Deep Learning Tools for Predicting Stock Market Movements

5Artificial Intelligence and Quantum Computing Techniques for Stock Market Predictions

Rajiv Iyer^1* and Aarti Bakshi²

¹Computer Science and Engineering, Amity University, Mumbai, India

²Department of Electronics and Telecommunication, K.C. College of Engineering and Management Studies and Research, Thane, Maharashtra, India

Abstract

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. Overall, these techniques leverage ...