Deep Learning Tools for Predicting Stock Market Movements E-Book
194,99 €
Mehr erfahren.
- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
DEEP LEARNING TOOLS for PREDICTING STOCK MARKET MOVEMENTS
The book provides a comprehensive overview of current research and developments in the field of deep learning models for stock market forecasting in the developed and developing worlds.
The book delves into the realm of deep learning and embraces the challenges, opportunities, and transformation of stock market analysis. Deep learning helps foresee market trends with increased accuracy. With advancements in deep learning, new opportunities in styles, tools, and techniques evolve and embrace data-driven insights with theories and practical applications. Learn about designing, training, and applying predictive models with rigorous attention to detail. This book offers critical thinking skills and the cultivation of discerning approaches to market analysis.
The book:
- details the development of an ensemble model for stock market prediction, combining long short-term memory and autoregressive integrated moving average;
- explains the rapid expansion of quantum computing technologies in financial systems;
- provides an overview of deep learning techniques for forecasting stock market trends and examines their effectiveness across different time frames and market conditions;
- explores applications and implications of various models for causality, volatility, and co-integration in stock markets, offering insights to investors and policymakers.
Audience
The book has a wide audience of researchers in financial technology, financial software engineering, artificial intelligence, professional market investors, investment institutions, and asset management companies.
Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 753
Veröffentlichungsjahr: 2024
Ähnliche
Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Dedication Page
Preface
Acknowledgments
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
1.8 Research Methodology
1.9 Analysis and Results
1.10 Conclusion
References
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
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
3.5 OI in Option Contracts
3.6 Conclusion
References
4 Stock Market Predictions Using Deep Learning: Developments and Future Research Directions
4.1 Background and Introduction
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
4.8 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
4.12 Classification Techniques (Also Called Clustering Techniques)
4.13 Future Direction
4.14 Conclusion
References
5 Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions
5.1 Introduction
5.2 Literature Survey
5.3 Analysis of Popular Deep Learning Techniques for Stock Market Prediction
5.4 Data Sources and Methodology
5.5 Result and Analysis
5.6 Challenges and Future Scope
5.7 Conclusion
References
6 Various Model Applications for Causality, Volatility, and Co-Integration in Stock Market
6.1 Introduction
6.2 Literature Review
6.3 Objectives of the Chapter
6.4 Methodology
6.5 Result and Discussion
6.6 Implications
6.7 Conclusion
References
7 Stock Market Prediction Techniques and Artificial Intelligence
7.1 Introduction
7.2 Financial Market
7.3 Stock Market
7.4 Stock Market Prediction
7.5 Artificial Intelligence and Stock Prediction
7.6 Benefits of Using AI for Stock Prediction
7.7 Challenges of Using AI for Stock Prediction
7.8 Limitations of AI-Based Stock Prediction
7.9 Conclusion
References
8 Prediction of Stock Market Using Artificial Intelligence Application
8.1 Introduction
8.2 Objectives
8.3 Literature Review
8.4 Future Scope
8.5 Sources of Study and Importance
8.6 Case Study: Comparison of AI Techniques for Stock Market Prediction
8.7 Discussion and Conclusion
References
9 Stock Returns and Monetary Policy
9.1 Introduction
9.2 Literature
9.3 Data and Methodology
9.4 Index-Based Analysis
9.5 Firm-Level Analysis
9.6 The Impact of Financial Constraints
9.7 Discussion and Conclusion
References
Appendix 1
Appendix 2
10 Revolutionizing Stock Market Predictions: Exploring the Role of Artificial Intelligence
10.1 Introduction
10.2 Review of Literature
10.3 Research Methods
10.4 Results and Discussion
10.5 Conclusion
10.6 Significance of the Study
10.7 Scope of Further Research
References
11 A Comparative Study of Stock Market Prediction Models: Deep Learning Approach and Machine Learning Approach
11.1 Introduction
11.2 Stock Market Prediction
11.3 Models for Prediction in Stock Market
11.4 Conclusion
References
12 Machine Learning and its Role in Stock Market Prediction
12.1 Introduction
12.2 Literature Review
12.3 Standard ML
12.4 DL
12.5 Implementation Recommendations for ML Algorithms
12.6 Overcoming Modeling and Training Challenges
12.7 Problems with Current Mechanisms
12.8 Case Study
12.9 Research Objective
12.10 Conclusion
12.11 Future Scope
References
13 Systematic Literature Review and Bibliometric Analysis on Fundamental Analysis and Stock Market Prediction
13.1 Introduction
13.2 Fundamental Analysis
13.3 Machine Learning and Stock Price Prediction/Machine Learning Algorithms
13.4 Related Work
13.5 Research Methodology
13.6 Analysis and Findings
13.7 Discussion and Conclusion
References
14 Impact of Emotional Intelligence on Investment Decision
14.1 Introduction
14.2 Literature Review
14.3 Research Methodology
14.4 Data Analysis
14.5 Discussion, Implications, and Future Scope
14.6 Conclusion
References
15 Influence of Behavioral Biases on Investor Decision-Making in Delhi-NCR
15.1 Introduction
15.2 Literature Review
15.3 Research Hypothesis
15.4 Methodology
15.5 Discussion
References
16 Alternative Data in Investment Management
16.1 Introduction
16.2 Literature Review
16.3 Research Methodology
16.4 Results and Discussion
16.5 Implications of This Study
16.6 Conclusion
References
17 Beyond Rationality: Uncovering the Impact of Investor Behavior on Financial Markets
17.1 Introduction
17.2 Statement of the Problem
17.3 Need for the Study
17.4 Significance of the Study
17.5 Discussions
17.6 Implications
17.7 Scope for Further Research
References
18 Volatility Transmission Role of Indian Equity and Commodity Markets
18.1 Introduction
18.2 Literature Review
18.3 Data and Methodology
18.4 Results and Discussions
18.5 Conclusion
References
Glossary
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Representation of sentiments polarity and weight.
Chapter 2
Table 2.1 Genesis of QuCo.
Table 2.2 Calculation of weights for all factors of the current study.
Table 2.3 Ranking of different factors.
Table 2.4 Result of analysis of factors and their respective variables.
