Stock Price Prediction based on the Improved Flower Pollination Algorithm Optimizing BP Neural Network

DOI

10.2991/978-94-6463-304-7_26How to use a DOI?

Keywords

Improved Flower Pollination Optimization Algorithm; BP Neural Network; Stock Price; Prediction

Abstract

This research introduces a predictive model, IFPA-BP, for stock price forecasting that optimizes the BP neural network weights and biases using the Improved Flower Pollination Optimization Algorithm (IFPA). We addressed the traditional inflexibility between global and local searches by introducing the concepts of adaptive conversion probability and temperature. To tackle the issue of population diversity, a chaotic reverse initialization strategy was employed, significantly reducing the local optimum challenges common with conventional flower pollination algorithms. The efficacy of IFPA was demonstrated using five benchmark functions. We subsequently used the IFPA-BP model to forecast the stock prices of Guoxin Securities. Notably, the IFPA-BP's MSE, MAPE, MAE, and RMSE metrics outperformed those of the traditional BP model, suggesting superior forecasting ability and providing valuable insights for financial investments.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Pengkai Wang
PY  - 2023
DA  - 2023/12/04
TI  - Stock Price Prediction based on the Improved Flower Pollination Algorithm Optimizing BP Neural Network
BT  - Proceedings of the 3rd International Conference on Digital Economy and Computer Application (DECA 2023)
PB  - Atlantis Press
SP  - 240
EP  - 249
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-304-7_26
DO  - 10.2991/978-94-6463-304-7_26
ID  - Wang2023
ER  -