| Summary: | 碩士 === 國立虎尾科技大學 === 資訊管理研究所 === 94 === Initial public offerings prediction has always been an issue concerned by academia and Industry. The analysis method is form Accounting-base Valuation and Statistics linear regression to neural network in artificial intelligence trick for this topic. In this study we will offer new analytical method and use SVR to respect this topic.
Support vector machine (SVM) is a popular method of machine learning. Support vector machine has been applied to biotechnology, text categorization and image recognition. Support vector machine has good performance like decision tree and artificial neural network. In this study we use SVR to rediscover initial public offerings prediction. And the experiment will construct three models to forecast the IPO price after the seven days, the fourteen days, and the thirty days respectively. For build stable and reliable prediction model, we use Grid Algorithm and 5-fold cross-validation technique to build the models with train data, and to get the prediction errors of the every different parameter set( ). And then we observe the difference between train error and test error to be parameters selection mechanism and find out the optimal parameter set. Finally, we use the optimal parameter set to construct real support vector regression prediction model. Moreover, this study implements sensitivity analysis technique, the analysis demonstrates that incorrectly selected parameters will lead the model’s results in the risk of over-fitting, or under-fitting.
Finally, the experiment shows that SVR forecast ability significantly better than BP neural network from the model’s Performance analysis. In a word, support vector machine can efficiently apply in initial public offerings prediction, and provide administrator or investors a direction to refer.
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