IPO underpricing algorithm: Difference between revisions

Revision as of 17:54, 5 April 2011

Revision as of 14:01, 12 April 2011

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{{multiple issues|cleanup=April 2011|essay-like=April 2011|original research=April 2011}}

'''[[Initial_public_offering#Pricing | IPO underpricing]]''', is the increase in stock value from the initial offering price to the first-day closing price. Many believe that underpriced IPOs 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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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, competitive response, 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_noiseStatistical 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_Normalization (statistics) | normalizes]] the data.

==Artificial Neural Network==

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In [[supervised learning]] models, there are tests that are needed to pass to reduce mistakes. Usually, when mistakes are encountered i.e. test output doesn't 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=145-150145–150|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 -- inputcontains—input level, hidden level, and output level:

* Input level, the data is received unprocessed.

* Hidden level, the data is processed for analyses

* Output level, the data goes through a sigmoid transition function

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==Evolutionary Models==

[[Evolutionary programming]] is often paired with other algorithms e.g. [[Artificial_neural_networkArtificial |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.

===Rule-based System===

For example, Quintana<ref>{{cite journal|last=Quintana|first=David|coauthors=Cristóbal Luque and Pedro Isasi|title=Evolutionary rule-based system for IPO underpricing prediction|journal=In Proceedings of the 2005 conference on Genetic and evolutionary computation (GECCO '05)|year=2005|pages=983-989983–989}}</ref> first abstracts a model with 7 major variables. The rules evolved from the Evolutionary Computation system developed at Michigan and Pittsburgh:

* Underwriter Prestige – Is the underwriter prestigious in role of lead manager? 1 for true, 0 otherwise.

* Price Range Width – The width of the non-binding reference price range offered to potential customers during the roadshow. This width can be interpreted as a sign of uncertainty regarding the real value of the company and a therefore, as a factor that could influence the initial return.

* Price Adjustment – The difference between the final offer price and the price range width. It can be viewed as uncertainity if the adjustment is outside the previous price range.

* Offering Price – The final offer price of the IPO

* Retained Stock – Ratio of number of shares sold at the IPO divided by post-offering number of shares minus the number of shares sold at the IPO.

* Offering Size – Logarithm of the offering size in millions of dollars excluding the over-allotment option

* Technology – Is this a technology company? 1 for true, 0 otherwise.

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==Agent-Based Modelling==

Currently, many of the algorithms assume homogeneous and rational behavior among investors. However, there’s an alternative approach being researched to financial modeling called [[Agent-based_modelbased model| Agent-Based Modelling]] (ABM). ABM uses different autonomous agents whose behavior evolves endogenously which lead to complicated system dynamics that are sometimes impossible to predict from the properties of individual agents .<ref>{{cite journal|last=Brabazon|first=Anthony|coauthors=Jiang Dang, Ian Dempsy, Michael O'Neill, and David M. Edelman|title=Natural Computing in finance: a review|journal=Handbook of Natural Computing|year=2010|url=irserver.ucd.ie/dspace/bitstream/10197/2737/1/NCinFinance_v8.pdf}}</ref>. ABM is starting to be applied to computational finance. Though, for ABMs to be more accurate, better models for rule-generation need to be developed.

== References ==

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{{Corporate finance and investment banking}}

{{DEFAULTSORT:Algorithmic Finance}}

[[Category:Corporate finance]]

[[Category:Stock market]]

[[Category:Algorithms]]

[[bg:Първично публично предлагане]]

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[[zh-yue:招股上市]]

[[zh:首次公开募股]]

{{DEFAULTSORT:Algorithmic Finance}}

[[Category:Corporate finance]]

[[Category:Stock market]]

[[Category:Algorithms]]