Stock Price forecasting based on Quantum Particle swarm optimization

Guohui Huang,Hunan University Of Science And Engineering

doi:10.47191/ijsshr/v4-i10-29

Guohui Huang, Hunan University Of Science And Engineering

Open Access

https://doi.org/10.47191/ijsshr/v4-i10-29

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Abstract

The stock market is very volatile, so the change of the stock price is also widely concerned by investors. In this paper, a new stock price forecasting model based on Quantum Particle Swarm Optimization(QPSO) , Quantum Bee Colony Optimization Algorithm(QABC) and Quantum Fruit Fly Optimization Algorithm (QFOA) is proposed. The three methods all use BP neural network to adjust the parameters of particle swarm, bee colony and Drosophila to reach the optimal parameters. Taking the daily closing price of CITIC Securities and Tianfeng Securities, a large-scale and a small-scale securities company, as the object of empirical analysis, comparing the accuracy of the three methods in predicting stocks, it also analyzes whether the size of the company has an effect on the accuracy of the model. The results show that the prediction effect of qpso is the best, and the size of the company has some influence on the prediction effect.

Highlights

For a long time, the prediction of the stock price has been a hot topic in finance, so when studying the stock prediction, the former scholars established a lot of prediction models, and the latter scholars basically improved the models on the basis of the former scholars, or to add innovative elements to the original model, so that the model has higher accuracy
A new stock price forecasting model based on Quantum Particle Swarm Optimization(QPSO), Quantum Bee Colony Optimization Algorithm(QABC) and Quantum Fruit Fly Optimization Algorithm (QFOA) is proposed
The results show that the prediction effect of qpso is the best, and the size of the company has some influence on the prediction effect

Summary

Introduction

The prediction of the stock price has been a hot topic in finance, so when studying the stock prediction, the former scholars established a lot of prediction models, and the latter scholars basically improved the models on the basis of the former scholars, or to add innovative elements to the original model, so that the model has higher accuracy. Stock Price Forecasting Based on Quantum Particle Swarm Optimization

Results

Conclusion