Chapter 3
Table 3.1 OI movement.
Table 3.2 Interpretation of OI and price movement.
Table 3.3 Analysis of OI and price of Adani Ports and SEZ Ltd.
Table 3.4 Seven-day average relative strength.
Table 3.5 Price OI analysis.
Table 3.6 Critical price of buyers and sellers.
Chapter 4
Table 4.2 List of journals with number of research papers published.
Table 4.3 List of countries and no of articles published.
Chapter 8
Table 8.1 Fundamental aspects of the stock market.
Table 8.2 Understanding artificial intelligence.
Table 8.3 Literature review.
Table 8.4 Future scope and citation.
Chapter 9
Table 9.1 The effect of monetary policy surprises on the ISE-100 Index.
Table 9.2 Descriptive statistics of B
1
coefficients.
Table 9.3 Sectoral effect of monetary policy surprises on stock prices.
a
Table 9.4 Correlation of financial constraint indicators.
Table 9.5 Monetary policy surprises and indicators of financial constraints.
a
Table 9.A.1 Test results.
Table 9.A.2 Test results.
Chapter 12
Table 12.1 Literature review.
Table 12.2 Key challenges with ML-based stock prediction.
Table 12.3 Pandas to obtain stock information.
Table 12.4 Statistical descriptions of the data.
Chapter 13
Table 13.1 List of top authors, countries, and institutes of fundamental analy...
Table 13.2 List of top journals for fundamental analysis and stock market pred...
Table 13.3 List of top articles in fundamental analysis and stock market predi...
Table 13.4 List of top author keywords in fundamental analysis and stock marke...
Table 13.5 Thematic clusters on fundamental analysis and stock market predicti...
Table 13.6 List of machine learning algorithm used.
Table 13.7 List of training and testing dataset criteria used.
Table 13.8 List of evaluation metrics used.
Table 13.9 List of factors used in fundamental analysis.
Table 13.10 List of technical indicators used.
Table 13.11 List of feature selection criteria.
Chapter 14
Table 14.1 Demographic profile of respondents.
Table 14.2 KMO and Bartlett’s tests.
Table 14.3 Total variance explained.
Table 14.4 Rotated component matrix.
Table 14.5 Reliability analysis.
Table 14.6 Factor naming table.
Table 14.7 Regression model summary.
Table 14.8 ANOVA table.
Table 14.9 Coefficients table.
Chapter 15
Table 15.1 Descriptive statistics.
Table 15.2 Factor loadings.
Table 15.3 Internal consistency and convergent validity.
Table 15.4 HTMT ratio-discriminant validity.
Table 15.5 Model fit results.
Table 15.6 R Square results.
Table 15.7 Hypothesis testing results.
Chapter 18
Table 18.1 Descriptive statistics of volatility series.
Table 18.2 Unit Roots tests on volatility series.
Table 18.3 Volatility transmission.
List of Illustrations
Chapter 1
Figure 1.1 Representation of share value as per sentiments.
Figure 1.2 Representation of project plan.
Figure 1.3 Representation of use case diagram.
Figure 1.4 Long short-term memory network cell state.
Figure 1.5 Count of tweets as per sentiment value.
Figure 1.6 Word and character count of tweets.
Figure 1.7 Multinomial naive Bayes classifier for true vs. predicted label.
Figure 1.8 Multinomial naive Bayes classifier.
Figure 1.9 Date vs. stock closing price.
Figure 1.10 LSTM epoch vs. loss.
Figure 1.11 LSTM prediction for Tata Motors.
Figure 1.12 Seasonal decompose graph for Tata Motors.
Figure 1.13 Checking ARIMA accuracy for Tata stock from 2020.
Figure 1.14 ARIMA prediction graph for Tata Motors from 2023.
Chapter 2
Figure 2.1 Chart showing the ranking of different factors considered for the s...
Chapter 3
Figure 3.1 Cumulative OI of Adani Ports and SEZ Ltd.
Figure 3.2 Moving average chart of Adani Ports and SEZ Ltd.
Figure 3.3 Seven-day moving average RS chart.
Figure 3.4 Option chain (equity derivatives).
Figure 3.5 Technical chart.
Chapter 4
Figure 4.1 Process of stock market prediction.
Figure 4.2 Deep learning models used in earlier research studies.
Figure 4.3 Chart showing a number of studies in different years.
Figure 4.4 Type of data sources.
Figure 4.5 Steps to be taken for stock market predictions.
Figure 4.6 Model classification for stock market predictions.
Chapter 5
Figure 5.1 BQC approach [3].
Figure 5.2 Quantum feedforward neural network [4].
Chapter 7
Figure 7.1 Functioning of stock market predictions system
1
.
Chapter 11
Figure 11.1 Forms of raw data.
Figure 11.2 Representation of stock market prediction approaches.
Figure 11.3 Assessment of performance amid AI, ML, and DL [24].
Figure 11.4 Stock market prediction models.
Figure 11.5 A sample of an artificial neural network.
Figure 11.6 The classification method of stock market prediction: Deep learnin...
Figure 11.7 The classic presentation of widely used deep learning models for s...
Chapter 12
Figure 12.1 Basic machine learning algorithms used for stock market prediction...
Figure 12.2 Various methods to stock forecast.
Figure 12.3 How ML is applied to stock prediction.
Figure 12.4 Flowchart of ARIMA approach.
Figure 12.5 Deep learning process used in ANN.
Figure 12.6 Using ML to sentiment analyses.
Figure 12.7 Stock’s performance over time.
Figure 12.8 Stock trading volume.
Figure 12.9 Moving average technical analysis technique.
Figure 12.10 Stock’s average daily return.
Figure 12.11 Histogram to obtain a general sense of the average daily return.
Figure 12.12 DataFrame for each of the stocks.
Figure 12.13 Charts obtained using PairGrid().
Figure 12.14 Correlation plot.
Figure 12.15 Daily percentage returns.
Chapter 13
Figure 13.1 Publication activity of fundamental analysis and stock price predi...
Figure 13.2 Network map of top author keywords in fundamental analysis and sto...
