research-article

Stock Volatility Forecast Base on Comparative Learning and Autoencoder Framework

Authors:

Yuxin LiuAuthors Info & Claims

ICMVA '22: Proceedings of the 2022 5th International Conference on Machine Vision and Applications

Pages 99 - 103

https://doi.org/10.1145/3523111.3523126

Published: 02 May 2022 Publication History

Abstract

Volatility is an important indicator of derivatives pricing, financial risk measurement, and market panic sentiment measurement. A reasonable prediction of volatility is of great significance to market participants and regulators. This article proposes a new volatility forecast model. We use comparative learning and autoencoders to improve the accuracy and robustness of the model. Reduce the instability of financial data due to noise. And this article expands traditional machine learning research methods. The traditional model is compared with other deep learning models. Our model has made very competitive progress in accuracy and loss compared to other models.

References

[1]

M. W. Woolrich, M. Jenkinson, J. M. Brady and S. M. Smith, "Fully Bayesian spatio-temporal modeling of FMRI data," in IEEE Transactions on Medical Imaging, vol. 23, no. 2, pp. 213-231, Feb. 2004.

[2]

E. Fox, E. B. Sudderth, M. I. Jordan and A. S. Willsky, "Bayesian Nonparametric Inference of Switching Dynamic Linear Models," in IEEE Transactions on Signal Processing, vol. 59, no. 4, pp. 1569-1585, April 2011.

Digital Library

[3]

P. M. T. Broersen, "Autoregressive model orders for Durbin's MA and ARMA estimators," in IEEE Transactions on Signal Processing, vol. 48, no. 8, pp. 2454-2457, Aug. 2000.

Digital Library

[4]

B. Maglaris, D. Anastassiou, P. Sen, G. Karlsson and J. D. Robbins, "Performance models of statistical multiplexing in packet video communications," in IEEE Transactions on Communications, vol. 36, no. 7, pp. 834-844, July 1988.

[5]

P. H. Le-Khac, G. Healy and A. F. Smeaton, "Contrastive Representation Learning: A Framework and Review," in IEEE Access, vol. 8, pp. 193907-193934, 2020.

[6]

Chen X, Kingma D P, Salimans T, Variational Lossy Autoencoder[J]. 2016.

[7]

Xu J, Xiang L, Liu Q, Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images[J]. IEEE Transactions on Medical Imaging, 2016, 35(1):119-130.

[8]

Kodirov E, Xiang T, Gong S . Semantic Autoencoder for Zero-Shot Learning[C]// IEEE. IEEE, 2017.

[9]

Ke N Y, Sukthankar R . PCA-SIFT: a more distinctive representation for local image descriptors[C]// IEEE Computer Society Conference on Computer Vision & Pattern Recognition. IEEE Computer Society, 2004.

[10]

Martinez, Aleix, M, PCA versus LDA.[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2001.

[11]

Zakzeski J, Bruijnincx P, Jongerius A L, The catalytic valorization of lignin for the production of renewable chemicals.[J]. Chemical Reviews, 2013, 110(6):3552-3599.

[12]

Bakshi B R . Multiscale PCA with application to multivariate statistical process monitoring[J]. Aiche Journal, 2010, 44(7).

[13]

Luo J, Oubong G . A Comparison of SIFT, PCA-SIFT and SURF[J]. International Journal of Image Processing, 2009.

[14]

Hubert M, Rousseeuw P J, Branden K V . ROBPCA: A New Approach to Robust Principal Component Analysis[J]. Technometrics, 2005, 47(1):64-79.

[15]

Lieb D, Lookingbill A, Thrun S . Adaptive Road Following using Self-Supervised Learning and Reverse Optical Flow[C]// IEEE International Conference on Communications Workshops. DBLP, 2005.

[16]

Sermanet P, Lynch C, Chebotar Y, Time-Contrastive Networks: Self-Supervised Learning from Video[C]// 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2018.

[17]

Cooperstock J R, Milios E E . Self-supervised learning for docking and target reaching[J]. Robotics and Autonomous Systems, 1993, 11(3-4):243-260.

[18]

Patel Y, Gomez L, Gomez R, TextTopicNet - Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces[J]. 2018.

[19]

Heidarsson H K, Sukhatme G S . Obstacle detection from overhead imagery using self-supervised learning for Autonomous Surface Vehicles[C]// Intelligent Robots and Systems (IROS), 2011 IEEE/RSJ International Conference on. IEEE, 2011.

[20]

Nair A, Chen D, Agrawal P, Combining Self-Supervised Learning and Imitation for Vision-Based Rope Manipulation[C]// IEEE. IEEE, 2017.

Cited By

Wang MChen FGuo JJia W(2023)Improving Stock Trend Prediction with Multi-granularity Denoising Contrastive Learning2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191523(1-10)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191523
Wang MWang SGuo JJia W(2023)Improving stock trend prediction with pretrain multi-granularity denoising contrastive learningKnowledge and Information Systems10.1007/s10115-023-02006-166:4(2439-2466)Online publication date: 28-Dec-2023
https://dl.acm.org/doi/10.1007/s10115-023-02006-1

Index Terms

Stock Volatility Forecast Base on Comparative Learning and Autoencoder Framework
1. Theory of computation

Index terms have been assigned to the content through auto-classification.

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ICMVA '22: Proceedings of the 2022 5th International Conference on Machine Vision and Applications

February 2022

128 pages

ISBN:9781450395670

DOI:10.1145/3523111

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 May 2022

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ICMVA 2022

ICMVA 2022: 2022 the 5th International Conference on Machine Vision and Applications

February 18 - 20, 2022

Singapore, Singapore

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Cited By

Wang MChen FGuo JJia W(2023)Improving Stock Trend Prediction with Multi-granularity Denoising Contrastive Learning2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191523(1-10)Online publication date: 18-Jun-2023
https://doi.org/10.1109/IJCNN54540.2023.10191523
Wang MWang SGuo JJia W(2023)Improving stock trend prediction with pretrain multi-granularity denoising contrastive learningKnowledge and Information Systems10.1007/s10115-023-02006-166:4(2439-2466)Online publication date: 28-Dec-2023
https://dl.acm.org/doi/10.1007/s10115-023-02006-1

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