M2VN: Multi-Modal Learning Network for Volatility Forecasting

This repository contains the official implementation of M2VN, a lightweight yet effective architecture that combines price, volume, and news embeddings for equity–market volatility prediction. The code accompanies our paper submitted to a ICAIF.

Repository Overview

| Path / file | Purpose | |-------------|---------| | data_provider/ | Data loading and on-the-fly preprocessing | | exp/ | Experiment settings and logging utilities | | layers/ | Custom PyTorch layers used by M2VN | | models/ | Model definition and loss functions | | runfile/ | Shell scripts for training / inference (run_final.sh) | | utils/ | Miscellaneous helpers | | Step 1–3 Aggregate Results.ipynb | Notebooks for reproducing paper tables | | run.py | Entry point if you prefer python run.py over the shell script | | LICENSE | License information (MIT) |

Quick Start

Install packages

pip install -r requirements.txt   # Provided in repo

Download the dataset

The full, pre-processed dataset is available on Google Drive Link Also, raw version news artical data is here: Link

After downloading, place the extracted folder inside e.g., (dataset/KO4.csv)

Train & evaluate

bash ./runfile/run_final.sh

The script trains M2VN with default hyper-parameters and writes:

* Checkpoints → checkpoints/ * Final metrics (CSV) → results_test/

Reproducing Paper Results

After training finishes, open the Jupyter notebooks in the repo root:

| Notebook | What it does | | ----------------------------------------- | ---------------------------------------------- | | Step 1 Aggregate Results.ipynb | Get Main model results | | Step 2 Aggregate Results Vol(non).ipynb | W/O Volume | | Step 3 Aggregate Results Vol-sent.ipynb | W/ News Sentiment |

Running the cells in order will produce the tables reported in the paper.

Citation

If you find M2VN useful in your work, please consider citing:

@inproceedings{Anonymous,
  title     = {M2VN: Multi-Modal Learning Network for Volatility Forecasting},
  author    = {Anonymous},
  booktitle = {ICAIF},
  year      = {2025}
}

License

This project is licensed under the MIT License – see the LICENSE file for details.

Contact

Questions or suggestions? Open an issue or reach out to Anonymous.

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