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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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Forward citations

Cited by 4 Pith papers

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

  1. HADES: Hardware Accelerated Decoding for Efficient Speculation in Large Language Models

    cs.CL 2024-12 conditional novelty 5.0 of 10

    A custom Verilog module accelerates the token-acceptance step of speculative decoding by about 7x over GPUs, but the step is only a small fraction of total LLM inference.

  2. Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms

    cs.LG 2024-12 reject novelty 4.0 of 10

    A standard autoencoder-CNN-GAN stack is applied to Bitcoin futures price prediction, with reported accuracy and profits that the paper does not adequately support.

  3. Mitigating Knowledge Conflicts in Language Model-Driven Question Answering

    cs.CL 2024-11 reject novelty 4.0 of 10

    On memorized question-answer pairs from KMIR and NQ, bottleneck and prefix adapters trained on entity-swapped contexts let a GPT-2 reader follow the new context most of the time, though no baselines are reported.

  4. 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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