Prediction of First-Day Returns of Initial Public Offering in the US Stock Market Using Rule Extraction from Support Vector Machines

Summary

Artificial neural networks (ANNs) and support vector machines have successfully improved the quality of predicting share movements in relation to statistically based counterparts. However, it has not been feasible to gain insight into the reasons why a certain prediction is made. Due to this limitation, the use of machine learning techniques in the capital market has met a critical hurdle. This chapter outlines a method based on pedagogical learning for extracting rules from support vector machines. To the best of our knowledge, the experiments reported here are the first attempt to utilize learning based rule extraction from support vector machines for financial data mining.

The experiments use predictions from support vector machines for extracting rules associated with the first-day returns of “initial public offerings” (IPOs) in the US stock market. A novel feature of the experiments is the simultaneous application of fundamental and technical analysis in the context of predicting the success of IPOs. Cross-industry IPOs covering the period from 1974 to 1984 and software and services IPOs launched between 1996 and 2000 are utilized.