Nifty stock prediction using elasticnet and LSTM
By: Sai, Navya
.
Contributor(s): Lekha, A C.
Publisher: Haryana IOSR - International Organization of Scientific Research 2023Edition: Vol.25(4), Jul-Aug.Description: 9-14p.Subject(s): Computer EngineeringOnline resources: Click here
In:
IOSR Journal of Computer Engineering (IOSR-JCE)Summary: In The Financial Markets, Predicting Stock Prices Is Essential For Risk Management And Investing Strategies.
The Long Short-Term Memory (LSTM) Algorithm And Deep Learning Techniques Are Used In This Study To
Provide A Novel Method For Forecasting Nifty Stock Prices. The Study Makes Use Of A Broad Dataset From
Yahoo Finance That Spans Five Years, From 2017 To 2023.Data Pre-Processing, Which Includes Data
Cleansing, Normalisation, And Feature Engineering, Is The First Step In The Research Process. The
Performance Of Elastic Net And LSTM, Two Distinct Stock Price Prediction Systems, Is Then Contrasted. Our
Findings Show That The LSTM Algorithm Fared Better Than The Elastic Net Algorithm, Capturing The
Underlying Patterns And Trends Inside The Nifty Stock Values With More Precision. The LSTM Method, Which
Is Well Known For Its Capacity To Simulate Long-Term Relationships And Sequential Data, Was Successful In
Reproducing The Intricate Dynamics Of The Stock Market. The LSTM Effectively Learned The Temporal
Patterns And Correlations In The Nifty Stock Prices By Utilising The Memory Cell Structure Of The System,
Leading To Higher Prediction Accuracy. To Evaluate The Effectiveness Of The LSTM Model, Evaluation
Measures Including R2 Square, Mean Absolute Error, Mean Squared Error, And Root Mean Squared Error
Were Computed. The Model's Ability To Correctly Forecast Nifty Stock Prices Is Demonstrated By The
Obtained R2 Square Value Of 0.9902756561864434 And Low Error Metrics, Including A Mean Absolute Error
Of 10.723074522467424.
| Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|
Articles Abstract Database
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School of Engineering & Technology Archieval Section | Not for loan | 2023-1546 |
In The Financial Markets, Predicting Stock Prices Is Essential For Risk Management And Investing Strategies.
The Long Short-Term Memory (LSTM) Algorithm And Deep Learning Techniques Are Used In This Study To
Provide A Novel Method For Forecasting Nifty Stock Prices. The Study Makes Use Of A Broad Dataset From
Yahoo Finance That Spans Five Years, From 2017 To 2023.Data Pre-Processing, Which Includes Data
Cleansing, Normalisation, And Feature Engineering, Is The First Step In The Research Process. The
Performance Of Elastic Net And LSTM, Two Distinct Stock Price Prediction Systems, Is Then Contrasted. Our
Findings Show That The LSTM Algorithm Fared Better Than The Elastic Net Algorithm, Capturing The
Underlying Patterns And Trends Inside The Nifty Stock Values With More Precision. The LSTM Method, Which
Is Well Known For Its Capacity To Simulate Long-Term Relationships And Sequential Data, Was Successful In
Reproducing The Intricate Dynamics Of The Stock Market. The LSTM Effectively Learned The Temporal
Patterns And Correlations In The Nifty Stock Prices By Utilising The Memory Cell Structure Of The System,
Leading To Higher Prediction Accuracy. To Evaluate The Effectiveness Of The LSTM Model, Evaluation
Measures Including R2 Square, Mean Absolute Error, Mean Squared Error, And Root Mean Squared Error
Were Computed. The Model's Ability To Correctly Forecast Nifty Stock Prices Is Demonstrated By The
Obtained R2 Square Value Of 0.9902756561864434 And Low Error Metrics, Including A Mean Absolute Error
Of 10.723074522467424.
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