Chapter 15
Figure 15.1 Proposed model.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Dedication Page
Preface
Acknowledgments
Begin Reading
Glossary
Index
WILEY END USER LICENSE AGREEMENT
Pages
ii
iii
iv
v
xvii
xviii
xix
xx
xxi
xxii
xxiii
xxiv
xxv
xxvi
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Deep Learning Tools for Predicting Stock Market Movements
Edited by
Renuka Sharma
Chitkara Business School, Chitkara University, Punjab, India
and
Kiran Mehta
Chitkara Business School, Chitkara University, Punjab, India
This edition first published 2024 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2024 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, except as permitted by law. Advice on how to obtain permission to reuse material from this title is available at http://www.wiley.com/go/permissions.
Wiley Global Headquarters111 River Street, Hoboken, NJ 07030, USA
For details of our global editorial offices, customer services, and more information about Wiley products visit us at www.wiley.com.
Limit of Liability/Disclaimer of WarrantyWhile the publisher and authors have used their best efforts in preparing this work, they make no representations or warranties with respect to the accuracy or completeness of the contents of this work and specifically disclaim all warranties, including without limitation any implied warranties of merchant-ability or fitness for a particular purpose. No warranty may be created or extended by sales representatives, written sales materials, or promotional statements for this work. The fact that an organization, website, or product is referred to in this work as a citation and/or potential source of further information does not mean that the publisher and authors endorse the information or services the organization, website, or product may provide or recommendations it may make. This work is sold with the understanding that the publisher is not engaged in rendering professional services. The advice and strategies contained herein may not be suitable for your situation. You should consult with a specialist where appropriate. Neither the publisher nor authors shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Further, readers should be aware that websites listed in this work may have changed or disappeared between when this work was written and when it is read.
Library of Congress Cataloging-in-Publication Data
ISBN 978-1-394-21430-3
Cover image: Pixabay.ComCover design by Russell Richardson
Dedicated to our Parents and God
Preface
Predicting the movement of stocks is a classic but difficult topic that has attracted the study of economists and computer scientists alike. Over the last couple of decades, several efforts have been made to investigate the use of linear and machine learning (ML) technologies with the objective of developing an accurate prediction model. New horizons, such as deep learning (DL) models, have just been brought to this field, and the pace of advancement is too quick to keep up with. Moreover, the stock market behavior and pattern have perplexed researchers and mathematicians for decades. Therefore, it is crucial to familiarize oneself with the many investment opportunities, styles, tools, and techniques to study the stock market volatility, and portfolio management solutions that exist in the case of a global financial catastrophe. Therefore, the objective of the current work is to give a thorough view of the evolution and development of DL tools and techniques in the field of stock market prediction in the developed and developing worlds.
Stock market interest has grown in recent years. Investors exchange millions of dollars in assets every day to profit. If an investor can predict market behavior, they may earn higher risk-adjusted returns. DL, ML, soft computing, and computational intelligence research have produced accurate stock market predictions. Financial research is tough but essential for stock market predictions. The efficient market hypothesis (EMH) may not be compatible with investors beating the market in risk-adjusted returns, but it does not imply that it is untrue. Its assumptions have been questioned. Momentum, reversal, and volatility contradict the EMH. Institutional investors can adjust for random over- and underreactions. This led to models that include how individuals think and behave, casting doubt on the premise that investors are always fully rational due to defects like loss aversion and overreaction. Fundamental and technical analyses are used to forecast stock prices. Previous research predicted stock prices and returns using statistical time series methods. Moving averages, Kalman filtering, and exponential smoothing are typical methods. Logistic regression and support vector machines have acquired appeal in stock market forecasting research with the introduction of AI and soft computing. These algorithms can handle more complex time series data to produce better predictions. These novel and helpful financial market forecasting tools intrigue academics. DL techniques and prediction models are evolving. Programming languages have evolved to make DL model creation and testing simpler. Online news or data adds to stock market forecasts. Knowledge graphbased graph neural networks are a new innovation. DL is used to recognize objects, classify images, and forecast time series. DL models outperform linear and Machine learning (ML) models for stock market prediction because they can handle vast volumes of data and grasp nonlinear associations. Asset management businesses (AMCs) and investment banks (IBs) are expanding their funding for AI research, which is currently represented by DL models. The objective of the current work is to give a thorough view of the evolution and development of DL tools and techniques in the field of stock market prediction.
We hope that the present work serves as a guiding beacon in your exploration of this captivating intersection. May the insights within these pages empower you to navigate the complexities of finance with newfound confidence and a deeper understanding of the transformative potential that lies at the nexus of DL and stock market predictions. In compiling this work, we have drawn from a myriad of sources, ranging from academic research and industry case studies to real-world applications. Our intent is to offer a balanced perspective—one that not only imparts technical knowledge but also fosters critical thinking and the cultivation of a discerning approach to market analysis.
Chapter 1 delves into the development of an ensemble model for stock market prediction, combining long short-term memory (LSTM), autoregressive integrated moving average (ARIMA), and sentiment analysis. The research captures long-term dependencies using LSTM, linear relationships through ARIMA, and public sentiment from tweets using sentiment analysis. Experimental results reveal the ensemble model’s superior accuracy over individual models. The study underscores the significance of sentiment analysis, extracted from tweets, in enhancing stock market predictions. This innovative approach offers improved insights into stock price movements, benefitting investors and financial institutions.
Chapter 2 explained that the rapid expansion of quantum computing (QuCo) technologies, which will change software engineering, confronts the software market. The evaluation and prioritization of QuCo problems, however, are fragmented and immature. The preliminary nature of QuCo research and the growing demand for multidisciplinary studies to address these challenges were shown by a thorough literature analysis using data from several digital libraries. Insights from the study include the necessity for significant organizational efforts to properly take advantage of QuCo’s benefits, documenting processes, needs, and fundamental norms for effective QuCo deployment, and addressing issues in scalability and resource performance evaluation. Researchers should look into how the assimilation of new technologies might lessen the organizational learning curve and encourage adoption. The study’s implications include the need for substantial organizational efforts to fully harness QuCo’s advantages, documenting processes, requirements, and inherent rules for effective QuCo adoption, and addressing challenges in scalability and resource performance evaluation. Scholars should investigate how the technology assimilation process can ease organizational learning load and promote the uptake of new technology.
