Explaining investors' reaction to internet security breach using deterrence theory
International Journal of Electronic Finance
Machine learning-based classifiers ensemble for credit risk assessment
International Journal of Electronic Finance
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This paper offers a brief review and analysis of potential indicators in stock index prediction and discusses the current state-of-the-art research. Forecasting price movements in the stock market has been a major challenge for common investors, businesses, brokers, and speculators. With the rapid growth of internet technologies, electronic finance (e-finance) has become a vital application of e-business. Thus, the primary area of concern is to determine the appropriate time to buy, hold or sell. Usually, one technical indicator may not be sufficient for making a trading decision. Rather, an optimal and potential set of indicators are used to provide confirmation of a technical hypothesis before taking actions. This paper suggests some useful information by processing past prices into an informative trading signal by testing ten potential indicators on the Dow Jones Index from 24 July 2000 to 22 October 2007, confirms which is potentially the best among them and finally builds a Neural Network (NN) model which is effective in improving the accuracy of stock price prediction.