Financial and capital markets (especially stock markets) are considered high return investment fields, which in the same time are dominated by uncertainty and volatility. Stock market prediction tries to reduce this uncertainty and consequently the risk. As stock markets are influenced by many economical, political and even psychological factors, it is very difficult to forecast the movement of...

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock pr...

Time series forecasting is of fundamental importance for a variety of domains including the prediction of earthquakes, financial market prediction, and the prediction of epileptic seizures. We present an original approach that brings a novel perspective to the field of long-term time series forecasting. Nonlinear properties of a time series are evaluated and used for long-term predictions. We u...

Stock market plays a significant role and has greater influence on basic economic energies of a country. Rapid changes in the stock exchange market with high dimensional uncertain data make the investors to look for effective forecasting using prediction mining techniques. The high dimensional stock data are classified into profitability, stability, cash flow and growth rate but does not deal c...

Real-world financial time series often contain both linear and nonlinear patterns. However, traditional time series analysis models, such as ARIMA, hold the assumption that a linear correlation exists among time series values while leaving nonlinear relation into error terms. Based on financial theories, we argue that investor sentiment is the main contributor to nonlinear pattern of stock time...

OBJECTIVES To evaluate short-term effects of publishing revised lower risk national drinking guidelines on related awareness and knowledge. To examine where drinkers heard about guidelines over the same period. DESIGN Trend analysis of the Alcohol Toolkit Study, a monthly repeat cross-sectional national survey. SETTING England, November 2015 to May 2016. PARTICIPANTS A total of 11 845 adu...

This paper reports on a number of experiments where three different groups of artificial agents learn, forecast and trade their holdings in a real stock market scenario given exogeneously in the form of easily-obtained stock statistics such as various price moving averages, first difference in prices, volume ratios, etc. These artificial agent-types trade while learning during – in most cases –...

The daily stock turning point detection problems are investigated in this study. The Support Vector Regression model has been applied in various forecasting applications and proved to be with stable performances. In this research, SVR has been used to predict the trading signal since it could handle overall information effectively even under the complex environment of stock price variations. Th...

In order to forecast the stock market more accurately, according to the dynamic property for the stock market, propose the real time modeling forecast via dynamic recurrent neural network and use GA to study online, then it improves the network performance and better describes the dynamic characteristic of stock market. By forecasting Shanghai negotiable securities index, it shows better validi...