In the stock market, technical analysis is a useful method for predicting stock prices. Although, professional stock analysts and fund managers usually make subjective judgments, based on objective technical indicators, it is difficult for non-professionals to apply this forecasting technique because there are too many complex technical indicators to be considered. Moreover, two drawbacks have ...

This research work, proposes forecasting stock prices in the stock market industry in Nigeria using a Weightless Neural Network (WNN). A neural network application used to demonstrate the application of the WNN in the forecasting of stock prices in the market is designed and implemented in Visual Foxpro 6.0. The proposed network is tested with stock data obtained from the Nigeria Stock Exchange...

Social media-based forecasting has received significant attention from academia and industries in recent years. With a focus on Twitter, this paper investigates whether sentiments of the tweets regarding the 7 largest US financial service companies (in U.S. dollars) are related to the stock price changes of these companies. The authors’ findings indicate, in the financial services context, nega...

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...

The especial importance of capital market in countries is undeniable in economic development via effective capital conduct and optimum resources allocation. Investment in capital market requires decision making in new stock exchanges, and   accessing information in the case of future status of capital market. Undoubtedly, nowadays most part of capital is exchanged via stock exchange all around ...

This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this pa...

Empirical research documents that temporary trends in stock price movements exist. Moreover, riding a trend can be a profitable investment strategy. Thus, the ability to recognize trends in stock markets influences the quality of investment decisions. In this paper, we provide a thorough test of the trend recognition and forecasting ability of financial professionals who work in the trading roo...

the difficulty of determining intrinsic value of stock prices have led many people to use technical analysis in order to forecast stock prices in the future. to predict the stock price we need to determine the generating process of stock prices. in recent years many time - series methods have been used for forecasting purposes. one of these methods is the rescaled range analysis (ris). in the a...

This article applied GARCH model instead AR or ARMA model to compare with the standard BP and SVM in forecasting of the four international including two Asian stock markets indices.These models were evaluated on five performance metrics or criteria. Our experimental results showed the superiority of SVM and GARCH models, compared to the standard BP in forecasting of the four international stock...

The use of price–earnings ratios and dividend-price ratios as forecasting variables for the stock market is examined using aggregate annual US data 1871 to 2000 and aggregate quarterly data for twelve countries since 1970. Various simple efficient-markets models of financial markets imply that these ratios should be useful in forecasting future dividend growth, future earnings growth, or future...