Traditionally, technical analysis approach, that predicts stock prices based on historical prices and volume, basic concepts of trends, price patterns and oscillators, is commonly used by stock investors to aid investment decisions. Advanced intelligent techniques, ranging from pure mathematical models and expert systems to neural networks, have also been used in many financial trading systems ...

Stock market is affected by news and information. If the stock market is not efficient, the reaction of stock price to news and information will place the stock market in overreaction and under-reaction states. Many models have been already presented by using different tools and techniques to forecast the stock market behavior. In this study, the reaction of stock price in the stock market was ...

BACKGROUND To accurately predict the movement of stock prices is always of both academic importance and practical value. So far, a lot of research has been reported to help understand the behavior of stock prices. However, some of the existing theories tend to render us the belief that the time series of stock prices are unpredictable on a long-term timescale. The question arises whether the lo...

This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive polar of all words. After that, we construct the features of news based on the ...

Empirical evidence is given for a significant difference in the collective trend of the share prices during the stock index rising and falling periods. Data on the Dow Jones Industrial Average and its stock components are studied between 1991 and 2008. Pearson-type correlations are computed between the stocks and averaged over stock pairs and time. The results indicate a general trend: whenever...

a r t i c l e i n f o Keywords: Fractional cointegration Long memory Range Volatility Daily high and low prices This work provides empirical support for the fractional cointegration relationship between daily high and low stock prices, allowing for the non-stationary volatility of stock market returns. The recently formalized fractionally cointegrated vector autoregressive (VAR) model is employ...

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...

This paper explores environments in which either the revelation or diffusion of information, or its incorporation into stock prices, is gradual, and develops appropriate estimation techniques. This paper has implications both for event study methodology and for understanding the process by which stock prices incorporate information. Two environments are highlighted. First, information is often ...

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 ...

Though uninteresting individually, Twitter messages, or tweets, can provide an accurate reflection of public sentiment on when taken in aggregation. In this paper, we primarily examine the effectiveness of various machine learning techniques on providing a positive or negative sentiment on a tweet corpus. Additionally, we apply extracted twitter sentiment to accomplish two tasks. We first look ...