Can Stock Markets Learn From Each Other?

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Author: Denis Avetisyan

New research explores whether artificial neural networks trained on different stock indexes can identify shared patterns, offering insights into market efficiency.

Stock indexes—including the NASDAQ, DOW, NIKKEI, and DAX—are shown to fluctuate in correspondence with the intervals of the conducted experiments, suggesting a relationship between these market indicators and the timing of the study.
Stock indexes—including the NASDAQ, DOW, NIKKEI, and DAX—are shown to fluctuate in correspondence with the intervals of the conducted experiments, suggesting a relationship between these market indicators and the timing of the study.

Cross-training artificial neural networks across financial indexes reveals potential commonalities, indirectly supporting a weak form of the Efficient Market Hypothesis.

Despite decades of research, consistently outperforming financial markets remains a significant challenge, prompting continued exploration of novel predictive techniques. This study, entitled “It Looks All the Same to Me”: Cross-index Training for Long-term Financial Series Prediction, investigates whether Artificial Neural Networks trained on one global stock market index can accurately forecast the behavior of others. Results demonstrate a surprising degree of transferability, suggesting underlying commonalities in market dynamics. Could this cross-index predictability offer further support for the Efficient Market Hypothesis, or hint at previously unrecognised global financial interdependencies?

Beyond Efficient Markets: Identifying Predictive Signals

The Efficient Market Hypothesis suggests asset prices fully reflect available information, making consistent outperformance improbable. However, empirical evidence reveals subtle patterns in financial time series, indicating current modeling approaches may be incomplete. These patterns, while not guaranteeing profit, offer opportunities to refine predictive capabilities. Traditional statistical techniques, like simple Auto-Regression models, often struggle with the intricate dynamics of financial markets due to assumptions of linearity and stationarity. Consequently, forecasts can be inaccurate, particularly during volatility or structural change. Overcoming these limitations requires advanced predictive techniques capable of capturing non-linear relationships and evolving market conditions; the pursuit isn’t perfect prediction, but discerning signal from noise.

Neural Networks and the Architecture of Prediction

Machine Learning, particularly Artificial Neural Networks, offers a compelling alternative to traditional statistical methods for modeling complex financial time series. These networks excel at identifying non-linear relationships often missed by linear models, improving predictive accuracy in volatile markets. Several architectures have been explored, each with unique strengths. Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks capture temporal dependencies. Convolutional Neural Networks (CNNs) identify localized patterns, while Radial Basis Function (RBF) networks offer efficient function approximation. Recent work focuses on enhancing these architectures. A new layer, termed Kolmogorov’s Gate, refines signal control within networks, improving prioritization of relevant information. Effective implementation also requires robust data handling, and the Group Method of Data Handling (GMDH) shows promise in optimizing model complexity and preventing overfitting.

Cross-Training and the Validation of Generalizable Models

Data Partitioning created distinct training and testing datasets from historical data, ensuring unbiased assessment of model performance. Datasets reflected a representative distribution of market conditions, mitigating overfitting. A Cross-Training technique evaluated model generalization across different market indexes. Models were trained on data from one index (e.g., NASDAQ) and tested on others (DAX, Dow Jones, NIKKEI). Model accuracy was quantified using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). Statistical comparison using the Wilcoxon Signed Rank Test yielded mixed results. The NIKKEI index consistently demonstrated superior cross-trained performance, while the Dow Jones index exhibited the poorest. Notably, the standard deviation of accuracy degradation was ≤ 0.02 in many cases, indicating minimal performance loss when adapting models to new markets.

Transfer Learning and the Resilience of Predictive Systems

Studies demonstrate the feasibility of transferring predictive models between stock markets through cross-training. This involves training a machine learning model on one market’s data and applying it to forecast performance in another. This success indicates the potential for developing robust and generalizable models capable of adapting to changing dynamics, reducing the need for extensive retraining. Findings support a weak form of the Efficient Market Hypothesis, showing machine learning models can predict performance in a secondary market with comparable, and sometimes superior, accuracy to models trained solely on that market’s data. This suggests broader market patterns and underlying economic principles significantly contribute to stock price movements, allowing for knowledge transfer. These models offer a promising tool for investors and financial institutions seeking to improve forecasting capabilities. Further research could explore integrating diverse algorithms and alternative data sources, such as news sentiment, to enhance accuracy. A simple, adaptable design may prove more resilient than a complex, overfitted system.

The research subtly probes the boundaries of predictability within complex systems, echoing a sentiment articulated by Stephen Hawking: “Intelligence is the ability to adapt to any environment.” This study, employing cross-training of Artificial Neural Networks, doesn’t seek to defeat the Efficient Market Hypothesis, but rather to understand the degree to which shared underlying structures might exist across seemingly disparate financial indexes. The methodology acknowledges that even within apparent randomness, patterns – however faint – can be revealed through rigorous analysis. The core idea of identifying shared patterns, even if supporting only a weak form of market efficiency, highlights the importance of holistic system understanding – recognizing that isolating a single variable offers limited insight into the behavior of the whole.

The Road Ahead

The pursuit of predictive power in financial time series, as explored in this work, inevitably encounters the constraints of systemic complexity. The observation of shared patterns across seemingly independent indices does not, of course, validate any particular hypothesis; rather, it highlights the inherent interconnectedness of these systems. Each newly leveraged dependency, each additional index incorporated into the cross-training regimen, is a hidden cost extracted from the freedom to generalize. The apparent simplicity of the Long Short-Term Memory network belies the labyrinthine feedback loops it attempts to model, loops which may, at scale, generate more questions than answers.

Future research should move beyond merely assessing whether cross-training improves performance, and instead focus on how these shared patterns manifest. Are they truly reflective of underlying economic principles, or simply artifacts of algorithmic construction – echoes of the model itself? A deeper investigation into the network’s internal representations, and the development of methods to disentangle signal from noise, will be crucial. The pursuit of forecasting accuracy, while pragmatically important, risks obscuring the more fundamental question: what is the structure of financial time series, and how does that structure dictate behavior?

Ultimately, the limitations encountered here are not unique to this specific methodology. They represent a broader challenge in complex systems modeling: the tendency to mistake correlation for causation, and the difficulty of isolating true innovation from the re-arrangement of existing components. The elegance of a solution, it seems, is inversely proportional to its complexity.

Original article: https://arxiv.org/pdf/2511.08658.pdf

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

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2025-11-13 09:19