Business

Introducing an Innovative Approach to Mitigate Investment Risk in Financial Markets: A Case Study of Nikkei 225

X. Duan

Discover a cutting-edge hybrid model for stock price forecasting! This research, conducted by Xiao Duan, leverages the Nikkei 225 index data from 2013 to 2022, combining Support Vector Regression with innovative optimization techniques. With MFO-SVR achieving an impressive MAPE of 0.70, this work aims to revolutionize investment risk mitigation.

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Playback language: English

Index

Abstract

This research introduces a hybrid stock price forecasting model using Nikkei 225 index data from 2013 to 2022 to mitigate investment risks. The model integrates Support Vector Regression (SVR) with multiple optimization approaches: Moth Flame Optimization (MFO), Artificial Bee Colony (ABC), and Genetic Algorithms (GA). MFO-SVR demonstrated the highest accuracy, achieving a Mean Absolute Percentage Error (MAPE) of 0.70. The model's accuracy was evaluated using MAE, MAPE, MSE, and RMSE.

Publisher

(IJACSA) International Journal of Advanced Computer Science and Applications

Published On

Mar 01, 2024

Authors

Xiao Duan

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