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{{multiple issues|cleanup=April 2011|essay-like=April 2011|original research=April 2011}}
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==IPO underpricing algorithms==
[[Underwriters]] and investors and corporations going for an [[initial public offering]] (IPO), issuers, are interested in their market value. There is always tension that results since the underwriters want to keep the price low while the companies want a high IPO price.
Underpricing may also be caused by investor over-reaction causing spikes on the initial days of trading. The IPO pricing process is similar to pricing new and unique products where there is sparse data on market demand, product acceptance, or competitive response. Thus it is difficult to determine a clear price which is compounded by the different goals issuers and investors have.
The problem with developing algorithms to determine underpricing is dealing with [[Statistical noise|noisy]], complex, and unordered data sets. Additionally, people, environment, and various environmental conditions introduce irregularities in the data. To resolve these issues, researchers have found various techniques from [[artificial intelligence]] that [[normalization (statistics)|normalizes]] the data.
==Artificial neural network==
[[Artificial neural networks]] (ANNs) resolves these issues by scanning the data to develop internal representations of the relationship between the data. By determining the relationship over time, ANNs are more responsive and adaptive to structural changes in the data. There are two models for ANNs: supervised learning and unsupervised learning.
In [[supervised learning]] models, there are tests that are needed to pass to reduce mistakes. Usually, when mistakes are encountered i.e. test output does not match test input, the algorithms use [[back propagation]] to fix mistakes. Whereas in [[unsupervised learning]] models, the input is classified based on which problems need to be resolved.
For example, Chou<ref>{{cite journal|last=Chou|first=Shi-Hao|coauthors=Yen-Sen Ni and William T. Lin|title=Forecasting IPO price using GA and ANN simulation|journal=In Proceedings of the 10th WSEAS international conference on Signal processing, computational geometry and artificial vision (ISCGAV'10)|year=2010|pages=145–150|publisher=World Scientific and Engineering Academy and Society (WSEAS)}}</ref> discusses their algorithm for determining the IPO price of [[Baidu]]. They have a three layer algorithm which contains—input level, hidden level, and output level:
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==Evolutionary models==
[[Evolutionary programming]] is often paired with other algorithms e.g. [[artificial neural network|ANN]] to improve the robustness, reliability, and adaptability. Evolutionary models reduce error rates by allowing the numerical values to change within the fixed structure of the program. Designers provide their algorithms the variables, they then provide training data to help the program generate rules defined in the input space that make a prediction in the output variable space.
In this approach, the solution is made an individual and the population is made of alternatives. However, the outliers cause the individuals to act unexpectedly as they try to create rules to explain the whole set.
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==Agent-based modelling==
Currently, many of the algorithms assume homogeneous and rational behavior among investors. However,
== References ==
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