This paper discusses the application of support vector machine (SVM) in stock price change trend forecasting. By reviewing prior research, thirteen technical indicators are defined as the input attributes of SVM. By training this model, we can forecast if the stock price would rise the next day. In order to make best use of market information, analyst recommendations about upgrading stocks are ...

In many empirical studies, the dynamic relationship among energy sector variables (such as, oil, electricity, gasoline, coal, renewable energy, etc.) and economic variables (such as; financial markets, real economy and the overall economy) are studied. Oil price changes may affect the economic variables more of oil importer countries then oil exporter countries especially emerging markets. In a...

this study examined the relationship between market monitoring cost consist the business disclosure index, the index of perception of corruption, rule of law and regulations quality, and also pointed out the technology index and exercise price limit rules that are in stock. in this research the relationship between the above indicators to price limit rules applied during the period 2005-2010 in...

Currently, stock price forecasting is carried out using either time series prediction methods or trend classifiers. The trend classifiers are designed to predict the behaviour of stock price’s movement. Recently, soft computing methods, like support vector machines, have shown promising results in the realization of this particular problem. In this paper, we apply several prototype generation c...

S tock returns of companies listed on the stock exchange is one of the most important criteria in assessing the macroeconomic. This study investigates the effect of exchange rate Volatility on the stock exchange Returns of D8 countries. It takes monthly data during the period (2008:1-2015:6) constituting 90 observations. At first we used Panel-GARCH model to estimate Exchange Rate Vo...

This paper surveys recent literature in the area of Neural Network, Data Mining, Hidden Markov Model and Neuro-Fuzzy system used to predict the stock market fluctuation. Neural Networks and Neuro-Fuzzy systems are identified to be the leading machine learning techniques in stock market index prediction area. The Traditional techniques are not cover all the possible relation of the stock price f...