Beyond the Numbers: Injecting Motion into Stock Market Forecasting

Author: Denis Avetisyan

A new approach leverages principles of kinematics to refine neural network predictions, aiming for more stable and accurate long-term stock market forecasts.

The KGate model, alongside a non-kinematic artificial neural network, attempts to predict Dow Jones fluctuations, but it is the kinematic-informed KIANN that demonstrates a potentially more nuanced understanding of market dynamics through its normalized input, suggesting an approach that integrates movement-based data may yield improved forecasting capabilities.

Researchers demonstrate improved performance by incorporating velocity and acceleration hints into the loss function of temporal graph neural networks.

Accurate long-term stock market forecasting remains a challenge due to inherent volatility and the potential for spurious predictions in autoregressive models. This is addressed in ‘Weak Relation Enforcement for Kinematic-Informed Long-Term Stock Prediction with Artificial Neural Networks’ by introducing a novel loss function that incorporates kinematic constraints—specifically, velocity relations—into the training process. The resulting Kinematic-Informed Neural Network (KINN) demonstrably improves forecasting accuracy across diverse architectures and datasets, mitigating normalization-sensitive behaviour and preserving data topology. Could this approach, by weakly enforcing data neighbourhood proximity, represent a crucial step towards more robust and reliable financial forecasting models?

The Inevitable Drift: Forecasting Challenges in Financial Systems

Predicting stock market behavior remains a persistent challenge due to the inherent non-linear and dynamic nature of financial systems. Traditional statistical methods often prove inadequate, failing to account for time-varying dependencies. Artificial Neural Networks offer potential, but are computationally expensive and prone to overfitting. Effective regularization is critical, though insufficient to overcome fundamental limitations.

The KGate model, a non-kinematic informed artificial neural network (ANN), demonstrates predictions for the Dow Jones, while a kinematic informed KIANN offers an alternative predictive approach, both utilizing non-normalized input data.

Successfully modeling temporal dependencies requires incorporating relevant features and understanding underlying dynamics. Forecasting isn’t about arresting the market’s drift, but building systems that age gracefully within its currents.

From Recurrence to Physics: Evolving Neural Network Architectures

Recurrent Neural Networks (RNNs), including LSTM and GRUs, initially addressed sequential data challenges, but often struggle with long-term dependencies. Physics-Aware Neural Networks represent a departure, integrating established physical principles to enhance understanding and generalization. A key application involves incorporating kinematic features – velocity and acceleration – to improve predictions in dynamic systems.

Kinematic-Informed Neural Networks utilize velocity and acceleration as integral features, allowing the model to gain a nuanced understanding of momentum and potential inflection points, leading to improved prediction accuracy in time-series analysis and forecasting.

Validation Across Indices: Assessing Predictive Power

The methodology involved training and evaluating predictive models on key financial indices – Dow Jones, NASDAQ, NIKKEI, and DAX – to ensure robustness and generalizability. Min-Max Normalization was employed as a crucial preprocessing step to prevent feature scaling issues and accelerate training. The Adam Optimizer efficiently trained the models, minimizing Sum of Squares Error ($SSE$).

Statistical significance was assessed using the Wilcoxon Signed Rank Test. Results demonstrated statistically significant performance improvements with Kinematic-Informed Neural Networks, with LSTM, GMDH, and KGate architectures exhibiting a greater than 99% confidence level of improvement when incorporating kinematic information.

Beyond Prediction: Towards Robust Forecasting Systems

The successful integration of kinematic features into physics-informed machine learning models demonstrates broadened applicability across diverse time series forecasting tasks. This approach leverages underlying physical principles to constrain and refine predictions, resulting in improved accuracy and generalization. Initial results indicate significant gains when applied to systems governed by well-defined kinematic relationships.

Exploring advanced architectures like Temporal Graph Neural Networks and Transformer Networks represents a logical next step. Furthermore, incorporating techniques like Attention Mechanisms and Radial Basis Function (RBF) Networks could refine feature selection and enhance generalization. Future research should prioritize combining these approaches—physics-informed constraints with advanced architectures and feature selection—to create even more robust and accurate forecasting models. Every iteration refines the forecast, and every version a record in the annals of prediction.

The pursuit of predictive accuracy, as demonstrated by the Kinematic-Informed Neural Network, inevitably faces the reality of systemic decay. Any improvement in forecasting, however elegantly engineered, ages faster than expected, demanding continuous refinement. This echoes Paul Erdős’s sentiment: “A mathematician knows a lot of things, but knows nothing deeply.” The KINN model, while demonstrating enhanced accuracy through the incorporation of kinematic hints into the loss function, is but a snapshot in time. The stock market, a complex adaptive system, will continuously evolve, requiring ongoing adaptation and innovation to maintain predictive power. The model’s efficacy isn’t a destination, but a temporary reprieve in the relentless march of entropy.

What Lies Ahead?

The pursuit of predictive accuracy in financial markets, as exemplified by this work, perpetually chases a receding horizon. While incorporating kinematic hints into the loss function demonstrably reduces spurious forecasts—a momentary stabilization—it merely alters the character of the eventual decay, not its inevitability. Uptime, in any predictive model, is temporary. The system will, by its nature, eventually diverge from observed reality; the question becomes not if, but when, and what form that divergence will take.

Future iterations will likely focus on refining the ‘physics’ of this artificial market. Greater fidelity in representing the underlying dynamics – perhaps through more complex temporal graph structures or incorporating higher-order derivatives – may yield incremental improvements. However, the inherent noise and irrationality of human behavior represent a fundamental latency, a tax every request for prediction must ultimately pay. The challenge isn’t simply to model the flow, but to acknowledge the inherent friction within it.

Ultimately, the value may lie not in achieving perfect foresight, but in understanding the limitations of the attempt. Stability is an illusion cached by time. Research should increasingly address the meta-problem: how to detect, quantify, and adapt to the inevitable model drift before it manifests as catastrophic error. The graceful degradation of predictive power, rather than its abrupt failure, may prove the most sustainable metric of success.

Original article: https://arxiv.org/pdf/2511.10494.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

The Inevitable Drift: Forecasting Challenges in Financial Systems

From Recurrence to Physics: Evolving Neural Network Architectures

Validation Across Indices: Assessing Predictive Power

Beyond Prediction: Towards Robust Forecasting Systems

What Lies Ahead?

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