in this paper we examine the effect of the oil volatility, consumer price index (cpi) and industrial production on the stock market return in tehran stock exchange (tse). we used seasonal data in period 1378-1390 and auto regressive distributed method (ardl) for the short-term and long-term relationship between the variables. as results of research indicate, we find that there is positive short...

AbstractThe Islamic stock index is a composite of stocks listed on the Indonesia Stock Exchange (IDX). Therefore, expected to have low volatility and be resistant possible financial crisis. This study aims see whether lower than conventional during Covid-19 The data taken daily closing for JKSE JII period March 1, 2020, April 30, 2022, with total 532 observations. model used ARMA/ ARIMA which t...

Objective: There is a large theoretical literature regarding stock market manipulation. However, empirical evidence of manipulation remains scare especially in emerging markets like Iran. So, it is vital to detect and prevent. Manipulation distorts prices, thereby reducing market efficiency and harms public confidence. Distorted prices increase market volatility and risk. This study empirically...

The jamor purpose of the present research is to predict the total stock market index of Tehran Stock Exchange, using a combined method of Wavelet transforms, Fuzzy genetics, and neural network in order to predict the active participations of finance market as well as macro decision makers.To do so, first the prediction was made by neural network, then a series of price index was decomposed by w...

The use of intelligent systems for stock market predictions has been widely established. This chapter introduces two Genetic Programming (GP) techniques: Multi-Expression Programming (MEP) and Linear Genetic Programming (LGP) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm and Takagi-Sugeno n...

Stock market price index prediction is regarded as a challenging task of the financial time series prediction process. Support vector regression (SVR) has successfully solved prediction problems in many domains, including the stock market. This paper hybridizes SVR with the self-organizing feature map (SOFM) technique and a filter-based feature selection to reduce the cost of training time and ...

this paper examines the causal relationship between stock prices and macroeconomic aggregates in iran, by applying the techniques of the long–run granger non–causality test proposed by toda and yamamoto (1995). we test the causal relationships between the tepix index and the three macroeconomic variables: money supply, value of trade balance, and industrial production using quarterly data for t...