Prediction of earnings per share for industry
| dc.contributor.author | Jadhav, Swati | |
| dc.contributor.author | He, Hongmei | |
| dc.contributor.author | Jenkins, Karl W. | |
| dc.date.accessioned | 2019-10-18T15:03:01Z | |
| dc.date.available | 2019-10-18T15:03:01Z | |
| dc.date.issued | 2016-08-01 | |
| dc.description.abstract | Prediction of Earnings Per Share (EPS) is the fundamental problem in finance industry. Various Data Mining technologies have been widely used in computational finance. This research work aims to predict the future EPS with previous values through the use of data mining technologies, thus to provide decision makers a reference or evidence for their economic strategies and business activity. We created three models LR, RBF and MLP for the regression problem. Our experiments with these models were carried out on the real datasets provided by a software company. The performance assessment was based on Correlation Coefficient and Root Mean Squared Error. These algorithms were validated with the data of six different companies. Some differences between the models have been observed. In most cases, Linear Regression and Multilayer Perceptron are effectively capable of predicting the future EPS. But for the high nonlinear data, MLP gives better performance. | en_UK |
| dc.identifier.citation | Jadhav S, He H, Jenkins K. Prediction of earnings per share for industry. Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 12-14 November 2015, Lisbon, Portugal | en_UK |
| dc.identifier.isbn | 978-9-8975-8164-9 | |
| dc.identifier.uri | http://ieeexplore.ieee.org/document/7526950/ | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/14627 | |
| dc.language.iso | en | en_UK |
| dc.publisher | IEEE | en_UK |
| dc.rights | Attribution-NonCommercial 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/ | * |
| dc.title | Prediction of earnings per share for industry | en_UK |
| dc.type | Conference paper | en_UK |
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