IPO underpricing algorithm: Difference between revisions

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{{Short description|Increase in stock value}}

{{Multiple issues|

{{original research|date=April 2011}}

{{essay-like|date=April 2011}}

{{cleanup|date=April 2011}}

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'''[[Initial public offering#Pricing|IPO underpricing]]''' is the increase in stock value from the [[initial public offering#Pricing|initial offering price]] to the first-day closing price. Many believe that underpriced IPOs[[initial public offering|IPO]]s leave money on the table for corporations, but some believe that underpricing is inevitable. Investors state that underpricing signals high interest to the market which increases the demand. On the other hand, overpriced stocks will drop long-term as the price stabilizes so underpricing may keep the issuers safe from investor litigation.

==IPO underpricing algorithms==

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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.

== Evolutionary models ==

==Artificial neural network==

[[Evolutionary programming]] is often paired with other algorithms e.g. [[artificial neural network|ANNnetworks]] 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.

[[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.

==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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[[Category:Initial public offering]]

[[Category:Artificial neural networks]]

[[Category:EvolutionaryApplications of evolutionary algorithms]]