In this paper, we showed a method to forecast the daily stock price using neural networks and the result of the Neural Network forecast is compared with the Statistical forecasting result. Stock price prediction is one of the emerging field in neural network forecasting area. This paper also presents the Neural Networks ability to forecast the daily Stock Market Prices. Stock market prediction ...

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 ...

a r t i c l e i n f o Keywords: Subtractive clustering Adaptive network-based fuzzy inference system Technical indicators Adaptive learning Genetic algorithm Technical analysis is one of the useful forecasting methods to predict the future stock prices. For professional stock analysts and fund managers, how to select necessary technical indicators to forecast stock trends is important. Traditio...

The past few years have witnessed a growing rate of attraction in adoption of Artificial Intelligence (AI) techniques and combining them to improve forecasting accuracy in different fields. Besides, stock market forecasting has always been a subject of interest for most investors and professional analysts. Stock market forecasting is a tough problem because of the uncertainties involved in the ...

Within the stock markets, the trading volumes and the asset prices are considered to be highly changeable and unpredictable. However, effective forecasting of the way how they will change guarantees constructive advice for financial practitioners. There are various factors that may have an impact on the movements, one of which is the financial information. On the other hand, financial volatilit...

The purpose of this study is to optimize the stock price forecasting model with meta-innovation method in pharmaceutical companies.In this research, stock portfolio optimization has been done in two separate phases.The first phase is related to forecasting stock futures based on past stock information, which is forecasting the stock price using artificial neural network.The neural network used ...

This thesis explores derived parameter optimization technique to optimize the performance of forecasting models. This study presents artificial neural network (ANN) based computational approach for predicting the stock market trend of companies from five different sectors such as:IT Sector (Infosys), Banking Sector (SBI), Consumer Goods Sector (Tata Motors), Industrial Goods Sector (BHEL) and B...

An important financial subject that has attracted researchers' attention for many years is forecasting stock return. Many researchers have contributed in this area of chaotic forecast in their ways. Among them data mining techniques have been successfully shown to generate high forecasting accuracy of stock price movement. Nowadays, instead of a single aspects of stock market, traders need...