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GoingBatty (talk | contribs) m Typo fixing and other clean up, typos fixed: uncertainity → uncertainty, replaced: doesn't → does not using AWB (7774) |
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[[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
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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* 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
* 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.
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