@article{ART003104513},
author={Jin-Hyeon Joo and Geun-Duk Park},
title={KOSPI index prediction using topic modeling and LSTM},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={7},
pages={73-80},
doi={10.9708/jksci.2024.29.07.073}
TY - JOUR
AU - Jin-Hyeon Joo
AU - Geun-Duk Park
TI - KOSPI index prediction using topic modeling and LSTM
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 7
PB - The Korean Society Of Computer And Information
SP - 73
EP - 80
SN - 1598-849X
AB - In this paper, we proposes a method to improve the accuracy of predicting the Korea Composite Stock Price Index (KOSPI) by combining topic modeling and Long Short-Term Memory (LSTM) neural networks. In this paper, we use the Latent Dirichlet Allocation (LDA) technique to extract ten major topics related to interest rate increases and decreases from financial news data. The extracted topics, along with historical KOSPI index data, are input into an LSTM model to predict the KOSPI index.
The proposed model has the characteristic of predicting the KOSPI index by combining the time series prediction method by inputting the historical KOSPI index into the LSTM model and the topic modeling method by inputting news data. To verify the performance of the proposed model, this paper designs four models (LSTM_K model, LSTM_KNS model, LDA_K model, LDA_KNS model) based on the types of input data for the LSTM and presents the predictive performance of each model. The comparison of prediction performance results shows that the LSTM model (LDA_K model), which uses financial news topic data and historical KOSPI index data as inputs, recorded the lowest RMSE (Root Mean Square Error), demonstrating the best predictive performance.
KW - Topic Modeling;LSTM;Machine Learning;Predictive Modeling;LDA
DO - 10.9708/jksci.2024.29.07.073
ER -
Jin-Hyeon Joo and Geun-Duk Park. (2024). KOSPI index prediction using topic modeling and LSTM. Journal of The Korea Society of Computer and Information, 29(7), 73-80.
Jin-Hyeon Joo and Geun-Duk Park. 2024, "KOSPI index prediction using topic modeling and LSTM", Journal of The Korea Society of Computer and Information, vol.29, no.7 pp.73-80. Available from: doi:10.9708/jksci.2024.29.07.073
Jin-Hyeon Joo, Geun-Duk Park "KOSPI index prediction using topic modeling and LSTM" Journal of The Korea Society of Computer and Information 29.7 pp.73-80 (2024) : 73.
Jin-Hyeon Joo, Geun-Duk Park. KOSPI index prediction using topic modeling and LSTM. 2024; 29(7), 73-80. Available from: doi:10.9708/jksci.2024.29.07.073
Jin-Hyeon Joo and Geun-Duk Park. "KOSPI index prediction using topic modeling and LSTM" Journal of The Korea Society of Computer and Information 29, no.7 (2024) : 73-80.doi: 10.9708/jksci.2024.29.07.073
Jin-Hyeon Joo; Geun-Duk Park. KOSPI index prediction using topic modeling and LSTM. Journal of The Korea Society of Computer and Information, 29(7), 73-80. doi: 10.9708/jksci.2024.29.07.073
Jin-Hyeon Joo; Geun-Duk Park. KOSPI index prediction using topic modeling and LSTM. Journal of The Korea Society of Computer and Information. 2024; 29(7) 73-80. doi: 10.9708/jksci.2024.29.07.073
Jin-Hyeon Joo, Geun-Duk Park. KOSPI index prediction using topic modeling and LSTM. 2024; 29(7), 73-80. Available from: doi:10.9708/jksci.2024.29.07.073
Jin-Hyeon Joo and Geun-Duk Park. "KOSPI index prediction using topic modeling and LSTM" Journal of The Korea Society of Computer and Information 29, no.7 (2024) : 73-80.doi: 10.9708/jksci.2024.29.07.073
