IPO Pricing Research Of GEM Based On Wavelet Neural Network

GEM, also known as the Second Board Market (Second-board Market), is a different class of securities markets, independent or attached to the main market. To meet the domestic law of financial development, improve the proportion of direct financing, China’s GEM officially opened on the Shenzhen Stock Exchange board in October2009. GEM focus on supporting enterprises with independent innovation capability, unlike the low class manufacturing industries, especially unlike the high-pollution or high energy-consumption enterprises.From the angle of financial markets, the establishment of GEM is mainly to solve the immediate financing needs of entrepreneurial companies, emerging businesses, industrial enterprises and other high-tech industries, to create normal exit mechanism for the risk capital. From a macroeconomic point of view, it is an effective method of adjusting China’s industrial structure, promoting industrial upgrading, optimizing the allocation of capital and financing social restructuring of important initiatives.Stock Market based on market functions can be divided into:primary market, also known as the issue market, which is supplied to the stock market; secondary market, also known as trading markets, it is trading in shares outstanding in the market. A market share of supply includes IPO (Initial Public offerings), the initial public offering and the refinancing of listed companies, including but not limited to equity public offerings, private placement, rights issue, convertible bonds and other forms. With respect to the latter, IPO market is more important because it is the first step to get business financing on the capital markets.However, in recent years, a large number of shares break in Primary Market, especially breaks of GEM has reached a very serious condition, which attracted the attention of scholars around the world. IPO pricing has a reasonable direct impact on the performance of the stock. Over the years, scholars have focused on the IPO underpricing, the study on the massive anomalous break phenomena in China has been very little. Thus, in current economic environment,"broken damp" phenomenon in capital markets especially in GEM prompts us to think and research:whether our IPO pricing is reasonable and which IPO pricing method is appropriate for our GEM IPO pricing.About IPO pricing, there have been a lot of researches and theories all over the world. Multiple factors influencing IPO pricing reflect nonlinear correlation characteristics. Numerous external and internal factors affect the market performance of new shares. These factors have to be into account when IPO pricing, thus the pricing is very complex. There are many methods of IPO pricing valuation in relevant research literature. Four ways is commonly used:Companies Compared Method, Discounted Cash Flow Method, Economic Value Added (EVA) Method and Real Options Valuation Method. However in my opinion, some methods may be rough and the estimation has large error, some methods may be much more accurate but contain more assumptions, so the applicability is very restricted.According to the actual situation in our country, on one hand GEM has been founded for a short time, the market is not mature, sample available for comparative analysis of the market is limited, and some data fluctuate by a large margin. On the other hand, GEM Enterprises, which mostly are growing entrepreneurial businesses, have distinct characteristics, so the indicators may be difficult to meet the model assumptions. Therefore, traditional methods used in GEM IPO pricing is subject to greater limitations. Such a situation prompts researchers to find new pricing method.In recent years, the Neural Network is widely used in economic forecasting, financial early warning, stock prediction, forecast futures prices, option pricing and other aspects. Meanwhile, some scholars use Artificial Neural Networks to do IPO pricing studies. From the research findings, this approach is feasible. Previous knowledge on the IPO factors is not comprehensive enough. Such as the pricing analysis for a particular type of business in a specific market is inadequate. However, we can see that the use of neural network model in IPO pricing valuation has much more room for development. Therefore, this paper will analyze influencing factors of GEM IPO pricing comprehensively and continue to develop the use of Neural Network in IPO pricing. We believe some new results and trends will be found based on the use of Neural Networks in GEM IPO pricing analysis, that will be a significance for pricing mechanisms in GEM market inevitably.