Title :
Financial Forecasting: Advanced Machine Learning Techniques in Stock Market Analysis
Author :
Yoo, Paul D. ; Kim, Maria H. ; Jan, Tony
Author_Institution :
Dept. of Comput. Syst., Univ. of Technol., Sydney, NSW
Abstract :
The prediction of stock market has been an important issue in the field of finance, mathematics and engineering due to its great potential financial gain. In addition, uncertainty in the prediction of the financial time series has attracted interest from many researchers. In this study, we present recent developments in stock market prediction models, and discuss their strengths and limitations. In addition, we investigate diverse macroeconomic factors and their issues in the prediction of stock market. From this study, we found that incorporating event information into the prediction models plays important roles for more accurate prediction. Hence, an accurate event weighting method and a stable automated event extraction system are required for more accurate and reliable stock market prediction
Keywords :
economic forecasting; learning (artificial intelligence); macroeconomics; stock markets; time series; event weighting method; financial forecasting; financial time series; machine learning; macroeconomic factor; stock market analysis; Data mining; Economic forecasting; Finance; Internet; Machine learning; Mathematics; Neural networks; Predictive models; Stock markets; Uncertainty;
Conference_Titel :
9th International Multitopic Conference, IEEE INMIC 2005
Conference_Location :
Karachi
Print_ISBN :
0-7803-9429-1
Electronic_ISBN :
0-7803-9430-5
DOI :
10.1109/INMIC.2005.334420