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Stock Market Prediction using Feed-forward Artificial Neural Network
| Content Provider | Semantic Scholar |
|---|---|
| Author | Boser, Bernhard |
| Copyright Year | 2014 |
| Abstract | This paper presents computational approach for stock market prediction. Artificial Neural Network (ANN) forms a useful tool in predicting price movement of a particular stock. In the short term, the pricing relationship between the elements of a sector holds firmly. An ANN can learn this pricing relationship to high degree of accuracy and be deployed to generate profits with sufficiently large amounts of data, preferably in times of low volatility and over a short time period. Experimental results are presented to demonstrate the performance of the proposed system. The paper also aims to suggest about training algorithms and training parameters that must be chosen in order to fit time series kind of complicated data to a neural network model. The proposed model succeeded in prediction of the trends of stock market with 100% prediction accuracy. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://www.ijcaonline.org/archives/volume99/number9/17399-7959?format=pdf |
| Alternate Webpage(s) | http://www.ijcaonline.org/archives/volume99/number9/17399-7959?format=pdf |
| Alternate Webpage(s) | http://research.ijcaonline.org/volume99/number9/pxc3897959.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Artificial neural network Network model Time series Volatility |
| Content Type | Text |
| Resource Type | Article |
