---
title: Predicting Stock Market Trends with Machine Learning: A Comprehensive Study
url: https://www.ml-quant.com/papers/ssrn/4629045/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4629045
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4629045
featured: 2023-11-15
citations: 2
topic: ML & AI Methods
---


# Predicting Stock Market Trends with Machine Learning: A Comprehensive Study

The article presents a study on the use of LSTM neural networks and linear regression for stock market prediction, showing superior performance over traditional models.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4629045
- Identifier: SSRN 4629045
- Released: 2023-06-09
- First featured: Quant Letter No. 26 (2023-11-15): https://www.ml-quant.com/issues/2023-11-15/
- Citations (Semantic Scholar): 2
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://www.ml-quant.com/papers/arxiv/2312.00752/): Sequence Modeling: Mamba, a neural network architecture that doesn't use attention or MLP blocks, provides faster inference and better performance in language, audio, and genomics than Transformers.
- [Graph Mamba: Towards Learning on Graphs with State Space Models](https://www.ml-quant.com/papers/arxiv/2402.08678/): Graph Mamba Networks, a new type of Graph Neural Networks, have been introduced, which achieve excellent performance in various benchmark datasets despite lower computational cost.
- [Edge Directionality Improves Learning on Heterophilic Graphs](https://www.ml-quant.com/papers/arxiv/2305.10498/): The study presents Directed Graph Neural Network (Dir-GNN), a new deep learning framework for directed graphs that surpasses traditional models in heterophilic benchmarks.
- [Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks](https://www.ml-quant.com/papers/arxiv/2310.02244/): Deep Residual Network Feature Learning: The research explores depthwise parametrizations in deep residual networks, pinpointing Depth-$\mu$P as the best parametrization for maximizing feature learning and diversity, but notes its limitations in deeper networks.
- [Modular Duality in Deep Learning](https://www.ml-quant.com/papers/arxiv/2410.21265/): The article presents a new theory of modular dualization for general neural networks, providing a theoretical basis for fast and scalable training algorithms, potentially leading to a new generation of optimizers for neural architectures.
- [A Coefficient Makes SVRG Effective](https://www.ml-quant.com/papers/arxiv/2311.05589/): The article introduces α-SVRG, a new method for optimizing neural networks that improves training loss reduction across various architectures and datasets.
