Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions

Chapter 5 Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions Rajiv Iyer, Rajiv Iyer Computer Science and Engineering, Amity University, Mumbai, IndiaSearch for more papers by this authorAarti Bakshi, Aarti Bakshi Department of Electronics and Telecommunication, K.C. College of Engineering and Management Studies and Research, Thane, Maharashtra, IndiaSearch for more papers by this author Rajiv Iyer, Rajiv Iyer Computer Science and Engineering, Amity University, Mumbai, IndiaSearch for more papers by this authorAarti Bakshi, Aarti Bakshi Department of Electronics and Telecommunication, K.C. College of Engineering and Management Studies and Research, Thane, Maharashtra, IndiaSearch for more papers by this author Book Editor(s):Renuka Sharma, Renuka Sharma Chitkara Business School, Chitkara University, Punjab, IndiaSearch for more papers by this authorKiran Mehta, Kiran Mehta Chitkara Business School, Chitkara University, Punjab, IndiaSearch for more papers by this author First published: 09 April 2024 https://doi.org/10.1002/9781394214334.ch5 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onEmailFacebookTwitterLinkedInRedditWechat Summary The financial crisis of 2008 had far-reaching effects on the world economy. Repercussions of this event are seen today in the Indian economy. Fast forward to 2022, we are looking at another impending crisis in 2023 on similar lines. The thing that is different this time around is that progress in the fields of artificial intelligence and quantum computing has reached a level where we can make predictions in the stock market. This will, in turn, help us make informed decisions thus preventing losses at every level. In the fields of statistics and finance, the stock market is considered a complex nonlinear dynamic system with multiple variables. Various techniques have been developed to analyze and predict stock market behavior. Two such techniques are blind quantum computing (BQC) and quantum neural networks (QNNs). These techniques have been explored and studied in the context of stock price prediction and financial engineering applications. Researchers have developed models and algorithms, such as a quantum artificial neural network for stock closing price prediction and a hybrid deep QNN for financial predictions. 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