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Deep learning tools for predicting stock market movements / edited by Renuka Sharma and Kiran Mehta

Published
Hoboken, NJ : John Wiley & Sons, Inc. ; Beverly, MA : Scrivener Publishing LLC, 2024.
Copyright Date
©2024
Physical Description
1 online resource
Additional Creators
Sharma, Renuka, 1980- and Mehta, Kiran
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Contents
Cover -- Title Page -- Copyright Page -- Dedication Page -- Contents -- Preface -- Acknowledgments -- Chapter 1 Design and Development of an Ensemble Model for Stock Market Prediction Using LSTM, ARIMA, and Sentiment Analysis -- 1.1 Introduction -- 1.2 Significance of the Study -- 1.3 Problem Statement -- 1.4 Research Objectives -- 1.5 Expected Outcome -- 1.6 Chapter Summary -- 1.7 Theoretical Foundation -- 1.7.1 Sentiment Analysis -- 1.7.1.1 Subjectivity -- 1.7.1.2 Polarity -- 1.7.2 Stock Market -- 1.7.3 Sentiment Analysis of Twitter in Stock Market Prediction, 1.7.4 Machine Learning Algorithms in Stock Market Prediction -- 1.8 Research Methodology -- 1.8.1 Stock Sentiment Data Fetching Through API -- 1.8.1.1 Stock Market Data Fetching -- 1.8.1.2 Sentiment Data Preprocessing -- 1.8.1.3 Stock Data Preprocessing -- 1.8.2 Project Plan -- 1.8.3 Use Case Diagram -- 1.8.4 Data Collection -- 1.8.5 Dataset Description -- 1.8.5.1 Tweets Precautions -- 1.8.5.2 Consolidation of Sentiment and Stock Price Data -- 1.8.6 Algorithm Description -- 1.8.6.1 ARIMA -- 1.8.6.2 LSTM -- 1.8.6.3 TextBlob -- 1.9 Analysis and Results -- 1.10 Conclusion -- 1.10.1 Limitation, 1.10.2 Future Work -- References -- Chapter 2 Unraveling Quantum Complexity: A Fuzzy AHP Approach to Understanding Software Industry Challenges -- 2.1 Introduction -- 2.2 Introduction to Quantum Computing -- 2.3 Literature Review -- 2.4 Research Methodology -- 2.5 Research Questions -- 2.6 Designing Research Instrument/Questionnaire -- 2.7 Results and Analysis -- 2.8 Result of Fuzzy AHP -- 2.9 Findings, Conclusion, and Implication -- References -- Chapter 3 Analyzing Open Interest: A Vibrant Approach to Predict Stock Market Operator's Movement -- 3.1 Introduction -- 3.2 Methodology, 3.3 Concept of OI -- 3.4 OI in Future Contracts -- 3.4.1 Interpreting OI & Price Movement -- 3.4.2 Open Interest and Cumulative Open Interest -- 3.4.3 Validation -- 3.4.4 Case Study with Live Market Data -- 3.5 OI in Option Contracts -- 3.5.1 Decoding Buyer or Seller in Option Chain -- 3.5.2 Put-Call Ratio (PCR) -- 3.5.3 Detection of Anomaly in Stock Price -- 3.6 Conclusion -- References -- Chapter 4 Stock Market Predictions Using Deep Learning: Developments and Future Research Directions -- 4.1 Background and Introduction -- 4.1.1 Machine Learning -- 4.1.2 About Deep Learning, and 4.2 Studies Related to the Current Work, i.e., Literature Review -- 4.3 Objective of Research and Research Methodology -- 4.4 Results and Analysis of the Selected Papers -- 4.5 Overview of Data Used in the Earlier Studies Selected for the Current Research -- 4.6 Data Source -- 4.7 Technical Indicators -- 4.7.1 Other (Advanced Technical Indicators) -- 4.8 Stock Market Prediction: Need and Methods -- 4.9 Process of Stock Market Prediction -- 4.10 Reviewing Methods for Stock Market Predictions -- 4.11 Analysis and Prediction Techniques
Subject(s)
ISBN
9781394214334 electronic book
1394214332 electronic book
9781394214327 electronic book
1394214324 electronic book
9781394214303 hardcover
1394214308 hardcover
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