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학술대회 Forecasting Daily Stock Trends Using Random Forest Optimization
Cited 3 time in scopus Download 6 time Share share facebook twitter linkedin kakaostory
저자
박지상, 조현성, 이지성, 정교일, 김정민, 김동진
발행일
201910
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2019, pp.1152-1155
DOI
https://dx.doi.org/10.1109/ICTC46691.2019.8939729
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.
키워드
cross validation, KOSPI, machine learning, parameter optimization, random forest, stock price
KSP 제안 키워드
Cross validation(CV), Learning parameters, Market trends, New approach, Optimal learning, Parameter optimization, Random forest, Stock Market, Stock prices, forecasting accuracy, machine Learning