Indian stock market analysis and prediction using LSTM model during COVID-19

Saravagi, Deepika; Agrawal, Shweta; Saravagi, Manisha

doi:10.1504/ijesms.2021.115532

Cited by 13 publications

(5 citation statements)

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“…Various researchers and practitioners have employed CNNs for pattern classification and function approximations [ 12 – 14 ]. They have utilized the UCI machine learning dataset [ 15 ] to categorize the vertebral column diseases using different approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Diagnosis of Lumbar Spondylolisthesis Using a Pruned CNN Model

Saravagi¹,

Agrawal²,

Saravagi

et al. 2022

Computational and Mathematical Methods in Medicine

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Convolutional neural network (CNN) models have made tremendous progress in the medical domain in recent years. The application of the CNN model is restricted due to a huge number of redundant and unnecessary parameters. In this paper, the weight and unit pruning strategy are used to reduce the complexity of the CNN model so that it can be used on small devices for the diagnosis of lumbar spondylolisthesis. Experimental results reveal that by removing 90% of network load, the unit pruning strategy outperforms weight pruning while achieving 94.12% accuracy. Thus, only 30% (around 850532 out of 3955102) and 10% (around 251512 out of 3955102) of the parameters from each layer contribute to the outcome during weight and neuron pruning, respectively. The proposed pruned model had achieved higher accuracy as compared to the prior model suggested for lumbar spondylolisthesis diagnosis.

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Section: Literature Reviewmentioning

confidence: 99%

Diagnosis of Lumbar Spondylolisthesis Using a Pruned CNN Model

Saravagi¹,

Agrawal²,

Saravagi

et al. 2022

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

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“…During network training, Dropout would set an inactivation probability for neurons on each layer of the network. Based on it, the network automatically eliminates the neurons to simplify the network structure and avoid overfitting [18][19][20][21][22][23]. For the gradient vanishing problem, the BN algorithm is introduced.…”

Section: Basic Structurementioning

confidence: 99%

Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM

Yang

Jia

Liu

2022

Security and Communication Networks

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In view of the financial risks faced by listed enterprises, how to accurately predict the risks is an important work. However, the traditional LSTM financial diagnosis model has the disadvantage of low accuracy; the specific reason is that the LSTM model has the problems of overfitting and gradient disappearance in risk diagnosis. Therefore, Dropout is adopted to solve the overfitting problem in the process of premodel prediction, and the BN algorithm is used to solve the gradient disappearance problem in the process of iteration. In order to verify the feasibility of above improvements, the financial data of China’s A-share listed enterprises from 2017 to 2020 are taken as samples to analyze the financial data of listed enterprises through single-step dimension and multistep dimension. The experimental results show that under the analysis of two dimensions, the financial prediction accuracy of the improved LSTM for T-2∼T-3 years can reach 83.96% and 91.19%, respectively, which indicates that through the above improvements, the model can be improved and has certain reference value.

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“…Then, the data matrix is standardized to remove differences in magnitude between different indicators. Next, the correlation coefficient matrix of the standardized data matrix is calculated to determine each indicator's variance contribution rate [16], and the indicators that make the cumulative variance contribution rate exceed 85% are used as principal components. The variance contribution rate can reflect the degree to which various indicators affect stock returns during the process of change in this article.…”

Section: Predictive Models For Stock Returnsmentioning

confidence: 99%

Application of PCA-LSTM algorithm for financial market stock return prediction and optimization model

Mi,

Xu,

Gao

2023

Int. J. Simul. Multidisci. Des. Optim.

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Accurately predicting stock returns can help reduce market risk. This paper briefly introduced the long short-term memory (LSTM) algorithm model for predicting stock returns and combined it with principal component analysis (PCA) to improve the prediction accuracy. Simulation experiments were conducted on 80 stocks, and the PCA-LSTM model was compared with back-propagation neural network (BPNN) and LSTM models. The results showed that the PCA analysis method effectively identified the principal components of variable indicators. During the training iteration convergence, the PCA-LSTM model not only converged faster but also had smaller errors after stabilization. Moreover, the PCA-LSTM model had the highest prediction accuracy, the LSTM model was the second, and the BPNN model was the worst.

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Indian stock market analysis and prediction using LSTM model during COVID-19

Cited by 13 publications

References 0 publications

Diagnosis of Lumbar Spondylolisthesis Using a Pruned CNN Model

Diagnosis of Lumbar Spondylolisthesis Using a Pruned CNN Model

Construction and Simulation of the Enterprise Financial Risk Diagnosis Model by Using Dropout and BN to Improve LSTM

Application of PCA-LSTM algorithm for financial market stock return prediction and optimization model

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