Chapter 3 delves into the intricacies of open interest in the derivative market, emphasizing its importance in predicting market sentiments. By tracking variations in spot price, open interest, and delivery data, traders can gauge operator intentions. The chapter underscores the significance of analyzing open interest alongside technical charts, pointing out key indicators like put-call ratios to determine market positions. Through a comprehensive analysis of stock data and open interest trends, investors can make well-informed decisions. Yet, it is pivotal to remember that multiple factors should influence market strategies, and intraday Open interest (OI) data play crucial roles in understanding market dynamics.
Chapter 4 provides an overview of DL techniques for forecasting stock market trends, examining their effectiveness across different time frames and market conditions. It explores architectures like recurrent neural networks, convolutional neural networks, and transformer-based models, highlighting data preprocessing, feature engineering, and model complexity. Future research directions include hybrid models, exploring alternative data sources, and addressing ethical concerns. This guide is valuable for researchers and practitioners seeking to navigate the evolving landscape of stock market prediction through DL.
Chapter 5 has examined the repercussions of the 2008 financial crisis and the potential of another in 2023, emphasizing the advancements in artificial intelligence (AI) and QuCo for stock market predictions. Techniques like blind QuCo (BQC) and quantum neural networks (QNNs) have emerged, with models designed for precise stock predictions. The chapter’s focus is to analyze and recommend the most accurate AI and QuCo-based algorithms. However, challenges persist, such as limited data, noisy market data, model interpretability, and the need for real-time predictions. Addressing these will pave the way for DL to revolutionize stock price predictions, ensuring enhanced forecasting and risk management.
Chapter 6 has explored the applications and implications of various models for causality, volatility, and co-integration in stock markets. By utilizing models such as the Granger causality, VAR, GARCH, and co-integration models, researchers can analyze and understand the intricate dynamics of financial systems. These models play a pivotal role in understanding causal relationships, predicting volatility, and identifying long-term economic equilibriums in stock markets. Practical applications extend to portfolio management, risk assessment, and guiding investment decisions. The chapter emphasizes the profound impact of these models in advancing the knowledge of finance, offering insights to investors and policymakers, and promoting a deeper comprehension of complex financial interrelationships.
Chapter 7 explains that the financial market is crucial for economic development, with the secondary market dealing with the share market. It offers long-term investment opportunities for investors and is used by small businesses and financial sectors. Stock dealing relies on predictability, which offers superior financial advice and forecasts the direction of the stock market. Techniques like Bayesian models, fuzzy classifiers, artificial neural networks, SVM classifiers, neural networks, and ML have been used to predict the stock market. Whereas AI-based prediction models can guide investors, they may not always account for unexpected occurrences.
Chapter 8 examines the increasing role of AI in stock market trading, highlighting free AI-driven programs that assist traders in making informed decisions. These AI systems enhance the efficiency of stock market operations by providing vast data-driven insights. The research focuses on the current and potential impact of AI in forecasting stock market trends. The study methodology, including data collection and analysis, is meticulously presented, with an exploration of future trajectories and implications of AI applications in stock market research.
Chapter 9 analyzes the relationship between monetary policy decisions and stock returns using an event study methodology. It finds that unexpected changes in policy decisions have the opposite effect on stock returns, emphasizing the importance of market conditions in assessing the relationship. The analysis also shows that the impact of monetary policy on stock returns is not uniform across sectors, emphasizing the need for a sector-specific approach. Financial constraints play a limited role in explaining differences in stock returns’ responses to monetary policy surprises. These findings contribute to the literature by offering diverse insights into the relationship between monetary policy decisions and stock returns in emerging markets.
Chapter 10 emphasizes the significance of AI in predicting stock market movements. Whereas stock market volatility can be daunting for investors, AI’s ability to rapidly process vast datasets and detect patterns offers an edge over traditional prediction methods. The study presents a systematic literature review, highlighting that whereas AI models have shown promise, there is a consistent oversight in selecting and processing input data, which forms the foundation of any predictive model. The research emphasizes the importance of model validation, which is often neglected, and the need for accurate multistep forward predictions. The findings suggest that leveraging advanced AI models can benefit various stakeholders in the financial sector, potentially enhancing confidence and participation in stock trading. This could, in turn, stimulate economic growth, inviting more investment and fostering trust in predictive models among the larger population.
Chapter 11 emphasized that AI is a widely used technology in various sectors, utilizing computer sciences to make decisions and solve problems. It comprises DL and ML, which are often referred to simultaneously. The finance sector is also adopting new technologies to improve operational efficiency. DL and ML approaches share similar principles, but they differ from each other. This book chapter aims to compare and contrast ML and DL strategies to identify their main distinctions. Understanding the benefits, drawbacks, and applicability of each method is crucial before adopting it. The study will provide information on the use of both strategies and their unique advantages for users.
Chapter 12 has explained that the financial market is known for its volatility and unpredictable nature, making accurate stock price predictions challenging. ML techniques, such as Random Forest (RF), k-nearest Neighbours (KNN), SVM, and Naive Bayes, have been used to predict stock values and market trends. This study analyzed various algorithms, including sentiment analysis, time series analysis, and graph-based methods. The results showed that ML algorithms outperform human predictions and save time and resources. To improve stock price prediction, research should prioritize integrating stock trend analysis with historical stock data, generating more accurate and effective stock recommendations. Advanced learning-based techniques can also be used to extract relevant features, improving the accuracy of stock price predictions. Further research should explore the complexities and gradients of networks with numerous nodes, providing insights and potential directions for future research in this area.
Chapter 13 delves into the stock market’s economic impact, investor participation for gain optimization, and risk reduction. Forecasting stock markets proves challenging due to economic uncertainties. The study explores predictive techniques like technical and fundamental analysis, alongside ML. It conducts a thorough systematic review and bibliometric analysis of 89 research works (2002–2023), focusing on fundamental analysis and stock market prediction. It highlights influential authors, institutions, countries, and sources while revealing intellectual structures using bibliographic coupling. ML algorithms, feature selection criteria, training/testing ratios, and accuracy metrics are discussed. Technical indicators and fundamental variables used in forecasting are examined. Overall, the study examines ML algorithms, feature selection, training/testing ratios, accuracy metrics, technical indicators, and variables for market forecasting.
