This paper systematically reviews three categories of key indicators influencing stock price fluctuations: technical, fundamental, and investor sentiment indicators, and demonstrates that combination models significantly enhance forecasting accuracy and stability by leveraging complementary strengths across models.
This paper finds that the hybrid model yields higher prediction accuracy and smaller errors than the single model by comparing the results of both models.
This work introduces a robust framework to predict daily log returns by leveraging a combined dataset comprising historical data, TIs, and macroeconomic data, including gold and oil prices, and macroeconomic data, including gold and oil prices, the volatility index, the dollar index, the interest rate, the 10-year Trea...
Aya Nabil, S. Barakat, Ahmed Aboelfetouh et al.· Scientific Reports· 0 citations
The results highlight the significance of using alternative information on sentiment along with traditional market indicators and show that the machine learning techniques outperform other techniques especially LSTM.
S. Durga, B. Ratnavalli, Visalakshi Naraparedd et al.· International Academic Journ...· 0 citations
A primary insight is that future research should move towards integrating these complementary methodologies and leveraging multi-modal data sources, which holds the potential for developing more robust and economically interpretable predictive frameworks.
Yan-Lin Huang· Advances in Economics, Manag...· 0 citations
The empirical results demonstrate that machine learning models significantly outperform OLS in capturing complex nonlinear relationships in stock returns, and the ANN model achieves the lowest RMSE, indicating the highest predictive accuracy, and generates superior long–short portfolio returns compared to the other mod...
Phat Ly Huynh Ngo, T. Pham, B. Lệ· Tạp chí Khoa học Đại học Côn...· 0 citations
The findings indicate that forecasting performance is strongly context-dependent: classical models remain effective for relatively stationary univariate series, volatility-oriented models are particularly relevant when clustering and spillover effects are present, and deep learning and transformer-based approaches are...
Elia Setiana, Kusrini Kusrini, Tonny Hidayat et al.· JITK (Jurnal Ilmu Pengetahua...· 0 citations
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