# Gradient Boosting Decision Tree with LSTM for Investment Prediction

arXiv: https://arxiv.org/abs/2505.23084 · HTML: https://mlsanity.com/paper/2505.23084/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate](https://mlsanity.com/paper/2402.02769/) (2024)
- [Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout](https://mlsanity.com/paper/2411.06128/) (2024)
- [Rhyme-aware Chinese lyric generator based on GPT](https://mlsanity.com/paper/2408.10130/) (2024)
- [GPT-Signal: Generative AI for Semi-automated Feature Engineering in the Alpha Research Process](https://mlsanity.com/paper/2410.18448/) (2024)
- [A Communication-Efficient Parallel Algorithm for Decision Tree](https://mlsanity.com/paper/1611.01276/) (2016)
- [AI-Driven Prognostics for State of Health Prediction in Li-ion Batteries: A Comprehensive Analysis with Validation](https://mlsanity.com/paper/2504.05728/) (2025)

## Abstract (arXiv)

This paper proposes a hybrid framework combining LSTM (Long Short-Term Memory) networks with LightGBM and CatBoost for stock price prediction. The framework processes time-series financial data and evaluates performance using seven models: Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Bidirectional LSTM (BiLSTM), vanilla LSTM, XGBoost, LightGBM, and standard Neural Networks (NNs). Key metrics, including MAE, R-squared, MSE, and RMSE, are used to establish benchmarks across different time scales. Building on these benchmarks, we develop an ensemble model that combines the strengths of sequential and tree-based approaches. Experimental results show that the proposed framework improves accuracy by 10 to 15 percent compared to individual models and reduces error during market changes. This study highlights the potential of ensemble methods for financial forecasting and provides a flexible design for integrating new machine learning techniques.