상세정보
학술대회
Forecasting Daily Stock Trends Using Random Forest Optimization
- 발행일
- 201910
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.1152-1155
- 협약과제
- 19PS2500, 빅데이터 및 AI 기반의 투자 및 자산관리 지원 서비스 시스템 개발, 박지상
- 초록
- This paper introduces a new approach to forecast daily stock trends using the random forest technique. This study intends to include as many features as possible to hopefully describe various aspects of stock market trends. A number of features are selected for forecasting the trends of stock prices. The new algorithm adjusts optimal learning parameters during the data training process. The usefulness of the proposed algorithm is demonstrated by processing two stock datasets while analyzing its forecasting accuracy. Additional several technical issues for future implementations and analysis are suggested.
- KSP 제안 키워드
- Learning parameters, Market trends, New approach, Optimal learning, Random forest, Stock Market, Stock prices, forecasting accuracy, new algorithm, technical issues, training process