Chapter 14 investigates the influence of emotional intelligence (EI) on investment decisions among Indian investors. Through a survey involving 239 seasoned investors from major metro cities, the research identifies four key factors linking EI with investment choices: attitude, emotions, perception, and risk aversion. The findings emphasize that those with higher EI tend to make wiser investment decisions. The study underscores the importance of fostering EI skills for investment stakeholders, suggesting that recognizing and nurturing these skills can optimize investment outcomes. Future research avenues in diverse cultural contexts are also recommended to expand on this understanding.
Chapter 15 discusses the challenges behavioral finance presents to traditional finance, which emphasizes rational decision-making processes in investments. Focusing on three cognitive biases, i.e., overconfidence, optimism, and the illusion of control, the research utilized a structured questionnaire completed by 362 participants to analyze their impact on financial decisions. The findings reveal a significant correlation between overconfidence and investment choices, whereas optimism and the illusion of control showed no notable influence. Although it offers critical insights into behavioral biases among investors in Delhi/NCR, the study’s scope remains limited. The research underscores the importance of investor awareness of these biases for informed decision-making and has implications for financial advisors, brokerage firms, and stock market policymakers.
Chapter 16 examines the role of alternative data in investment management. This nontraditional, unstructured information offers unique insights but comes with challenges such as data quality, privacy, and lack of standardization. Effective governance, validation, and best practices can enhance the utility of alternative data. The future demands collaboration among stakeholders, advancements in data analysis technologies, evolving regulatory frameworks, and ongoing education for investment professionals. The study’s findings offer guidance to investment firms, data providers, regulatory bodies, and the academic community on harnessing alternative data for better investment decisions and strategies.
Chapter 17 discusses that traditional finance theories rely on rational behavior in investors, focusing on optimizing returns through fundamental analysis, technical analysis, and personal judgment. However, recent research has identified inconsistencies in these theories when applied in practical scenarios. Retail investors in the equity market are prone to various influences, biases, and emotional factors that can impact their decision-making process. Behavioral finance, an interdisciplinary field, aims to understand irrational decision-making by integrating psychological principles and human behavior theories. It examines the influence of investors’ emotions and psychology on their investment decisions, highlighting the importance of understanding how emotions contribute to irrational behavior. Warren Buffet emphasized the need for intellectual acumen and emotional restraint for rational conduct. The field of psychology remains relatively nascent, but it is crucial to examine the dynamics of group behavior within markets and individual investors’ behavioral characteristics to achieve success in investment endeavors.
Chapter 18 examines the concept of volatility and its significance in interconnected financial markets, particularly focusing on its transmission between India’s commodity and equity futures markets. By utilizing various statistical tests and models on data from 2007 to 2022, the study determines the extent of volatility spillover among different indices. Initial results highlight energy’s index as the most volatile, with the Comdex index being the primary volatility transmitter. The findings provide valuable insights for portfolio managers, investors, and policymakers to strategize effectively in the face of volatile market conditions.
In closing, the journey into the realm of DL tools for predicting stock market movements is one of continuous discovery and innovation. As the financial landscape continues to evolve and the boundaries of technology are pushed further, the insights gleaned from this book are meant to be a stepping stone, not an endpoint. The path forward requires ongoing exploration, adaptation, and collaboration among researchers, practitioners, and visionaries. As you delve into the pages that follow, we encourage you to embrace the challenges, embrace the opportunities, and embrace the transformation that these tools can bring to the intricate world of stock market analysis.
Renuka Sharma
Chitkara Business School, Chitkara University, Punjab, India
Kiran Mehta
Chitkara Business School, Chitkara University, Punjab, IndiaJanuary 2024
Acknowledgments
Writing a book is a journey that stretches far beyond the author’s efforts. It is a collaborative endeavor fuelled by inspiration, support, and guidance from countless individuals whose contributions shape the final work. As I reflect on the completion of this book, I am profoundly grateful for the diverse and dedicated community that has enriched my path.
In awe and humility, we extend our thanks to the divine presence that has illuminated our path throughout this creative journey. Your guidance, whether through moments of clarity or challenges, has been our constant source of inspiration and strength.
Heartfelt gratitude goes to the exceptional authors and contributors whose collective insights, expertise, and dedication have brought this book to life. Your willingness to share your knowledge and perspectives has enriched every page, and we are humbled by the collaborative spirit that drove this endeavor.
We extend our appreciation to the dedicated team at Scrivener Publishing LLC, for believing in the potential of this book and for bringing it to fruition with unwavering commitment. Your dedication to quality and your belief in the power of ideas have made this venture a reality, and we are truly thankful for our partnership.
We express gratitude to the insightful critics whose thoughtful perspectives have pushed us to refine and polish this work. Your constructive feedback has been instrumental in shaping the final narrative, and we are thankful for the opportunity to grow and improve through your valuable insights.
With sincere gratitude, we acknowledge the reviewers whose time and expertise were invested in evaluating our book. Your thoughtful analysis and constructive critiques have played a pivotal role in refining the book, ensuring its precision, coherence, and resonance. Your dedication to the thorough review process is deeply valued, and we are thankful for the vital role you’ve played in elevating the quality of this publication.
