significant and was considered equal to zero. Therefore, the third hypothesis was accepted.

In This Study We Compare the Efficiency of Both Artificial Neural Network Prediction Methods (ANN) and Traditional Method of Auto Regressive Integrated Moving Average (ARIMA) in Predicting Stock Prices in Iranian Stock Market. For This Purpose, Four Pharmaceutical Companies, Alborz Drug, Iran Drug, Pars Drug, and Jam Drug Were Selected and ARIMA Model and Artificial Neural Network Model Were Estimated For All Four Companies. In Order to Estimate Artificial Neural Network Model, Stock Price Variable as Dependent Variable and Stock Trading Volume, Drug Industry Index, OPEC Oil Price, Exchange Rate and Gold Price are Considered as Independent Variables. MSE, RMSE, MAD, R2 and MAPE Criteria Were Used to Compare Two Models. In Order to Estimate the Stock Price Forecast Regression Model, Use of Auto Regressive Integrated Moving Average (ARIMA) Regression Is Used and Estimation of the Coefficients of the Model is Performed Using the EVIEWS Statistical Software. An Suitable ANN Model Was Created For Predicting Stock Prices Using MATLAB Software. The Results of the Research Showed That the Research Hypothesis is Correct and the Artificial Neural Network Model (ANN) Has a Better Predictor of Stock Price in the Iranian Stock Market Than the ARIMA Method.