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STOCK RANKING  A NEURAL NETWORKS APPROACH RITURAJ B.Tech. (EEE) NIT Calicut Copyright © 2008 Rituraj  All Rights Reserved
Topic to be Discussed ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Copyright © 2008 Rituraj  All Rights Reserved
What is ‘Stock Ranking’ ,[object Object],[object Object],[object Object],Mainly, it entails the comparison and prediction of the performance of stocks over a period of time. It is the  task of assigning  ratings 1  to different stocks   (shares, bonds, etc) based on its forecast, such that it facilitates the construction of a  portfolio 2 . Mainly, it entails the comparison and prediction of the performance of stocks over a period of time. Copyright © 2008 Rituraj  All Rights Reserved
What is ‘Neural Network’ ,[object Object],[object Object],Biological Neuron   Copyright © 2008 Rituraj  All Rights Reserved
C omparison between    B rain and  D igital  C omputer   Interesting point is that even with slower processing speed, our brain outperforms the Digital Computer. Copyright © 2008 Rituraj  All Rights Reserved
NEURON MODEL   ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Copyright © 2008 Rituraj  All Rights Reserved
NEURAL NETWORK ARCHITECTURES   Copyright © 2008 Rituraj  All Rights Reserved
NEURAL NETWORK LEARNING ALGORITHMS ,[object Object],[object Object],[object Object],Copyright © 2008 Rituraj  All Rights Reserved
Why use Neural Networks  in Stock Ranking ,[object Object],[object Object],[object Object],[object Object],Copyright © 2008 Rituraj  All Rights Reserved
[object Object],[object Object],[object Object],Why use Neural Networks  in Stock Ranking E xisting actuarial methods for Stock Ranking are ‘ Time-series prediction’, ‘Regression analysis’ and ‘Multiple Linear Regression’.   Copyright © 2008 Rituraj  All Rights Reserved
METHODOLOGY ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Copyright © 2008 Rituraj  All Rights Reserved
VARIABLE SELECTION ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Copyright © 2008 Rituraj  All Rights Reserved
DATA COLLECTION   ,[object Object],[object Object],[object Object],[object Object],[object Object],Copyright © 2008 Rituraj  All Rights Reserved
DATA PRE-PROCESSING   ,[object Object],[object Object],[object Object],[object Object],[object Object],Copyright © 2008 Rituraj  All Rights Reserved
[object Object],[object Object],[object Object],DATA PRE-PROCESSING   Copyright © 2008 Rituraj  All Rights Reserved
NEURAL NETWORK SELECTION   ,[object Object],Copyright © 2008 Rituraj  All Rights Reserved
[object Object],[object Object],[object Object],[object Object],[object Object],NEURAL NETWORK SELECTION   Copyright © 2008 Rituraj  All Rights Reserved
[object Object],[object Object],[object Object],[object Object],[object Object],NEURAL NETWORK SELECTION   Copyright © 2008 Rituraj  All Rights Reserved
TRAINING, TESTING AND VALIDATION   ,[object Object],[object Object],Performance under varying parameters Copyright © 2008 Rituraj  All Rights Reserved
[object Object],TRAINING, TESTING AND VALIDATION   Copyright © 2008 Rituraj  All Rights Reserved
[object Object],TRAINING, TESTING AND VALIDATION   Copyright © 2008 Rituraj  All Rights Reserved
[object Object],TRAINING, TESTING AND VALIDATION   Copyright © 2008 Rituraj  All Rights Reserved
[object Object],TRAINING, TESTING AND VALIDATION   Copyright © 2008 Rituraj  All Rights Reserved
[object Object],TRAINING, TESTING AND VALIDATION   Copyright © 2008 Rituraj  All Rights Reserved
EVALUATION CRITERIA   ,[object Object],[object Object],1. Denotes  variability in the observations   Copyright © 2008 Rituraj  All Rights Reserved
[object Object],[object Object],[object Object],EVALUATION CRITERIA   Scatter-Plot of Achieved Value and the Target Value using MLR   Copyright © 2008 Rituraj  All Rights Reserved
RESULT AND CONCLUSION   ,[object Object],[object Object],[object Object],[object Object],Copyright © 2008 Rituraj  All Rights Reserved
[object Object],[object Object],RESULT AND CONCLUSION   Copyright © 2008 Rituraj  All Rights Reserved
FUTURE SCOPE ,[object Object],[object Object],Copyright © 2008 Rituraj  All Rights Reserved
[object Object],[object Object],[object Object],FUTURE SCOPE Copyright © 2008 Rituraj  All Rights Reserved
REFERENCES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Copyright © 2008 Rituraj  All Rights Reserved
Thank you Copyright © 2008 Rituraj  All Rights Reserved

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Stock Ranking - A Neural Networks Approach

  • 1. STOCK RANKING A NEURAL NETWORKS APPROACH RITURAJ B.Tech. (EEE) NIT Calicut Copyright © 2008 Rituraj All Rights Reserved
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  • 5. C omparison between B rain and D igital C omputer Interesting point is that even with slower processing speed, our brain outperforms the Digital Computer. Copyright © 2008 Rituraj All Rights Reserved
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