Overview

The Kumbure et al. (2022) systematic review identified neural networks and support vector machines as the two most frequently employed techniques for stock market prediction, collectively appearing in approximately 60% of the 138 analyzed papers. This dominance reflects the ability of these methods to model nonlinear relationships in financial time series. However, the review also documents a clear evolution toward deep learning architectures, particularly LSTM networks and CNN-LSTM hybrids, in research published after 2015.

The choice of algorithm depends on multiple factors: prediction horizon (intraday vs. monthly), target variable (price level vs. directional movement), data characteristics (high-frequency vs. daily), and interpretability requirements. Studies indicate that no single method dominates across all settings. In other words, algorithm selection requires understanding the specific prediction task and data environment. This section provides detailed analysis of the major algorithmic families documented in the review. For comprehensive coverage of input features that feed these algorithms, see the Data Sources & Features page. For performance metrics and backtesting approaches, see the Performance Evaluation page.

Neural Networks

Artificial neural networks (ANNs) have been applied to stock prediction since the early 1990s and remain the most popular technique in the reviewed literature. In practice, the multilayer perceptron (MLP) architecture with one or two hidden layers serves as the baseline approach. According to multiple studies, ANNs can approximate complex nonlinear mappings from input features to price predictions, though they require careful hyperparameter tuning and regularization to avoid overfitting. For instance, early experiments using feedforward networks achieved directional accuracy improvements of 5-8% over random walk benchmarks—a result that has been replicated across different markets and time periods.

Key findings from the literature indicate that neural network performance is highly sensitive to feature selection and preprocessing. Studies that carefully selected input variables using domain knowledge or automated techniques achieved accuracy improvements of 5-15% compared to those using all available features. This is because irrelevant or noisy features can obscure the weak predictive signals in financial data. Normalization of inputs, typically to [0,1] or standard normal ranges, is essential for stable training—without normalization, features with larger magnitudes dominate the learning process. The number of hidden neurons and layers requires cross-validation, with deeper networks generally reserved for larger datasets where overfitting can be controlled.

The reviewed research identifies several common pitfalls in neural network application to financial prediction. Look-ahead bias, where future information inadvertently leaks into training data, remains a frequent error. The Kumbure et al. (2022) review documented how improper data splitting led to inflated accuracy claims in several early studies. Similarly, many studies fail to account for transaction costs and market impact when reporting profitable trading strategies. This is why proper backtesting methodology is essential for realistic performance assessment. For critical analysis of evaluation methodologies, see the Performance Evaluation page.

Support Vector Machines

Support Vector Machines (SVMs) rank as the second most popular technique, appearing in approximately 25% of reviewed papers. In essence, SVMs are particularly well-suited to classification tasks such as predicting directional movement (up/down), where they seek the maximum-margin hyperplane separating classes. For regression tasks (SVR), the epsilon-insensitive loss function enables price level prediction with controlled tolerance. This approach has advantages over other methods because it focuses on margin maximization rather than minimizing average error.

The kernel choice critically affects SVM performance. The radial basis function (RBF) kernel dominates in financial applications due to its ability to model complex nonlinear decision boundaries. Studies comparing kernels found that RBF typically outperforms linear and polynomial kernels by 3-8% in directional accuracy. However, computational cost increases significantly with dataset size, making SVMs less suitable for high-frequency data.

Compared to neural networks, SVMs offer stronger theoretical foundations through statistical learning theory, including generalization bounds based on VC dimension. In practice, SVMs often achieve comparable performance to shallow neural networks while being less prone to local minima during training. However, SVMs lack the scalability of modern deep learning and struggle with very large datasets or real-time prediction requirements.

Ensemble Methods

Ensemble methods combine multiple models to improve prediction robustness and reduce variance. Random forests, gradient boosting machines (GBM), and bagging approaches appear in approximately 15% of reviewed papers. According to recent surveys, these methods offer advantages in handling high-dimensional feature spaces and providing built-in feature importance measures. Specifically, ensemble methods can capture different aspects of market dynamics when individual models focus on different feature subsets or time horizons.

Random forests provide feature importance rankings that help identify which predictors contribute most to forecasting accuracy. Because these rankings are derived from out-of-bag samples, they provide an unbiased estimate of variable relevance—which means practitioners can trust the importance scores without additional validation. Studies using random forest feature importance for variable selection report improvements when feeding selected features to other algorithms. XGBoost and LightGBM have gained popularity since 2015, demonstrating strong performance on structured financial data with faster training times than traditional implementations. Compared to random forests, gradient boosting methods offer more aggressive error correction but require careful regularization to prevent overfitting.

