Big data analytics for financial Market volatility forecast based on support vector machine

Yang, Rongjun; Lin, Yanfen; Zhao, Yuanjun; Yu, Hongxin; Xu, Gui-Ping; Wu, Yiting; Liu, Zhengkai

doi:10.1016/j.ijinfomgt.2019.05.027

Cited by 100 publications

(47 citation statements)

References 71 publications

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“…(3) Select the operator. In the process of building genetic algorithm model, operator selection is the key link, which is closely related to the effectiveness of genetic algorithm [16]. The designers of genetic algorithm model need to select the operators that meet the requirements scienti cally and reasonably, so as to improve the performance of genetic algorithm to the greatest extent.…”

Section: Running Process Of Genetic Algorithmmentioning

confidence: 99%

Research on Investment Portfolio Model Based on Neural Network and Genetic Algorithm

Wei

Zhao

Chen

et al. 2020

Preprint

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With the maturity of neural network theory, it provides new ideas and methods for the prediction and analysis of stock market investment. The purpose of this paper is to improve the accuracy of stock market investment prediction, we build neural network model and genetic algorithm model, study the law of stock market operation, and improve the effectiveness of neural network and genetic algorithm. Through the empirical research, it is found that the neural network model can make up for the shortcomings of the traditional algorithm through the optimization of genetic algorithm.

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Section: Running Process Of Genetic Algorithmmentioning

confidence: 99%

Research on Investment Portfolio Model Based on Neural Network and Genetic Algorithm

Wei

Zhao

Chen

et al. 2020

Preprint

Self Cite

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“…The essence of the whole process is random search, which has great uncertainty [15]. The operation process of genetic algorithm is continuously optimized under the expected value of the optimal solution, and it is regarded as the initial sequence [16]. Through the convergence theory of genetic algorithm, its convergence can be effectively verified.…”

Section: Related Workmentioning

confidence: 99%

Research on investment portfolio model based on neural network and genetic algorithm in big data era

Zhou

Zhao

Chen

et al. 2020

J Wireless Com Network

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“…Time series forecasting aims at solving various problems, specifically in the financial field [ 2 ]. In the course of financial market development, a large number of studies have shown that the market is non-linear and chaotic [ 3 , 4 , 5 ], especially for the financial time series data such as the values of stocks, foreign exchanges, and commodities in the financial market that are sensitive to external impact and tend to fluctuate violently. Such time series data often have strong non-linear characteristics [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

A Labeling Method for Financial Time Series Prediction Based on Trends

Wang

et al. 2020

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Time series prediction has been widely applied to the finance industry in applications such as stock market price and commodity price forecasting. Machine learning methods have been widely used in financial time series prediction in recent years. How to label financial time series data to determine the prediction accuracy of machine learning models and subsequently determine final investment returns is a hot topic. Existing labeling methods of financial time series mainly label data by comparing the current data with those of a short time period in the future. However, financial time series data are typically non-linear with obvious short-term randomness. Therefore, these labeling methods have not captured the continuous trend features of financial time series data, leading to a difference between their labeling results and real market trends. In this paper, a new labeling method called “continuous trend labeling” is proposed to address the above problem. In the feature preprocessing stage, this paper proposed a new method that can avoid the problem of look-ahead bias in traditional data standardization or normalization processes. Then, a detailed logical explanation given, a definition of continuous trend labeling was proposed and also an automatic labeling algorithm was given to extract the continuous trend features of financial time series data. Experiments on the Shanghai Composite Index and Shenzhen Component Index and some stocks of China showed that our labeling method is a much better state-of-the-art labeling method in terms of classification accuracy and some other classification evaluation metrics. The results of the paper also proved that deep learning models such as LSTM and GRU are more suitable for dealing with the prediction of financial time series data.

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Big data analytics for financial Market volatility forecast based on support vector machine

Cited by 100 publications

References 71 publications

Research on Investment Portfolio Model Based on Neural Network and Genetic Algorithm

Research on Investment Portfolio Model Based on Neural Network and Genetic Algorithm

Research on investment portfolio model based on neural network and genetic algorithm in big data era

A Labeling Method for Financial Time Series Prediction Based on Trends

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