A Long-Term Funds Predictor Based on Deep Learning

Shuiyi Kuang, Yan Zhang

2023

Abstract

Numerous neural network models have been created to predict the rise or fall of stocks since deep learning has gained popularity, and many of them have performed quite well. However, since the share market is hugely influenced by various policy changes or unexpected news, it is challenging for investors to use such short-term predictions as a guide. In this paper, a suitable long-term predictor for the funds market is proposed and tested using different kinds of neural network models, including the Long Short-Term Memory (LSTM) model with different layers, the Gated Recurrent Units (GRU) model with different layers, and the combination model of LSTM and GRU. These models were evaluated on two funds datasets with various stock market technical indicators added. Since the fund is a long-term investment, we attempted to predict the range of change in the future 20 trading days. The experimental results demonstrated that the single GRU model performed best, reached an accuracy of 92.14% to correctly predict the direction of rise or fall, and the accuracy of predicting the specific change also hit 85.35%.

Paper Citation

in Harvard Style

Kuang S. and Zhang Y. (2023). A Long-Term Funds Predictor Based on Deep Learning. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR; ISBN 978-989-758-671-2, SciTePress, pages 347-354. DOI: 10.5220/0012206400003598

in Bibtex Style

@conference{kdir23,
author={Shuiyi Kuang and Yan Zhang},
title={A Long-Term Funds Predictor Based on Deep Learning},
booktitle={Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR},
year={2023},
pages={347-354},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012206400003598},
isbn={978-989-758-671-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR
TI - A Long-Term Funds Predictor Based on Deep Learning
SN - 978-989-758-671-2
AU - Kuang S.
AU - Zhang Y.
PY - 2023
SP - 347
EP - 354
DO - 10.5220/0012206400003598
PB - SciTePress