The purpose of this research is to predict stock returns and the purpose of the least squares regression and neural network approach is used. The potential financial ratios based on the historical cost and financial ratios based on Adjusted Cost predict stock returns are investigated. Independent variables and the dependent variable in this study and financial ratios and stock returns is for th...

Options on stocks are priced using information on index options and viewing stocks in a factor model as indirectly holding index risk. The method is particularly suited to developing quotations on stock options when these markets are relatively illiquid and one has a liquid index options market to judge the index risk. The pricing strategy is illustrated on IBM and Sony options viewed as holdin...

This study presents an integrated system where wavelet transforms and recurrent neural network (RNN) based on artificial bee colony (abc) algorithm (called ABC-RNN) are combined for stock price forecasting. The system comprises three stages. First, the wavelet transform using the Harr wavelet is applied to decompose the stock price time series and thus eliminate noise. Second, the RNN, which ha...

us 2008 financial crisis, spreaded out across the international stock markets in the second half of the year 2008. the crisis decreased the returns and increased the volatilities of almost all of the stock indices, however, its effects on iran’s stock markets were unexplored. financial limitations create an exclusive situation in iran’s stock market. using weekly data on the stock indices from ...

This study examines the dynamic relationships between stock market performance and the interest rates in Sri Lanka during June 2004 to April 2011. We use all share price index in the Colombo stock exchange as a measure of stock market performance indicator and Sri Lanka interbank offer rate as a measure of interest rate. We employ some conventional time series econometric techniques namely Unit...

Focuses on several key industries like Textile, Plastics, and Electronics, this study applies the Vector Autoregressive Moving Average (VARMA) Model on the industrial sales index and industrial stock prices index to observe the dynamic relationship between sales and stock prices. Our empirical result has shown a consistent pattern of relationship between sales and stock prices among all industr...

Artificial Neural Network (ANN) has preeminent learning ability, but often exhibit inconsistent and unpredictable performance for noisy data. In addition, it may not be possible to train ANN or the training task cannot be effectively carried out without data reduction when the amount of data is so large. In this paper, we have used a Neuro-genetic model to predict the index value for Stock Pric...

Data Mining (DM) is a relatively recent technology that is employed in inferring useful knowledge that can be put to use from a vast amount of data. This paper presents the data mining processes applied to the seemingly chaotic behavior of stock markets which could be well represented using the enhance GMDH, and we compared its results with published results using neural network, TS fuzzy syste...

the goal of this research was prioritize effective financial factors on price stock in tehran stock exchange with using topsis method. based on, it was reviewed literature and interviews and specially questionnaire obtain, effective financial factors to analysis with emphasize on topsis technique. the results shown in the entire ratio price to income, historical event share, eps and return on a...

in this paper, we have estimated the memory of thetehran stock exchange indices. the estimation of fractional differencing parameter is carried out by various methods such as mle, nls, hurst exponent, gph, lo, whittle and wavelet. the estimation results of whittle, wavelet, hurst, and lo methods allow us to conclude that the returns on stock indices (tepix, tedpix, tedix, financial index and in...