Affiliation:
1. Faculty of Computing, Engineering and Technology, Asia Pacific University of Technology and Innovation, 57000, Malaysia
Abstract
Finance sector is highly volatile where the stock prices fluctuate rapidly and it is usually challenging to forecast. The unstable conditions and rapid changes can drastically modify the monetary value of an organization or an individual. Hence, the prediction of stock prices continues
to remain as one of the sizzling and vital topics in the applications of data mining in the finance sector. This forecasting is significant as it has the potential to reduce the losses that happen mainly due to erroneous intuitions and blind investment. Moreover, the prediction of stock prices
endure to increase in complexity with accumulation of more and more historical data. This paper focuses on American Stock Market (New York Stock Exchange and NASDAQ Stock Exchange). Taking into account the complexity of the prediction, this research proposes Autoregressive Integrated Moving
Average (ARIMA) model for estimating the value of future stock prices. ARIMA demonstrated better results for prediction as it can handle the time series data very well which is suitable for forecasting the future stock index.
Publisher
American Scientific Publishers
Subject
Electrical and Electronic Engineering,Computational Mathematics,Condensed Matter Physics,General Materials Science,General Chemistry
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献