Technical Analysis can predict future market prices
Is it true that technical Analysis can predict future market prices?
Yes. Recent computational and empirical studies demonstrate that integrating technical analysis indicators with advanced machine learning and deep learning models can successfully predict future market prices and trends.
Verdict: SUPPORTED · 11 sources for, 0 against · Last checked
The retrieved papers consistently utilize technical indicators—such as moving averages, RSI, MACD, and Fibonacci retracement—combined with modern machine learning, deep learning, or ensemble methods to achieve successful price predictions and trading returns across various asset classes (stocks, crypto, carbon, options). Since all relevant studies support the predictive value of technical analysis when paired with modern computational frameworks, the balance verdict is SUPPORTED.
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Wang, Q., Xu, W., & Zheng, H. (2018). Combining the wisdom of crowds and technical analysis for financial market prediction using deep random subspace ensembles. doi.org. https://doi.org/10.1016/J.NEUCOM.2018.02.095
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Mitchell, J. (2010). Soybean Futures Crush Spread Arbitrage: Trading Strategies and Market Efficiency. doi.org. https://doi.org/10.3390/jrfm3010063
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G, C., A, A., & R, E. (2025). Artificial Intelligence Models for Predicting Stock Returns Using Fundamental, Technical, and Entropy-Based Strategies: A Semantic-Augmented Hybrid Approach.. doi.org. https://doi.org/10.3390/e27060550
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Porwal, S., Srivastava, N., & Jha, M. (2025). Deep Learning Techniques For Option Price Prediction: A Comparative Analysis. doi.org. https://doi.org/10.1109/ICITEICS64870.2025.11341590
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Samson, T. J., Ayebamieprete, N. D., Iheagwara, S. E., & Ishaq, A. (2025). Development of Bitcoin Closing Price Prediction Model Using Machine Learning. doi.org. https://doi.org/10.51584/ijrias.2025.100800047
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Kg, H., Krishnaswamy, V., M, V., & Mahima, B. (2024). Navigating Financial Markets: Exploring Advanced Neural Networks for Stock Price Prediction. doi.org. https://doi.org/10.1109/ICETCI62771.2024.10704121
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Julus, L. J., Naweed, S. S., Aadhavan, D., Sankar, S., Rampriya, R., & Kavitha, T. (2025). LSTM & MACD Approach: Prediction and Analysis of Stock Market. doi.org. https://doi.org/10.1109/INCIP64058.2025.11019395
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Q, F. & X, L. (2026). Enhancing the pricing efficiency of financial assets with an optimized bayesian network based on efficient fusion.. doi.org. https://doi.org/10.1371/journal.pone.0347047
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L, Y., B, G., X, N., & Q, L. (2025). SSE forecasts based on market-sentiment dual anchoring.. doi.org. https://doi.org/10.1371/journal.pone.0339065
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K, Y., Z, Y., CC, W., Q, H., J, Z., Z, H., & Y, L. (2026). Flexible Target Prediction for Quantitative Trading in the American Stock Market: A Hybrid Framework Integrating Ensemble Models, Fusion Models and Transfer Learning.. doi.org. https://doi.org/10.3390/e28010084
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H, W. & Y, L. (2026). RF-LSTM carbon price prediction based on CEEMDAN decomposition and multiscale entropy reconstruction.. doi.org. https://doi.org/10.1038/s41598-026-35085-5
What does the evidence say?
The retrieved papers consistently utilize technical indicators—such as moving averages, RSI, MACD, and Fibonacci retracement—combined with modern machine learning, deep learning, or ensemble methods to achieve successful price predictions and trading returns across various asset classes (stocks, crypto, carbon, options). Since all relevant studies support the predictive value of technical analysis when paired with modern computational frameworks, the balance verdict is SUPPORTED.
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