Forecasting the short-term trend of a stock market has long been a big challenging task. Parameters of stock markets, including open/close prices, daily-high/low prices and trading volumes, were frequently used in previous studies to forecast the stock market. Basing on the fact that the moving direction of these parameters have certain inertia within short-term period, we here explored the pot...

This paper presents a novel trend-based segmentation method TBSM and the support vector regression SVR for financial time series forecasting. The model is named as TBSM-SVR. Over the last decade, SVR has been a popular forecasting model for nonlinear time series problem. The general segmentation method, that is, the piecewise linear representation PLR , has been applied to locate a set of tradi...

Accurate stock trend prediction is a difficult job because various intricate and complex factors affect changes in price, trading volume and trends of a stock market. On a macro scale, the factors could be the overall global economic environment, industry trends, individual economic environment (business operation and competitors’ development), the amount of floating capital in the market, etc....

Recently, data mining and time series prediction in financial forecasting has received much research attention. Many techniques are used in prediction on stock and fund trend, volatility, etc. In this paper, two technique of neural network is compared, namely, Support Vector Machine (Support Vector Machine, SVM) and MLP for considering four years of data of Sensex.(Bombay Stock Exchange).

User generated contents on web and social media grow rapidly in this emerging information age. Social media provides a platform for people to create contents, share them and bookmark them in a tremendous way. The exponential growth of social media arouses much attention on the use of public opinion to make better decisions about a particular product or person or service. The social media like o...

This paper addresses the problem of forecasting daily stock trends. The key consideration is to predict whether a given will close on uptrend tomorrow with reference today’s closing price. We propose model that comprises features selection model, based Genetic Algorithm (GA), and Random Forest (RF) classifier. In our study, we consider four international indices follow concept distributed lag a...

Price forecasting is an integral part of economic decision making. Forecasts may be used in numerous ways; specifically, individuals may use forecasts to try to earn income from speculative activities, to determine optimal government policies or to make business decisions. The importance of this topic is caused by instability in the world economy and stock markets; there is a growing interest i...

In our daily life, people are often using forecasting techniques to predict weather, stock, economy and even some important Key Performance Indicator (KPI), and so forth. Therefore, forecasting methods have recently received increasing attention. In the last years, many researchers used fuzzy time series methods for forecasting because of their capability of dealing with vague data. The followe...