# A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm

arXiv: https://arxiv.org/abs/2412.07223 · HTML: https://mlsanity.com/paper/2412.07223/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks](https://mlsanity.com/paper/2409.09892/) (2024)
- [GuiltyWalker: Distance to illicit nodes in the Bitcoin network](https://mlsanity.com/paper/2102.05373/) (2021)
- [Application of an ANN and LSTM-based Ensemble Model for Stock Market Prediction](https://mlsanity.com/paper/2410.20253/) (2024)
- [Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Data](https://mlsanity.com/paper/2410.14963/) (2024)
- [Learning a Robust Representation via a Deep Network on Symmetric Positive Definite Manifolds](https://mlsanity.com/paper/1711.06540/) (2017)

## Built on by (most cited first, 5 of 5)

- [HADES: Hardware Accelerated Decoding for Efficient Speculation in Large Language Models](https://mlsanity.com/paper/2412.19925/) (2024)
- [Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacy](https://mlsanity.com/paper/2504.08751/) (2025)
- [Journey into Automation: Image-Derived Pavement Texture Extraction and Evaluation](https://mlsanity.com/paper/2501.02414/) (2025)
- [Regression and Forecasting of U.S. Stock Returns Based on LSTM](https://mlsanity.com/paper/2502.05210/) (2025)
- [Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms](https://mlsanity.com/paper/2412.18202/) (2024)

## Abstract (arXiv)

This paper provides a unique approach with AI algorithms to predict emerging stock markets volatility. Traditionally, stock volatility is derived from historical volatility,Monte Carlo simulation and implied volatility as well. In this paper, the writer designs a consolidated model with back-propagation neural network and genetic algorithm to predict future volatility of emerging stock markets and found that the results are quite accurate with low errors.