Predicting Firms’ Competitive Actions from Business News Documents
Date Issued
2015
Date
2015
Author(s)
Chen, Yu-An
Abstract
Due to highly competitive and globalized business environments, firms frequently take aggressive actions to challenge their competitors in an effort to gain advantageous position on their market or improve relative performance. Therefore, it is an important issue how to identify market opportunities and timing to initiate and develop effective competitive actions. However, when a firm is affected by competitors’ competitive actions, it will undertake competitive counteractions in response to competitors’ actions to destroy or weaken rivals’ competitive advantages. But, passive responses are limited and cannot easily gain competitive advantages. If a firm can predict its rivals’ future competitive actions, it can initiate some preventive actions early or even destroy its rivals’ plan in order for the firm to capture better competitive position. Therefore, the objective of this study is to build a prediction system. According to the earlier competitive actions of a focal firm itself, all competitors’ competitive actions in market, and its direct competitors’ competitive actions, our proposed system can predict the focal firm’s future competitive actions. To be automated, it is not feasible for our system to rely on questionnaire items as its predictors. We thus decide to exploit news documents, which are large in size. In addition, our prediction system also faces a class imbalance problem. Thus, we develop an ensemble approach to address this problem. Besides, we conduct several experiments to evaluate the effectiveness of our proposed system. Our experimental results show that our proposed prediction system can predict competitive actions on the basis of data extracted from news documents, and it will get better result when using frequency as the representation scheme. Finally, we find out that different competitive action types need to consider different types of predictors, so the proposed system should apply different feature sets for different types of competitive actions.
Subjects
Competitive Actions Prediction
Competitive Actions Mining
Competitive Actions
News Documents Applications
Data Mining
Text Mining
Type
thesis
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ntu-104-R02725010-1.pdf
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