Multi-Horizon Echo State Network Prediction of Intraday Stock Returns

ArXiv ID: 2504.19623 “View on arXiv”

Authors: Giovanni Ballarin, Jacopo Capra, Petros Dellaportas

Abstract

Stock return prediction is a problem that has received much attention in the finance literature. In recent years, sophisticated machine learning methods have been shown to perform significantly better than ‘‘classical’’ prediction techniques. One downside of these approaches is that they are often very expensive to implement, for both training and inference, because of their high complexity. We propose a return prediction framework for intraday returns at multiple horizons based on Echo State Network (ESN) models, wherein a large portion of parameters are drawn at random and never trained. We show that this approach enjoys the benefits of recurrent neural network expressivity, inherently efficient implementation, and strong forecasting performance.

Keywords: Echo State Network (ESN), reservoir computing, intraday return prediction, recurrent neural networks, time series forecasting, Equities

Complexity vs Empirical Score

  • Math Complexity: 4.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Street Traders
  • Why: The paper employs relatively accessible machine learning techniques (ESNs with regularized linear regression) and focuses on practical implementation with specific datasets and backtesting metrics, making it more applied than theoretical. The mathematical complexity is moderate due to the reservoir computing framework, but the emphasis is on empirical validation and computational efficiency for trading contexts.
  flowchart TD
    A["Research Goal<br>Forecast Intraday Stock Returns<br>Multiple Horizons"] --> B["Methodology<br>Multihorizon Echo State Network"]
    B --> C["Data Input<br>Intraday Equity Data"]
    C --> D["Computational Process<br>Reservoir Computing & ESN Training"]
    D --> E["Key Finding 1<br>Superior Predictive Accuracy<br>vs Classical Methods"]
    D --> F["Key Finding 2<br>High Efficiency<br>Minimal Training Parameters"]
    D --> G["Key Finding 3<br>Scalability & Expressivity<br>Deep RNN Performance"]