Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/14690
Full metadata record
DC FieldValueLanguage
dc.contributor.authorB. Charumathi-
dc.contributor.authorSuraj E. S.-
dc.date.accessioned2024-03-02T06:28:56Z-
dc.date.available2024-03-02T06:28:56Z-
dc.date.issued2015-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/14690-
dc.description.abstractThis study performs and compares the accuracy of Price to Earnings model and Refined Price to Earnings model using Artificial Neural Network (ANN) for valuing bank stocks. Prediction accuracy measuring procedures are used to compare the performance of these models. This study also focused on comparing the predictive power of Price to Earnings Model & Refined Price to Earnings Model (using ANN) using coefficient of determination. The outcomes of predictions are discussed to know the power of Artificial Neural Network. The results of empirical analysis support that Refined Price to earnings model using ANN can be used as a valuation tool to provide better and more accurate estimation of equity stock prices of banks.-
dc.publisherJournal of Banking Information Technology and Management-
dc.subjectPrice to Earnings Valuation Model-
dc.subjectPrediction Accuracy-
dc.subjectArtificial Neural Network-
dc.titleRefining Price to Earnings Model for Valuing Bank Stocks -An Artificial Neural Network Approach-
dc.volVol. 12-
dc.issuedNo. 2-
Appears in Collections:Articles to be qced

Files in This Item:
File SizeFormat 
Refining Price to Earnings Model For Valuing Bank Stocks.pdf3.55 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.