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...