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

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arxiv 2412.07223 v7 pith:R4SLKYB2 submitted 2024-12-10 q-fin.CP cs.LGcs.NE

Zong Ke , Jingyu Xu , Zizhou Zhang , Yu Cheng , Wenjun Wu This is my paper · ORCID
classification q-fin.CPcs.LGcs.NE
keywords volatilitystockalgorithmconsolidatedemerginggeneticmarketsnetwork
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Regression and Forecasting of U.S. Stock Returns Based on LSTM

    q-fin.ST 2025-02 reject novelty 2.0 of 10

    The authors fit standard factor models and an LSTM to U.S. sector returns and report that the five-factor model and LSTM each look best in different sectors.

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