Deep Learning Tools for Predicting Stock Market Movements

1 Design and Development of an Ensemble Model for Stock Market

Prediction Using LSTM, ARIMA, and Sentiment Analysis 1

Poorna Shankar, Kota Naga Rohith

and Muthukumarasamy Karthikeyan

1.1 Introduction 2

1.2 Significance of the Study 3

1.3 Problem Statement 5

1.4 Research Objectives 6

1.5 Expected Outcome 6

1.6 Chapter Summary 7

1.7 Theoretical Foundation 8

1.7.1 Sentiment Analysis 8

1.7.1.1 Subjectivity 8

1.7.1.2 Polarity 9

1.7.2 Stock Market 10

1.7.3 Sentiment Analysis of Twitter in Stock

Market Prediction 11

1.7.4 Machine Learning Algorithms in Stock Market

Prediction 12

1.8 Research Methodology 13

1.8.1 Stock Sentiment Data Fetching Through API 13

1.8.1.1 Stock Market Data Fetching 13

1.8.1.2 Sentiment Data Preprocessing 13

1.8.1.3 Stock Data Preprocessing 14

1.8.2 Project Plan 14

1.8.3 Use Case Diagram 16

1.8.4 Data Collection 17

1.8.5 Dataset Description 18

viii Contents

1.8.5.1 Tweets Precautions 18

1.8.5.2 Consolidation of Sentiment and Stock

Price Data 18

1.8.6 Algorithm Description 18

1.8.6.1 ARIMA 18

1.8.6.2 LSTM 20

1.8.6.3 TextBlob 21

1.9 Analysis and Results 22

1.10 Conclusion 33

1.10.1 Limitation 34

1.10.2 Future Work 34

References 34

2 Unraveling Quantum Complexity: A Fuzzy AHP Approach

to Understanding Software Industry Challenges 39

Kiran Mehta and Renuka Sharma

2.1 Introduction 39

2.2 Introduction to Quantum Computing 41

2.3 Literature Review 43

2.4 Research Methodology 45

2.5 Research Questions 46

2.6 Designing Research Instrument/Questionnaire 48

2.7 Results and Analysis 49

2.8 Result of Fuzzy AHP 50

2.9 Findings, Conclusion, and Implication 54

References 56

3 Analyzing Open Interest: A Vibrant Approach to Predict

Stock Market Operator’s Movement 61

Avijit Bakshi

3.1 Introduction 62

3.2 Methodology 64

3.3 Concept of OI 64

3.4 OI in Future Contracts 65

3.4.1 Interpreting OI & Price Movement 65

3.4.2 Open Interest and Cumulative Open Interest 68

3.4.3 Validation 70

3.4.4 Case Study with Live Market Data 72

3.5 OI in Option Contracts 79

3.5.1 Decoding Buyer or Seller in Option Chain 81

3.5.2 Put-Call Ratio (PCR) 83

3.6 Conclusion 85

References 87

4 Stock Market Predictions Using Deep Learning: Developments

and Future Research Directions 89

Renuka Sharma and Kiran Mehta

4.1 Background and Introduction 90

4.1.1 Machine Learning 92

4.1.2 About Deep Learning 94

4.2 Studies Related to the Current Work, i.e., Literature Review 97

4.3 Objective of Research and Research Methodology 100

4.4 Results and Analysis of the Selected Papers 100

4.5 Overview of Data Used in the Earlier Studies Selected

for the Current Research 102

4.6 Data Source 103

4.7 Technical Indicators 105

4.7.1 Other (Advanced Technical Indicators) 106

4.8 Stock Market Prediction: Need and Methods 106

4.9 Process of Stock Market Prediction 107

4.10 Reviewing Methods for Stock Market Predictions 110

4.11 Analysis and Prediction Techniques 111

4.12 Classification Techniques (Also Called Clustering

Techniques) 111

4.13 Future Direction 112

4.13.1 Cross-Market Evaluation or Analysis 112

4.13.2 Various Data Inputs 112

4.13.3 Unexplored Frameworks 113

4.13.4 Trading Strategies Based on Algorithm 114

4.14 Conclusion 114

References 116

5 Artificial Intelligence and Quantum Computing Techniques

for Stock Market Predictions 123

Rajiv Iyer and Aarti Bakshi

5.1 Introduction 124

5.2 Literature Survey 125

5.3 Analysis of Popular Deep Learning Techniques

for Stock Market Prediction 132

5.3.1 Blind Quantum Computing (BQC) in Stock

Market Prediction 132