Stock price crash risk is a phenomenon in which stock prices are subject to severe negative and sudden adjustments. So far, different approaches have been proposed to model and predict  the  stock price crash risk, which in most cases have been the main emphasis on the factors affecting it, and often traditional methods have been used for prediction. On the other hand, using  Meta Heuristic Alg...

In the portfolio selection problem, the manager considers several objectives simultaneously such as the rate of return, the liquidity and the risk of portfolios. These objectives are conflicting and incommensurable. Moreover, the objectives can be imprecise. Generally, the portfolio manager seeks the best combination of the stocks that meets his investment objectives. The imprecise Goal Program...

oil-exporting economies largely dependent on oil revenues and oil income fluctuation are one of the most important factors that influence sectors of the economy specially the stock market. this paper investigate the relationship between oil markets and stock return volatility and transmission in a selection of opec countries, using a multivariate garch models (full-vech) over the period may 201...

Selecting the proper stock type for reforestation on dry sites can be critical for the long-term survival and growth of seedlings. In this study, we use a novel approach to understand stock type selection on a site where drought was induced with vegetative competition. Three ponderosa pine (Pinus ponderosa Lawson & C. Lawson var. ponderosa C. Lawson) seedling stock types were planted in the fie...

one of known methods for measuring, forecasting and managing risk is value at risk, which recently has been used by financial institutions extensively. value at risk (var) is a method for recognizing and evaluating risk and uses standard statistical techniques that have daily using in other contexts. this research is seeking a career for managing investment risk in stock exchange and selection ...

This paper presents a multi-stock discrete time market model. In this model, we consider an optimal solution in the multi-stock portfolio selection. Specially, the model allows that the optimal strategy to maximize an exponent function of the expected value is quasi myopic.

We propose a new computational method of input selection for stock market forecasting with neural networks. The method results from synthetically considering the special feature of input variables of neural networks and the special feature of stock market time series. We conduct the experiments to compare the prediction performance of the neural networks based on the different input variables b...

In this study, we utilize the genetic algorithm (GA) to select high quality stocks with investment value. Given the fundamental financial and price information of stocks trading, we attempt to use GA to identify stocks that are likely to outperform the market by having excess returns. To evaluate the efficiency of the GA for stock selection, the return of equally weighted portfolio formed by th...

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 ...

The stock market is a complex and dynamic system with noisy, non-stationary and chaotic data series. Prediction of a financial market is more challenging due to chaos and uncertainty of the system. Soft computing techniques are progressively gaining presence in the financial world. Compared to traditional techniques to predict the market direction, soft computing is gaining the advantage of acc...