Paper: arXiv 2502.15853

Abstract

This paper presents a comprehensive study on stock price prediction, leveragingadvanced machine learning (ML) and deep learning (DL) techniques to improve financial forecasting accuracy. The research evaluates the performance of various recurrent neural network (RNN) architectures, including Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and attention-based models. These models are assessed for their ability to capture complex temporal dependencies inherent in stock market data. Our findings show that attention-based models outperform other architectures, achieving the highest accuracy by capturing both short and long-term dependencies. This study contributes valuable insights into AI-driven financial forecasting, offering practical guidance for developing more accurate and efficient trading systems.

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 demonstrates high mathematical complexity through detailed exposition of LSTM/GRU/Transformer architectures and their gating mechanisms, while empirical rigor is elevated by its use of specific real-world datasets (Tesla stock), comparison of multiple architectures (RNNs, CNNs, Transformers), and focus on practical trading system applications.

Research Flowchart

  flowchart TD
  A["Research Goal: Enhance Stock Price Prediction Accuracy<br>using AI/ML"] --> B["Methodology: Evaluate ML/DL Models<br>on Historical Stock Data"]
  B --> C["Data Inputs: Historical Stock Prices<br>Time-Series Data"]
  C --> D["Computational Process: Train & Compare<br>LSTM, GRU, & Attention-Based Models"]
  D --> E["Key Finding: Attention-Based Models<br>Outperform (Highest Accuracy)"]
  E --> F["Outcome: Improved Financial Forecasting<br>for Real-Time Trading Strategies"]

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  8. Indian stock market prediction using artificial neural networks on tick data (2019)
and 40 more
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0 of 48 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.