# Boosting the Accuracy of Stock Market Prediction via Multi-Layer Hybrid MTL Structure

arXiv: https://arxiv.org/abs/2501.09760 · HTML: https://mlsanity.com/paper/2501.09760/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 46 references that are in the map.
- First posted on arXiv 1 Jan 2025.

## Builds on (most cited first, 6 of 6)

- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [Kolmogorov-Arnold Networks (KANs) for Time Series Analysis](https://mlsanity.com/paper/2405.08790/) (2024)
- [Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models](https://mlsanity.com/paper/2009.10819/) (2020)
- [Application of an ANN and LSTM-based Ensemble Model for Stock Market Prediction](https://mlsanity.com/paper/2410.20253/) (2024)
- [A Margin-Maximizing Fine-Grained Ensemble Method](https://mlsanity.com/paper/2409.12849/) (2024)
- [Achieving More with Less: A Tensor-Optimization-Powered Ensemble Method](https://mlsanity.com/paper/2408.02936/) (2024)

## Abstract (arXiv)

Accurate stock market prediction provides great opportunities for informed decision-making, yet existing methods struggle with financial data's non-linear, high-dimensional, and volatile characteristics. Advanced predictive models are needed to effectively address these complexities. This paper proposes a novel multi-layer hybrid multi-task learning (MTL) framework aimed at achieving more efficient stock market predictions. It involves a Transformer encoder to extract complex correspondences between various input features, a Bidirectional Gated Recurrent Unit (BiGRU) to capture long-term temporal relationships, and a Kolmogorov-Arnold Network (KAN) to enhance the learning process. Experimental evaluations indicate that the proposed learning structure achieves great performance, with an MAE as low as 1.078, a MAPE as low as 0.012, and an R^2 as high as 0.98, when compared with other competitive networks.