Comparative Study of Long Short-Term Memory (LSTM) and Quantum Long Short-Term Memory (QLSTM): Prediction of Stock Market Movement

TL;DR

This study compares LSTM and QLSTM models in predicting the Karachi Stock Exchange index, demonstrating QLSTM's potential as a more effective method in complex economic environments.

Contribution

It introduces the application of quantum LSTM to stock prediction and compares its performance with traditional LSTM using real-world data.

Findings

QLSTM outperforms LSTM in prediction accuracy

QLSTM shows promise in complex economic conditions

Both models effectively predict stock index trends

Abstract

In recent years, financial analysts have been trying to develop models to predict the movement of a stock price index. The task becomes challenging in vague economic, social, and political situations like in Pakistan. In this study, we employed efficient models of machine learning such as long short-term memory (LSTM) and quantum long short-term memory (QLSTM) to predict the Karachi Stock Exchange (KSE) 100 index by taking monthly data of twenty-six economic, social, political, and administrative indicators from February 2004 to December 2020. The comparative results of LSTM and QLSTM predicted values of the KSE 100 index with the actual values suggested QLSTM a potential technique to predict stock market trends.