“It Looks All the Same to Me”: Cross-index Training for Long-term Financial Series Prediction

ArXiv ID: 2511.08658 “View on arXiv”

Authors: Stanislav Selitskiy

Abstract

We investigate a number of Artificial Neural Network architectures (well-known and more ``exotic’’) in application to the long-term financial time-series forecasts of indexes on different global markets. The particular area of interest of this research is to examine the correlation of these indexes’ behaviour in terms of Machine Learning algorithms cross-training. Would training an algorithm on an index from one global market produce similar or even better accuracy when such a model is applied for predicting another index from a different market? The demonstrated predominately positive answer to this question is another argument in favour of the long-debated Efficient Market Hypothesis of Eugene Fama.

Keywords: Cross-Market Prediction, Neural Networks, Financial Forecasting, Index Prediction, Transfer Learning

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Street Traders
• Why: The paper applies standard neural network architectures and statistical metrics (MAPE, RMSE) without introducing novel mathematical theory or complex derivations. However, it is heavily data-driven, using a substantial dataset (2005-2022) and rigorous experimental setup including cross-validation and statistical hypothesis testing (Wilcoxon signed-rank), making it backtest-ready and implementation-heavy.

  flowchart TD
    A["Research Goal:<br>Does training on one global market index<br>enable accurate prediction of another?"] --> B["Data Inputs:<br>Long-term financial time-series<br>from multiple global market indexes"]
    B --> C["Methodology: Cross-Index Training<br>Train ANN models on Source Market Index<br>(e.g., S&P 500)"]
    C --> D["Computational Process:<br>Transfer Learning / Cross-Market Application<br>Apply trained model to predict<br>Target Market Index (e.g., Nikkei 225)"]
    D --> E["Comparison & Evaluation<br>Measure accuracy vs. models trained<br>specifically on the target index"]
    E --> F{"Key Findings"}
    F --> G["Predominately Positive Results<br>Supports Efficient Market Hypothesis<br>Indices exhibit correlated behavior<br>transferable via Neural Networks"]
    F --> H["Conclusion:<br>Cross-market prediction is viable<br>with ANN architectures"]