{"repo":"Yoontae6719/M2VN-Multi-Modal-Learning-Network-for-Volatility-Forecasting","free":true,"listed":false,"github":"https://github.com/Yoontae6719/M2VN-Multi-Modal-Learning-Network-for-Volatility-Forecasting","clone":"git clone https://github.com/Yoontae6719/M2VN-Multi-Modal-Learning-Network-for-Volatility-Forecasting.git","description":"Official code - M2VN(Multi-Modal Learning Network for Volatility Forecasting)","language":"Jupyter Notebook","stars":11,"topics":["forecating","quantitative-finance","realized-volatility","volatility","volatility-forecasting"],"license":null,"category":"trading","readme_excerpt":"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 1. Install packages 2. 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 ) 4. Train & evaluate 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 res","default_branch":null,"files":null,"tree":[],"storefront":"/r/Yoontae6719","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/Yoontae6719/M2VN-Multi-Modal-Learning-Network-for-Volatility-Forecasting/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}