mlsanityData to 14 Oct 2026

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

arXiv 2412.07223 ↗First posted 10 Dec 2024q-fin.CP cs.LG cs.NEcited by 5

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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.

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

5 of 26 references.

References · 5

  1. 2024Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networkscited by 19
  2. 2021GuiltyWalker: Distance to illicit nodes in the Bitcoin networkcited by 13
  3. 2024Application of an ANN and LSTM-based Ensemble Model for Stock Market Predictioncited by 13
  4. 2024Deep Learning for Weather Forecasting: A CNN-LSTM Hybrid Model for Predicting Historical Temperature Datacited by 9
  5. 2017Learning a Robust Representation via a Deep Network on Symmetric Positive Definite Manifoldscited by 7

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Cited by year

Citing papers per year: 2024: 2; 2025: 3.

5 papers.

  1. 2024HADES: Hardware Accelerated Decoding for Efficient Speculation in Large Language Modelscited by 15
  2. 2025Research on the Design of a Short Video Recommendation System Based on Multimodal Information and Differential Privacycited by 9
  3. 2025Journey into Automation: Image-Derived Pavement Texture Extraction and Evaluationcited by 7
  4. 2025Regression and Forecasting of U.S. Stock Returns Based on LSTMcited by 4
  5. 2024Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithmscited by 3