An Efficient Stock Market Prediction Method Based on Kalman Filter

Deepika, N.; Bhat, M. Nirupama

doi:10.1007/s40031-021-00583-9

Cited by 13 publications

(11 citation statements)

References 29 publications

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“…Whereas, it can be seen from Table 8, that our method attained an accuracy value of 92.5% which is higher than all techniques under comparison. More specifically, the methods in [47][48][49][50] show an average accuracy value of 75.4%, which is 92.5% in our case, therefore, the presented framework exhibits an average performance gain of 17.1% approx.…”

Section: Comparison With State-of-the-arts Methodsmentioning

confidence: 62%

“…The work in [47] attained an average accuracy value of 54.58%. In contrast, the method in [48] introduced Kalman filter-based Accelerated Gradient LSTM to determine the future move-ments of stock market and showed an average accuracy of 90.42%. Similarly, in [50] the author proposed the LSTMbased model for stock market prediction and obtained an average accuracy of 66.83%.…”

Section: Comparison With State-of-the-arts Methodsmentioning

confidence: 99%

“…Here, we have examined the stock future trends prediction accuracy of our approach with the other modern approaches using the same dataset. To show the performance comparison, we have performed the comparative analysis of our approach by comparing its average highest accuracy results with the average highest prediction results of the approaches mentioned in [47][48][49][50]. The obtained quantitative results are shown in Table 8.…”

Section: Comparison With State-of-the-arts Methodsmentioning

confidence: 99%

“…For the reported quantitative results, it can be said that our method is more robust to stock market movements prediction than the other approaches, due to the employment of DCWR which results in the more discriminative set of feature selection. Furthermore, the techniques in [47][48][49][50] are economically inefficient and suffering from the model overfitting problem. Whereas our method applies HANet which is capable of dealing with over-fitted training data and assists in attaining effective prediction accuracy.…”

Section: Comparison With State-of-the-arts Methodsmentioning

confidence: 99%

See 3 more Smart Citations

A deep learning method DCWR with HANet for stock market prediction using news articles

Albahli

Awan

Nazir

et al. 2022

Complex Intell. Syst.

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Stock movement prediction is a challenging problem to analyze in both academic and financial research areas. The advancement of deep learning (DL) techniques has grasped the attention of researchers to employ them for predicting the stock market’s future trends. Few frameworks can understand the financial terms in literature, and the volatile nature of stock markets further complicates this process. This paper has tried to overcome the existing challenges by introducing a DL-based framework using financial news articles to forecast the stock market. After performing preprocessing step, the deep contextualized word representation (DCWR) approach is applied for feature extraction. In the next step, the independent component analysis (ICA) method is used for feature reduction. Finally, the resultant features train the hierarchical attention networks (HANet) classifier to predict the stock movements. The proposed scheme is evaluated over the 7 years of data from a publicly available dataset gathered from the Reuter’s website and attained an average prediction accuracy of 92.5% which shows our framework’s robustness.

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Section: Comparison With State-of-the-arts Methodsmentioning

confidence: 62%

Section: Comparison With State-of-the-arts Methodsmentioning

confidence: 99%

Section: Comparison With State-of-the-arts Methodsmentioning

confidence: 99%

Section: Comparison With State-of-the-arts Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A deep learning method DCWR with HANet for stock market prediction using news articles

Albahli

Awan

Nazir

et al. 2022

Complex Intell. Syst.

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show abstract

“…Deepika et al [ 45 ] proposed a model of accelerated gradient LSTM where the Kalman filter is applied to reduce the noise and errors of data. The study was applied to predict the stock market where the data were collected from Twitter and Yahoo.…”

Section: Related Workmentioning

confidence: 99%

Improving Sentiment Analysis for Social Media Applications Using an Ensemble Deep Learning Language Model

Alsayat

2021

Arab J Sci Eng

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As data grow rapidly on social media by users’ contributions, specially with the recent coronavirus pandemic, the need to acquire knowledge of their behaviors is in high demand. The opinions behind posts on the pandemic are the scope of the tested dataset in this study. Finding the most suitable classification algorithms for this kind of data is challenging. Within this context, models of deep learning for sentiment analysis can introduce detailed representation capabilities and enhanced performance compared to existing feature-based techniques. In this paper, we focus on enhancing the performance of sentiment classification using a customized deep learning model with an advanced word embedding technique and create a long short-term memory (LSTM) network. Furthermore, we propose an ensemble model that combines our baseline classifier with other state-of-the-art classifiers used for sentiment analysis. The contributions of this paper are twofold. (1) We establish a robust framework based on word embedding and an LSTM network that learns the contextual relations among words and understands unseen or rare words in relatively emerging situations such as the coronavirus pandemic by recognizing suffixes and prefixes from training data. (2) We capture and utilize the significant differences in state-of-the-art methods by proposing a hybrid ensemble model for sentiment analysis. We conduct several experiments using our own Twitter coronavirus hashtag dataset as well as public review datasets from Amazon and Yelp. For concluding results, a statistical study is carried out indicating that the performance of these proposed models surpasses other models in terms of classification accuracy.

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Predicting Stock Market Movement Employing Data Filtration and Gradient Boost Algorithm

Verma¹,

Soni²

2022

Hybrid Intelligent Systems

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An Efficient Stock Market Prediction Method Based on Kalman Filter

Cited by 13 publications

References 29 publications

A deep learning method DCWR with HANet for stock market prediction using news articles

A deep learning method DCWR with HANet for stock market prediction using news articles

Improving Sentiment Analysis for Social Media Applications Using an Ensemble Deep Learning Language Model

Predicting Stock Market Movement Employing Data Filtration and Gradient Boost Algorithm

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