Research model on stock price prediction system through real-time Macroeconomics index and stock news mining analysis = 실시간 거시지표 예측과 증시뉴스 마이닝을 통한 주가 예측시스템 모델연구

다국어 초록 (Multilingual Abstract)

As the global economy stagnated due to the Corona 19 virus from Wuhan, China, most countries, including the US Federal Reserve System, introduced policies to boost the economy by increasing the amount of money. Most of the stock investors tend to invest only by listening to the recommendations of famous YouTubers or acquaintances without analyzing the financial statements of the company, so there is a high possibility of the loss of stock investments. Therefore, in this research, I have used artificial intelligence deep learning techniques developed under the existing automatic trading conditions to analyze and predict macro-indicators that affect stock prices, giving weights on individual stock price predictions through correlations that affect stock prices. In addition, since stock prices react sensitively to real-time stock market news, a more accurate stock price prediction is made by reflecting the weight to the stock price predicted by artificial intelligence through stock market news text mining, providing stock investors with the basis for deciding to make a proper stock investment.

참고문헌 (Reference)

1 신석하, "외국인 투자자 주식매매와 국내외 거시경제요인의 관계 분석" 한국국제경영관리학회 17 (17): 89-107, 2013

2 M. S. Mahmud, "Time series stock price prediction using recurrent error based neuro-fuzzy system with momentum" 1-4, 2014

3 S. Selvin, "Stock price prediction using LSTM, RNN and CNN-sliding window model" IEEE 1643-1647, 2017

4 G. P. C. Fung, "Stock prediction: Integrating text mining approach using real-time news" IEEE 395-402, 2003

5 홍성혁, "Research on Stock price prediction system based on BLSTM" 한국융합학회 11 (11): 19-24, 2020

6 L. R. Medsker, "Recurrent neural networks" 5 : 2001

7 신동하, "RNN과 LSTM을 이용한 주가 예측율 향상을 위한 딥러닝 모델" 한국정보기술학회 15 (15): 9-16, 2017

8 Y. Qian, "On the training aspects of deep neural network(DNN)for parametric TTS synthesis" IEEE 3829-3833, 2014

9 K. Guru-Gharan, "Influences of selected macroeconomic variables on US stock market returns and their predictability over varying time horizons" 13 (13): 13-, 2009

10 M. F. Elhusseiny., "Industries Stock Return Reactions To Risk Factors : An Empirical Investigation On The G-7 Countries" 4 (4): 196-204, 2017

11 R. Pascanu, "How to construct deep recurrent neural networks"

12 Tsung-Jung Hsieh, "Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm" Elsevier BV 11 (11): 2510-2525, 2011

13 I. Goodfellow, "Deep learning (Vol. 1, No. 2)" MIT press 2016

14 J. Mao, "Deep captioning with multimodal recurrent neural networks(m-rnn)"

15 홍성혁, "A study on stock price prediction system based on text mining method using LSTM and stock market news" 한국디지털정책학회 18 (18): 223-228, 2020