Paper: arXiv 2504.17313

Authors: Zhuohang Zhu, Haodong Chen, Qiang Qu, Xiaoming Chen, Vera Chung

Abstract

Effective stock price forecasting (estimating future prices) and prediction (estimating future price changes) are pivotal for investors, regulatory agencies, and policymakers. These tasks enable informed decision-making, risk management, strategic planning, and superior portfolio returns. Despite their importance, forecasting and prediction are challenging due to the dynamic nature of stock price data, which exhibit significant temporal variations in distribution and statistical properties. Additionally, while both forecasting and prediction targets are derived from the same dataset, their statistical characteristics differ significantly. Forecasting targets typically follow a log-normal distribution, characterized by significant shifts in mean and variance over time, whereas prediction targets adhere to a normal distribution. Furthermore, although multi-step forecasting and prediction offer a broader perspective and richer information compared to single-step approaches, it is much more challenging due to factors such as cumulative errors and long-term temporal variance. As a result, many previous works have tackled either single-step stock price forecasting or prediction instead. To address these issues, we introduce a novel model, termed Patched Channel Integration Encoder (PCIE), to tackle both stock price forecasting and prediction. In this model, we utilize multiple stock channels that cover both historical prices and price changes, and design a novel tokenization method to effectively embed these channels in a cross-channel and temporally efficient manner. Specifically, the tokenization process involves univariate patching and temporal learning with a channel-mixing encoder to reduce cumulative errors. Comprehensive experiments validate that PCIE outperforms current state-of-the-art models in forecast and prediction tasks.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper introduces a novel neural network architecture (PCIE) with technical details on tokenization and multi-head attention, indicating substantial math/ML complexity, and it includes a comprehensive experimental section with multiple baselines and performance metrics, showing strong empirical backing.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Multi-Step Stock Price Forecast & Prediction"] --> B["Data Input<br>Multi-Channel Historical Prices"]
  B --> C["Methodology: PCIE Model<br>Univariate Patching & Tokenization"]
  C --> D["Computation<br>Channel-Mixing Encoder & Temporal Learning"]
  D --> E["Outcomes<br>Reduced Cumulative Error"]
  D --> F["Outcomes<br>Multi-Step Prediction"]
  E --> G["Validation<br>Outperforms SOTA Models"]
  F --> G

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1 of 40 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.