Renuka Sharma
Kiran Mehta
1Design and Development of an Ensemble Model for Stock Market Prediction Using LSTM, ARIMA, and Sentiment Analysis
Poorna Shankar1*, Kota Naga Rohith2 and Muthukumarasamy Karthikeyan3
1Department of MCA, Indira College of Engineering and Management, Pune, India
2Salesforce Consultant, Essen, Germany
3Chemical Engineering and Process Development, National Chemical Laboratory, Pune, India
Abstract
The accurate prediction of stock market movements is a challenging task due to its complex and dynamic nature. In recent years, machine learning techniques have shown promise in addressing this challenge. This study focuses on the design and development of an ensemble model that combines long short-term memory (LSTM), autoregressive integrated moving average (ARIMA), and sentiment analysis to enhance stock market predictions. The ensemble model leverages the strengths of LSTM, which captures long-term dependencies in sequential data, and ARIMA, a statistical model known for its ability to capture linear and autoregressive relationships in time series data. Additionally, sentiment analysis is incorporated to analyze and quantify the impact of public sentiment expressed in textual data on stock market dynamics. The research methodology involves collecting historical stock market data, sentiment analysis data, and performing preprocessing steps to ensure data quality. The LSTM and ARIMA models are developed and trained using the collected data. Sentiment analysis techniques are applied to extract sentiment scores from the effect of public sentiment on stock market movements generated through Twitter application programming interface (API). The ensemble model is developed by fusing the predictions from LSTM, ARIMA, and sentiment analysis, with careful consideration of the weights assigned to each model. The integration of sentiment analysis enriches the model by incorporating qualitative features derived from public sentiments. Experimental results using real-world historical stock market data demonstrate the effectiveness of the ensemble model. It outperforms the individual LSTM and ARIMA models in terms of accuracy and robustness. The inclusion of sentiment analysis further enhances the prediction performance by capturing the influence of public sentiment on stock market dynamics. The ensemble approach effectively leverages the strengths of each component to improve prediction accuracy and adaptability. The results of this study demonstrated that there is a high correlation between stock price rises and falls and public sentiment expressed in tweets. The findings offer valuable insights for investors, financial institutions, and policymakers, aiding in informed decision-making in the dynamic stock market environment.
Keywords: LSTM, ARIMA, NLP, sentiment analysis, RNN, stock market prediction
1.1 Introduction
Trading in the stock market and getting returns from it have become popular nowadays around the world. Predicting stock price has long been considered one of the most delicate yet critical undertakings. Understanding and predicting the behavior of stock prices have been the focus of extensive research and analysis for decades. The stock market is a dynamic and intricate system influenced by a multitude of factors, including economic indicators, company performance, geopolitical events, and investor sentiment. The stock market’s prices are highly volatile. According to the efficient market hypothesis (EMH), financial market movements are influenced by news, tweets, and other factors, all of which have a substantial impact on a company’s stock. Because the stock request is a nonlinear and dynamic system, sentiment values in tweets are one of the most important factors in the monetary request [1–4].
As a supplement to traditional stock market data, the development of news, blogs, social networking websites, and textual content on the Internet provides a valuable source to reflect attitudes and predict stock values. It is a difficult process to identify the collection of significant criteria for creating correct predictions; thus, regular stock market study is critical. Stock market values follow a random walk pattern and cannot be anticipated with more than 50% accuracy because of the sheer unpredictability of news and current events. Investment opportunities for individual investors or business opportunities in the stock market will increase if there is an efficient algorithm that can predict the short-term price of a stock of a certain company with the news/tweets around that company [5–7].
The main objective of this study is to give future stock price insights with deep learning algorithms and prevent future losses for investors and investment companies by providing accurate results using sentiment analysis.
1.2 Significance of the Study
Predicting stock market movements accurately is a challenging task due to the complex and volatile nature of financial markets. The relationship between stock price movements and public sentiment has long been a topic of interest in financial research. Traditionally, financial analysts relied on economic indicators, company performance, and market trends to forecast stock prices. In particular, the explosion of social media platforms, such as Twitter, has led to a vast amount of user-generated content being shared in real time. This content often reflects individuals’ thoughts, opinions, and sentiments on various topics, including the stock market. As a result, researchers have increasingly explored the relationship between public sentiment expressed in tweets and stock price movements.
According to the EMH, financial market movements are influenced by news, tweets, and other factors, all of which have a substantial impact on a company’s stock. Stock market values follow a random walk pattern and cannot be anticipated with more than 50% accuracy because of the sheer unpredictability of news and current events. Many studies have used Twitter as a primary source for public opinion research [8, 9].
In addition, this study will fill a gap in the scientific stock market literature by predicting stock market movements utilizing a variety of current news articles and tweets from various sources. In addition, a variety of machine and deep learning algorithms are employed to create models that can forecast stock market behavior. These models are tested using a variety of datasets, sentiment analysis methods, and whether or not a technical price indicator is used. As a result, the datasets and sentiment analysis methods are compared, and the accuracy of news article sentiment prediction is evaluated.
In recent years, machine learning techniques have gained significant attention as a promising approach to tackle this challenge. Among these techniques, long short-term memory (LSTM), autoregressive integrated moving average (ARIMA), and sentiment analysis have emerged as powerful tools for stock market prediction. This research focuses on the design and development of an ensemble model that combines these three approaches to improve the accuracy and robustness of stock market predictions [10].
LSTM, a type of recurrent neural network (RNN), is well suited for capturing temporal dependencies and patterns in sequential data. In the context of stock market prediction, LSTM can analyze historical stock market data, identify intricate patterns, and make predictions based on learned patterns. By leveraging the memory and hidden state of LSTM, the model can capture long-term dependencies and nonlinear relationships present in the stock market data [11].
ARIMA, on the other hand, is a classical statistical model widely used in time series analysis. It is particularly effective in capturing the linear and autoregressive nature of stock market data. By incorporating ARIMA as a component of the ensemble model, we can harness its strengths in modeling the time series aspects of stock prices and augment the predictive power of the overall model [12].
In addition to LSTM and ARIMA, sentiment analysis plays a crucial role in understanding the influence of public sentiment on stock market dynamics. The sentiment values of tweets will be tested to check whether it has an impact on stock request movements. Investors are veritably interested in the exploration area of stock price forecasts since the stock demand has gotten a lot of attention. Investors and investment businesses have become a popular choice for stock forecasts for secure investment results as a result of the stock request’s irregularities.
Sentiment analysis involves the application of natural language processing (NLP) techniques to analyze textual data, such as news articles, social media posts, and financial reports, to determine the sentiment expressed within them. This study will fill the gap in the scientific stock market literature by predicting stock market movements utilizing a variety of current news articles and tweets from various sources. The task of assessing a text’s viewpoint as good, negative, or neutral is known as sentiment categorization. The information gleaned from tweets is extremely important in generating predictions. By quantifying sentiment scores associated with the stock-related textual data, we can capture the effect of public sentiment on stock price movements [5].
