Paper: arXiv 2511.08658

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.

Complexity vs Empirical Score

  • Math Complexity: 2.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: 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.

Research Flowchart

  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"]

Cited References (41)

  1. The Elements of Statistical Learning (2001)
  2. On the representation of continuous functions of several variables by superpositions of continuous functions of a smaller number of variables (1961)
  3. Representation properties of networks: Kolmogorov’s theorem is irrelevant (1989)
  4. Kolmogorov’s Theorem Is Relevant (1991) — link
  5. Approximation theory of the MLP model in neural networks (1999)
  6. Polynomial theory of complex systems (1971)
  7. The GMDH algorithm of Ivakhnenko (1981)
  8. Initial conditions and boundary conditions (1994)
and 33 more
  1. Searching for Activation Functions (2017) — link
  2. Artificial Neural Networks as a Tool for Recognition of Movements by Electroencephalograms. (2018)
  3. Universal Approximation Using Radial-Basis-Function Networks (1991)
  4. New RBF neural network classifier with optimized hidden neurons number (2004)
  5. Radial basis functions, multi-variable functional interpolation and adaptive networks (1988)
  6. Going deeper with convolutions (2015)
  7. Deep residual learning for image recognition (2016)
  8. Kolmogorov’s Gate Non-linearity as a Step toward Much Smaller Artificial Neural Networks (2022)
  9. Efficient capital markets: A review of theory and empirical work (1970)
  10. Efficient Market Hypothesis (EMH): Past, Present and Future (2008)
  11. History of the efficient market hypothesis (2011)
  12. Inefficient markets: An introduction to behavioural finance (2000)
  13. Orange juice and weather (1984)
  14. The performance of mutual funds in the period 1945-1964 (1968)
  15. International transmission of stock market movements (1989)
  16. Mean reversion in stock prices: Evidence and implications (1988)
  17. What every CFO should know about scientific progress in financial economics: What is known and what remains to be resolved (1994)
  18. Neural networks and statistical techniques in marketing research: A conceptual comparison (1994)
  19. Deep learning for stock market prediction (2020)
  20. Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations (2019)
  21. A neural network ensemble method with jittered training data for time series forecasting (2007)
  22. Self-supervised contrastive pre-training for time series via time-frequency consistency (2022)
  23. Mixing up contrastive learning: Self-supervised representation learning for time series (2022)
  24. Recurrent neural networks for financial time-series modelling (2018)
  25. Multi-source transfer learning with ensemble for financial time series forecasting (2020)
  26. Deep learning for biometric face recognition: experimental study on benchmark data sets (2020)
  27. Evolving polynomial neural networks for detecting abnormal patterns (2016)
  28. Extraction of Texture Features from X-Ray Images: Case of Osteoarthritis Detection (2018)
  29. Bayesian assessment of newborn brain maturity from two-channel sleep electroencephalograms (2012)
  30. Informativeness of sleep cycle features in Bayesian assessment of newborn electroencephalographic maturation (2011)
  31. Elements of Active Continuous Learning and Uncertainty Self-awareness: A Narrow Implementation for Face and Facial Expression Recognition (2023)
  32. HYBRID CONVOLUTIONAL-MULTILAYER PERCEPTRON ARTIFICIAL NEURAL NETWORK FOR PERSON RECOGNITION BY HIGH GAMMA EEG FEATURES (2022)
  33. Using Statistical and Artificial Neural Networks Meta-learning Approaches for Uncertainty Isolation in Face Recognition by the Established Convolutional Models (2022)

0 of 41 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.