Fine-tuned support vector regression model for stock predictions

Dash, Ranjan Kumar; Nguyen, Ngoc‐Tu; Cengiz, Korhan; Sharma, Aditi

doi:10.1007/s00521-021-05842-w

Cited by 69 publications

(27 citation statements)

References 30 publications

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“…In the last few years, the tendency to use artificial intelligence-based stock forecasting models, specifically deep learning models, has increased [21,46]. However, there are also some other traditional artificial intelligence-based models, like support vector and linear regression analyses, which are used for this purpose [77,78]. Still, finding the most suitable model for stock forecasting is a vital area of research.…”

Section: Discussionmentioning

confidence: 99%

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Maqsood

Yasmin

et al. 2022

Systems

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Stock exchange analysis is regarded as a stochastic and demanding real-world setting in which fluctuations in stock prices are influenced by a wide range of aspects and events. In recent years, there has been a great deal of interest in social media-based data analytics for analyzing stock exchange markets. This is due to the fact that the sentiments around major global events like Brexit or COVID-19 significantly affect business decisions and investor perceptions, as well as transactional trading statistics and index values. Hence, in this research, we examined a case study from the Brexit event to assess the influence that feelings on the subject have had on the stock markets of European Union (EU) nations. Brexit has implications for Britain and other countries under the umbrella of the European Union (EU). However, a common point of debate is the EU’s contribution preferences and benefit imbalance. For this reason, the Brexit event and its impact on stock markets for major contributors and countries with minimum donations need to be evaluated accurately. As a result, to achieve accurate analysis of the stock exchanges of different EU nations from two different viewpoints, i.e., the major contributors and countries contributing least, in response to the Brexit event, we suggest an optimal deep learning and machine learning model that incorporates social media sentiment analysis regarding Brexit to perform stock market prediction. More precisely, the machine learning-based models include support vector machines (SVM) and linear regression (LR), while convolutional neural networks (CNNs) are used as a deep learning model. In addition, this method incorporates around 1.82 million tweets regarding the major contributors and countries contributing least to the EU budget. The findings show that sentiment analysis of Brexit events using a deep learning model delivers better results in comparison with machine learning models, in terms of root mean square values (RMSE). The outcomes of stock exchange analysis for the least contributing nations in relation to the Brexit event can aid them in making stock market judgments that will eventually benefit their country and improve their poor economies. Likewise, the results of stock exchange analysis for major contributing nations can assist in lowering the possibility of loss in relation to investments, as well as helping them to make effective decisions.

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Section: Discussionmentioning

confidence: 99%

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Maqsood

Yasmin

et al. 2022

Systems

View full text Add to dashboard Cite

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“…Xu et al [54] proposed a new fusion method by combining the k-mean clustering and ensemble method (i.e., SVM and RF). In order to reduce the influence of parameters, Dash et al [55] proposed a new stock price prediction method named fine-tuned SVR, which combines the grid search technique and SVR. e grid search technique is used to select the best kernel function and tune the optimized parameters through training and validation datasets.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Chen

Fang

Liang

et al. 2021

Scientific Programming

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Financial data as a kind of multimedia data contains rich information, which has been widely used for data analysis task. However, how to predict the stock price is still a hot research problem for investors and researchers in financial field. Forecasting stock prices becomes an extremely challenging task due to high noise, nonlinearity, and volatility of the stock price time series data. In order to provide better prediction results of stock price, a new stock price prediction model named as CNN-BiLSTM-ECA is proposed, which combines Convolutional Neural Network (CNN), Bidirectional Long Short-term Memory (BiLSTM) network, and Attention Mechanism (AM). More specifically, CNN is utilized to extract the deep features of stock data for reducing the influence of high noise and nonlinearity. Then, BiLSTM network is employed to predict the stock price based on the extracted deep features. Meanwhile, a novel Efficient Channel Attention (ECA) module is introduced into the network model to further improve the sensitivity of the network to the important features and key information. Finally, extensive experiments are conducted on the three stock datasets such as Shanghai Composite Index, China Unicom, and CSI 300. Compared with the existing methods, the experimental results verify the effectiveness and feasibility of the proposed CNN-BILSTM-ECA network model, which can provide an important reference for investors to make decisions.

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“…With an intelligent edge, distributed or decentralized system components can perform many types of data processing, which can be managed at a center located in a system [3]. Notably, IoT o ers several notable drawbacks for a conventional paradigm for routing numerous data channels from IoT-connected equipment into a central database [4]. It is potentially wasteful and can make the system extra insecure if the information is not secured.…”

Section: Intelligent Edge Processing Technique and Network Componentsmentioning

confidence: 99%

Collaborative Computation for Offloading and Caching Strategy Using Intelligent Edge Computing

Zheng¹,

Wei²,

Liu³

2022

Mobile Information Systems

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Computation offloading and caching strategy is a well-established concept for allowing mobile applications that are high in resources. Furthermore, the unloaded duties can be replicated when several customers are within easy access because of the rising mobile cooperation applications. However, the problematic characteristics of offloading and caching strategy delay bandwidth transfer from mobile computing devices to cloud computing. A new technical approach to restrict the issues and unwanted functions in offloading and caching is called the intellectual power computing framework (IPCF). IPCF depends on two conventional offloading and caching strategies called systematic offloading technique and managerial migrant caching. The migration of data transfer from the destination to location basis lies in the systematic offloading technique to restrict network delays. Managerial migrant caching duplicates the data required by the mobile terminals (MTs) from the remote cloud storage to the mobile application to reduce the access time. The forbidden actions in current techniques are refused, and solutions are enhanced for better communication strategy. Thus, the simulation analysis performs better in IPCF to reach efficient outcomes for offloading and caching processes.

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Fine-tuned support vector regression model for stock predictions

Cited by 69 publications

References 30 publications

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Analyzing the Stock Exchange Markets of EU Nations: A Case Study of Brexit Social Media Sentiment

Stock Price Forecast Based on CNN-BiLSTM-ECA Model

Collaborative Computation for Offloading and Caching Strategy Using Intelligent Edge Computing

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