The proposed ensemble model combines the strengths of LSTM, ARIMA, and sentiment analysis to improve stock market predictions. This study aims to contribute to the field of text analytics in forecasting using tweet sentiment and predicting stock market movements with statistical models such as ARIMA with artificial neural network (ANN) and LSTM.
The integration of these approaches aims to leverage the advantages of each technique, addressing their individual limitations and creating a more robust and accurate prediction framework. By combining the quantitative features captured by LSTM and ARIMA with the qualitative information derived from sentiment analysis, the ensemble model can potentially capture a broader range of factors that impact stock prices.
This research aims to design and develop an ensemble model for stock market prediction using LSTM, ARIMA, and sentiment analysis. Real-world historical stock market data will be used to evaluate the performance of the ensemble model against individual LSTM and ARIMA models. The study will also assess the impact of incorporating sentiment analysis on prediction accuracy and reliability. The predicted outcome of the study is to create a highly accurate algorithm that determines the correlation between sentimental analysis and the respective stock price.
The outcomes of this research have significant implications for investors, financial institutions, and policymakers. Accurate stock market predictions can assist investors in making informed decisions, financial institutions in managing risks, and policymakers in formulating effective economic policies. The ensemble model, combining LSTM, ARIMA, and sentiment analysis, represents a comprehensive and innovative approach to enhance stock market prediction capabilities and contribute to a deeper understanding of the intricate dynamics of financial markets.
1.3 Problem Statement
Accurately predicting stock market movements is a complex and challenging task, primarily due to the dynamic and unpredictable nature of financial markets. Traditional models and techniques often struggle to capture the intricate patterns, nonlinear relationships, and the influence of external factors on stock prices. Additionally, the emergence of social media platforms has introduced a new dimension to stock market analysis, with public sentiment expressed in platforms like Twitter showing a strong correlation with stock price rises and falls. Investment opportunities for individual investors or business opportunities in the stock market will increase if there is an efficient algorithm that can predict the short-term price of a stock of a certain company with the news or tweets around that company. However, effectively incorporating sentiment analysis into existing prediction models remains a significant challenge.
1.4 Research Objectives
The main objective of this study is to provide future stock price insights using ML algorithms and prevent future losses for investors and investment companies by providing accurate results using sentiment analysis by executing the following process.
Develop an ensemble model combining LSTM, ARIMA, and sentiment analysis to improve stock market prediction accuracy.
Investigate the impact of incorporating sentiment analysis on stock market predictions.
Evaluate and compare the performance of the ensemble model against individual LSTM and ARIMA models using real-world data.
Analyze the strengths and limitations of the ensemble model to identify areas for improvement.
Provide valuable insights for investors, financial institutions, and policymakers to support informed decision-making in the stock market.
1.5 Expected Outcome
The research will result in the design and development of an ensemble model that incorporates LSTM and ARIMA for time series analysis of stock market data and sentiment analysis techniques to analyze public sentiment expressed on Twitter.
This model will effectively leverage the strengths of each component to improve stock market prediction accuracy and robustness.
This analysis will provide insights into the factors contributing to the model’s success and identify any potential limitations or challenges associated with incorporating social media data.
The findings will enhance our understanding of the relationship between Twitter sentiment and stock market dynamics, providing practical implications for decision-making in the financial industry.
1.6 Chapter Summary
The research study is narrated in five chapters. The first chapter provides an overview of the research topic, highlighting the challenges of stock market prediction and the potential of machine learning techniques. It introduces the concept of ensemble modeling, which combines LSTM, ARIMA, and sentiment analysis to improve prediction accuracy. The significance of this research, problem statement, research objectives, and the importance of this research in enhancing stock market predictions are discussed.
The second chapter reviews relevant literature on stock market prediction, LSTM, ARIMA, and sentiment analysis. It explores previous studies that have examined the correlation between public sentiment expressed in tweets and stock price movements. The chapter also discusses the strengths and limitations of LSTM, ARIMA, and sentiment analysis, as well as existing research on ensemble modeling for stock market prediction.
The third chapter outlines the design and development of the ensembled model. It describes the data collection process, including the acquisition of historical stock market data, sentiment analysis data, and the preprocessing steps applied to ensure data quality. The chapter provides a detailed explanation of LSTM and ARIMA models, including their architectures and training procedures. It also describes the sentiment analysis techniques used to extract sentiment scores from textual data.
The fourth chapter presents the development of the ensemble model that integrates LSTM, ARIMA, and sentiment analysis. It outlines the fusion approach used to combine the predictions from the individual models, emphasizing how the weights are determined to optimize the ensemble’s performance. The chapter also discusses the integration of sentiment analysis into the ensemble model and the methods employed to combine the sentiment features with the quantitative features derived from LSTM and ARIMA.
The fifth chapter discusses the experimental results obtained from applying the ensemble model to real-world historical stock market data that are presented and analyzed in this chapter. The performance of the ensemble model is compared against the individual LSTM and ARIMA models. Various evaluation metrics, such as accuracy, precision, recall, and F1 score, are used to assess the predictive capabilities of the ensemble model. The impact of incorporating sentiment analysis on prediction accuracy is also examined.
The findings of the research are discussed in detail in the sixth chapter. The strengths and limitations of the ensemble model are highlighted, along with the potential reasons for its performance. The implications of the research for stock market prediction and the insights gained from the integration of LSTM, ARIMA, and sentiment analysis are discussed. The chapter concludes with a summary of the research outcomes and suggestions for future work to enhance the ensemble model and further advance stock market prediction capabilities.
1.7 Theoretical Foundation
1.7.1 Sentiment Analysis
The practice of analyzing and interpreting the remarks, thoughts, and emotions made by people with emotional tendencies is known as sentiment analysis. For mining online reviews, sentiment analysis technology has been frequently deployed. The findings can aid firms in making changes to their future marketing strategy, such as examining the benefits and drawbacks of items from multiple perspectives in order to improve product quality and customized suggestions [2].
Due to their unpredictable nature and the large amount of noise and variables involved, financial markets are one of the most difficult systems to anticipate. For years, complex statistical approaches have been employed to forecast the stock market, allowing for the creation of robust prediction models with low error [4].