Deep Learning Architectures

Deep learning represents the most significant methodological trend in post-2015 research. Long Short-Term Memory (LSTM) networks address the vanishing gradient problem in traditional recurrent networks, effectively enabling modeling of long-term dependencies in price sequences that span weeks or months. This is particularly important for capturing cyclical patterns that shorter-term models miss. Convolutional Neural Networks (CNNs) extract local patterns from price charts or technical indicator images—essentially treating the financial time series as a visual pattern recognition problem. Unlike traditional approaches that rely on hand-crafted features, deep learning models can automatically discover relevant representations from raw data. The review documents increasing adoption of these architectures for capturing complex temporal dynamics.

Studies comparing deep learning to traditional methods report mixed results, though the evidence suggests consistent advantages for sequence-aware architectures. LSTM networks typically outperform MLPs by 3-7% in directional accuracy on daily data, with larger improvements on longer sequences. As a result, LSTM has become the default choice for researchers working with sequential price data. However, deep learning requires substantially more training data to avoid overfitting—specifically, the review notes that many deep learning studies use only 2-3 years of data, which may be insufficient for models with millions of parameters. Therefore, practitioners should carefully consider sample size requirements when selecting architectures. On the other hand, for researchers with access to decades of historical data, deep learning offers clear advantages over simpler methods.

Hybrid Approaches

Hybrid methods combine multiple techniques to leverage complementary strengths. The Kumbure et al. (2022) review documents several common hybrid architectures that achieve state-of-the-art results in specific settings. These combinations address limitations of individual methods while increasing system complexity.

Common hybrid approaches include:

• Wavelet + Neural Network: Wavelet decomposition separates signal from noise before feeding cleaner inputs to prediction models
• EMD + LSTM: Empirical mode decomposition handles non-stationary data characteristics before LSTM modeling
• Genetic Algorithm + SVM: Evolutionary optimization of SVM hyperparameters and feature selection
• Sentiment + Technical: NLP-derived sentiment features combined with traditional technical indicators
• Ensemble of Deep Networks: Multiple LSTM or CNN variants combined through averaging or stacking

The evidence from multiple studies suggests that hybrid methods often outperform single-technique approaches, with accuracy improvements of 2-8% commonly reported. This means that combining complementary methods can address individual weaknesses. However, hybrid systems introduce additional complexity in training, validation, and maintenance. Consequently, the increased number of hyperparameters creates risk of overfitting to specific market conditions. For discussion of performance metrics and evaluation challenges, see the Performance Evaluation page.

Recent Developments (2024-2025)

Since the Kumbure et al. (2022) review, several technical advances have emerged. Transformer architectures have been adapted specifically for financial time series, with models like Temporal Fusion Transformer and Informer designed for long-sequence forecasting. Graph neural networks now model inter-stock relationships and sector dependencies. Reinforcement learning approaches frame trading as a sequential decision problem rather than pure prediction.

Key recent publications advancing algorithmic approaches include:

• Deep Learning techniques for stock market forecasting (ACM SEIM, 2023) - Analysis of CNN-LSTM variants and attention mechanisms for price prediction
• FinGPT: Open-Source Financial Large Language Models (arXiv, 2024) - Framework for LLM-based financial analysis
• Financial applications of machine learning: A literature review (Expert Systems with Applications, 2023) - Broader survey covering prediction alongside portfolio optimization
• Graph neural networks for stock market prediction (Knowledge-Based Systems, 2024) - Modeling sector and supply-chain dependencies through graph structures
• Transformer-based models for financial time series (Journal of Financial Data Science, 2024) - Comparison of attention mechanisms for price prediction
• Explainable AI for financial prediction (Finance Research Letters, 2024) - Interpretable deep learning methods for regulatory compliance

Leading Research Teams

Algorithmic development for financial prediction spans computer science, finance, and quantitative research groups. Key institutions contributing to ML technique advances include:

Key Journals

Algorithmic advances in financial prediction appear across multiple venues. For comprehensive coverage of research institutions, see the Research Teams page.

• Expert Systems with Applications - Leading venue for applied AI including financial ML
• Neurocomputing - Neural network and deep learning methods
• IEEE TKDE - Data mining and knowledge discovery
• Journal of Machine Learning Research - Foundational ML methods

External Resources