While macroeconomic variables have been used extensively to forecast the U.S. equity risk premium and build models to explain it, relatively little attention has been paid to the technical stock market indicators widely employed by practitioners. Our paper fills this gap by studying the forecasting ability of a variety of technical indicators in comparison to that of a number of well-known macr...

The use of technical indicators to derive stock trading signals is a foundation of financial technical analysis. Many of these indicators have several parameters which creates a difficult optimization problem given the highly non-linear and non-stationary nature of a financial timeseries. This study investigates a popular financial indicator, Bollinger Bands, and the fine tuning of its paramete...

Abstract Financial markets are seen as one of the most important in economic terms. The activities investors financial consist predicting how best to invest accumulated capital, using all kinds analyzes and forecasts. In literature on subject, apart from fundamental analysis, technical analysis is distinguished. While first help you choose a specific asset with profitable potential, designed in...

Models of stock price prediction have traditionally used technical indicators alone to generate trading signals. In this paper, we build trading strategies by applying machine-learning techniques to both technical analysis indicators and market sentiment data. The resulting prediction models can be employed as an artificial trader used to trade on any given stock exchange. The performance of th...

| In this paper, a neural network based foreign exchange rates forecasting method is discussed. Neural networks with time series and technical indicators as inputs are built to capture the underlying \rules" of the movement in currency exchange rates. Before using historical data to train the neural networks, the traditional R/S analysis is used to test the \eeciency" of each market. The study ...

This paper offers a method for generating discrimination criteria that can be used to identify strong patterns in stock market data. More effective criteria are necessary as short-term trades are often driven by noise rather than valid market signals a viewpoint known as the "noisy trader" model of the stock market. To demonstrate why traditional technical analysis falls short, we applied the s...