1.7.1.1 Subjectivity
Subjectivity refers to the degree to which a piece of text expresses subjective or opinionated content rather than objective facts. It represents the subjective perspective, beliefs, emotions, or personal opinions of the author. Text that contains subjective content often involves expressions of sentiments, emotions, evaluations, or judgments. The degree to which a person is personally connected with an object is referred to as subjectivity. Personal connections and individual experiences with that object are most important here, which may or may not differ from someone else’s perspective. For example, the sentence “I am really delighted with my new smartphone because it offers the best performance on the market,” has strong subjectivity. The user is actually talking about his experience and how he feels about an object, therefore the phrase is plainly subjective [3].
1.7.1.2 Polarity
Polarity, on the other hand, refers to the strength of the sentiment or emotional orientation expressed in the text. It indicates whether the sentiment conveyed is positive, negative, or neutral. Polarity classification assigns a sentiment label to a given text, indicating the overall sentiment polarity of the content. For example, a positive polarity indicates a positive sentiment such as trust, love, or admiration, whereas a negative polarity indicates a negative sentiment like “I don’t think I will buy this item.” Neutral polarity is assigned when no clear positive or negative sentiment is expressed [4].
Several studies have explored the potential of sentiment analysis of Twitter data to improve stock market prediction. The use of sentiment analysis, which involves extracting and analyzing emotions, opinions, and attitudes from text data, has shown promise in capturing market sentiment and predicting stock price movements.
The tweets were classified into three levels of polarity using sentiment analysis on each of the hashtag datasets: N, NEU, and P. Negative polarity (N) is formed for a negative view in the text, whereas positive polarity (P) is generated for a favorable opinion. For neutral opinion (NEU) or when the polarity cannot be estimated, NEU polarity is generated. The weights attributed to the standard polarities are shown in Table 1.1. Polarities in the NEU category tweets are given a 0 (zero) weight because they contain impartial opinions regarding the concept or thing. Negative opinion tweets (N) were given a “−1” weighting based on the magnitude of the negative sentiment. Positive opinion tweets (P) receive “+1,” respectively. A sentiment dictionary is essential for recognizing sentiment tokens in any document during sentiment analysis [5].
Figure 1.1 Representation of share value as per sentiments.
Source: [39].
Table 1.1 Representation of sentiments polarity and weight.
Polarity
N
NEU
P
Weight
−1
0
+1
This study’s results can assist investors in anticipating and predicting whether their investment will be profitable or lose money, as well as preventing large margin losses and keeping their stock investments on track.
1.7.2 Stock Market
Stock price forecasting has recently gained more attention in the financial industry. The continued use of the Internet in the modern age has reached extraordinary levels, which suggests it may have something to do with stock price behavior. The aim is to detect association patterns and use them to predict how different stock prices will perform in the future [6]. Undoubtedly, even when individually boring, aggregated tweets can provide a satisfying representation of public attitudes. Today, a large volume of data, containing information on many topics, is transmitted online through various sources. A good example is Twitter, where more than 400 million tweets are sent daily. Whereas each tweet may not be meaningful as a unit, a large collection of them can provide valuable data on general opinions on a particular topic [7]. Assessing public sentiment by retrieving online information from Twitter can be helpful in developing trading strategies. Accurately predicting stock price movements depends on many factors, and certainly public sentiment is included [8].
The wealth of the inventory marketplace can sell the boom of purchaser demand. However, in different circumstances, the common fluctuations in inventory fees have led to the growth of macroeconomic uncertainty. Especially after experiencing unheard-of big fluctuations inside the A-proportion marketplace in 2015, the self-assurance of purchasers has been substantially undermined, which is not conducive to reaching the intention of increasing home demand. Therefore, many pupils have performed studies on inventory forecasting. Cheng et al. use historical stock market data and technical indicators to predict future stock price movements using an attention-based long-term memory model [9].
1.7.3 Sentiment Analysis of Twitter in Stock Market Prediction
Several studies have explored the potential of sentiment analysis of Twitter data to improve stock market prediction. The use of sentiment analysis, which involves extracting and analyzing emotions, opinions, and attitudes from text data, has shown promise in capturing market sentiment and predicting stock price movements. Bollen et al. conducted a pioneering study that demonstrated a correlation between Twitter sentiment and stock market performance. By analyzing the collective mood expressed in tweets, they found that changes in Twitter mood could predict changes in the Dow Jones Industrial Average. Their research highlighted the potential of sentiment analysis in capturing public sentiment and its relevance to stock market dynamics [10].
Building on this work, Sprenger et al. focused on sentiment analysis of individual stocks in the year 2016. They found that sentiment scores derived from tweets related to specific stocks exhibited a positive correlation with future stock returns. The study concluded that sentiment analysis of stock-specific tweets could provide valuable signals for predicting stock price movements [11].
Zhang et al. examined sentiment polarity in tweets and its impact on stock market prediction. Their research revealed that the volume of positive and negative tweets about particular stocks was significantly correlated with future stock returns. This finding indicated the predictive power of sentiment analysis in capturing investor sentiment and incorporating it into stock market predictions [13].
In a more recent study, Chen et al. combined sentiment analysis with machine learning techniques to improve stock market prediction accuracy. By integrating sentiment analysis features into their predictive model, they achieved superior forecasting performance compared to traditional models. The study highlighted the benefits of leveraging sentiment analysis to capture the influence of public sentiment on stock price movements [14].
Overall, the literature suggests that sentiment analysis of Twitter data can be a valuable tool in stock market prediction. By capturing and analyzing public sentiment expressed on Twitter, sentiment analysis provides insights into market sentiment, investor sentiment, and collective opinions, which can enhance the accuracy and robustness of stock market forecasts.
1.7.4 Machine Learning Algorithms in Stock Market Prediction
Machine learning has emerged as a powerful tool for stock market prediction due to its ability to analyze vast amounts of data, identify patterns, and make predictions based on historical trends. The application of deep learning, particularly convolutional neural networks (CNNs) and RNNs, has gained attention in stock prediction. Wang et al
