인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2026.3
- 수록면
- 658 - 666 (9page)
- DOI
- 10.5370/KIEE.2026.75.3.658
이용수
초록· 키워드
Forecasting dynamics in financial markets remains a central yet unresolved challenge due to their inherent volatility, nonlinear dependence, and the presence of structural breaks and seasonality. The accurate modeling of stock price movements is not only of theoretical significance but also of considerable practical relevance for investment strategy design, portfolio optimization and systemic risk management. In this study, we investigated a comprehensive comparative analysis of forecasting techniques, encompassing both classical statistical models and modern machine learning approaches specifically adapted for time series prediction. Traditional econometric methods, as generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive integrated moving average (ARIMA) were systematically evaluated alongside conventional machine learning algorithms, including linear regression and support vector machines (SVM). Beyond these baselines, we assessed the predictive capacity of advanced neural network architectures, with particular emphasis on long short-term memory (LSTM) networks and convolutional neural networks (CNN), which are designed to capture long-range temporal dependencies and nonlinear feature interactions. Empirical experiments conducted on real stock market datasets allow for a rigorous performance assessment under diverse market regimes. The results demonstrated differentiated strengths across methods, where statistical models retain interpretability and robustness, while deep learning approaches yield superior accuracy in highly volatile environments. The study concludes with evidence-based recommendations concerning methodological suitability for varying forecasting horizons and financial application scenarios.
#Artificial Intelligence
#Financial Decision-Making
#Predictive Modeling
#Business Process Model and Notation
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목차
- Abstract
- 1. Introduction
- 2. Literature Review and Used Algorithms
- 3. Methodology
- 4. Data Collection
- 5. Results and Discussion
- 6. Conclusion
- References
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