# Random Forest Prediction of IPO Underpricing
## 1 Introduction  
The magnitude of initial returns in initial public offerings (IPOs) has been puzzling both researchers and practitioners for decades. The difference between the offering price and the first closing price regularly results in sizeable gains or losses. Among these, there is a bias in the distribution of initial returns that is positive and heavily skewed to the right. For this reason, first-day initial returns are often referred to in the literature as IPO underpricing.  
During the Internet bubble of the late 1990s, when the market was very hot, a substantial portion of the companies taken public in the USA more than doubled their offering price in the first day and the phenomenon often resulted in headlines in the news. [PERSON] and [PERSON] [1] researched the phenomenon, and reported that the average initial return on a sample of 6249 companies taken public in the USA between 1980 and 2001 was 18.8%. More recently, the number of IPOs has dramatically gone down, and so has the average initial return. Having said that, in 2014 Castiglit, San Francisco, CA, USA, a health care software company, soared 149% during its first trading day. Another recent example, though more limited, would be Alibaba Group, Hangzhou, China, the e-commerce giant. This corporation showed a 38% increase from its initial public offering price on the day it went public.  
The academic literature has been tracking first-day trading returns in initial public offerings (IPOs) for a long time. Over time, researchers have postulated a large number of explanatory theories, and it is still a very active field of investigation. The vast majority of the empirical analyses carried out to explain and predict underpricing relying on variables related to the structure of the offerings are based on linear models. There have been, however, a number of efforts trying to predict first-day returns using computational intelligence.  
Among the latter, we should mention the work of [PERSON] and [PERSON] [2]. These authors try to predict the post-issue market price using artificial neural networks. More recently, [PERSON] et al. [3], [PERSON] [4], [PERSON] [5], and [PERSON] et al. [6] followed suit with the same technique. This line wascomplemented by [PERSON] et al. [7] and [PERSON] et al. [8], who explored the problem using evolutionary algorithms. Others, such as [PERSON] et al. [9], used a combination of both of them.  
In this paper, we suggest that there is an instrument that has not been tested in the domain yet, that has the potential to be extremely valuable. In a field where the presence of outliers and the problem of overfitting severely affects the performance of predictive algorithms, random forests [9], are likely to bring a great deal to the table. For this reason, we intend to benchmark the random forests on a sample from the USA financial markets against a number of classic machine learning alternatives that represent different algorithm categories.  
The rest of the paper will be structured as follows: in Section 2, we provide a brief introduction to random forests and introduce both the explanatory variables and the sample that will be used in the analysis; Section 3 will be used to report the experimental results; that will be followed by a discussion in Section 4; and, finally, Section 5 will cover the summary and conclusions.