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Stock Ranking - A Neural Networks Approach

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riturajvasant
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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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  • 7. NEURAL NETWORK ARCHITECTURES Copyright © 2008 Rituraj All Rights Reserved
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  • 32. Thank you Copyright © 2008 Rituraj All Rights Reserved