Stock Price Pattern Prediction Based on Complex Network and Machine Learning

Caö, Hongduo; Lin, Tiantian; Li, Ying; Zhang, Hanyu

doi:10.1155/2019/4132485

Cited by 31 publications

(26 citation statements)

References 40 publications

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“…The RMSE of the number of monolayer neurons, learning rate, and batch size are shown in Figure 3. Figure 3(a) illustrates that the RMSE of the number of monolayer neurons is smaller in [10,65]…”

Section: Core Hyperparameter Selection and Value Rangementioning

confidence: 99%

“…The Fintech index is volatile and difficult to be predicted by the traditional time series methods. Machine learning algorithms, such as support vector regression (SVR) [6], genetic algorithm (GA) [7], and deep neural network (DNN) [8], have been widely used recently [9,10]. SVR was introduced by Vapnik originally [11], which has a global optimum, while its hyperparameter selection needs to be determined by the experience of practitioners [5], which has strong subjectivity and may lead to poor performance in prediction [12].…”

Section: Introductionmentioning

confidence: 99%

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Fintech Index Prediction Based on RF‐GA‐DNN Algorithm

Liu

Fan

Zhu

2021

Wireless Communications and Mobile Computing

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The Fintech index has been more active in the stock market with the Fintech industry expanding. The prediction of the Fintech index is significant as it is capable of instructing investors to avoid risks and provide guidance for financial regulators. Traditional prediction methods adopt the deep neural network (DNN) or the combination of genetic algorithm (GA) and DNN mostly. However, heavy computational load is required by these algorithms. In this paper, we propose an integrated artificial intelligence-based algorithm, consisting of the random frog algorithm (RF), GA, and DNN, to predict the Fintech index. The proposed RF-GA-DNN prediction algorithm filters the key input variables and optimizes the hyperparameters of DNN. We compare the proposed RF-GA-DNN with the traditional GA-DNN in terms of convergence time and prediction accuracy. Results show that the convergence time of GA-DNN is up to 20 hours and its prediction accuracy is 97.4%. In comparison, the convergence time of our RF-GA-DNN is only about 1.5 hours and the prediction accuracy reaches 97.0%. These results demonstrate that the proposed RF-GA-DNN prediction algorithm significantly reduces the convergence time with the promise of competitive prediction accuracy. Thus, the proposed algorithm deserves to be widely recommended for predicting the Fintech index.

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Section: Core Hyperparameter Selection and Value Rangementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fintech Index Prediction Based on RF‐GA‐DNN Algorithm

Liu

Fan

Zhu

2021

Wireless Communications and Mobile Computing

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“…Recently, Deep Learning is getting more attention in the field of EDM and has been thoroughly explored in [29]. In another study in [30], complex networks in stock market and stock price volatility pattern are combined with machine learning to predict stock price patterns. SVM and KNN algorithms are used and have achieved an accuracy of 70%.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

et al. 2020

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Organizations can grow, succeed, and sustain if their employees are committed. The main assets of an organization are those employees who are giving it a required number of hours per month, in other words, those employees who are punctual towards their attendance. Absenteeism from work is a multibillion-dollar problem, and it costs money and decreases revenue. At the time of hiring an employee, organizations do not have an objective mechanism to predict whether an employee will be punctual towards attendance or will be habitually absent. For some organizations, it can be very difficult to deal with those employees who are not punctual, as firing may be either not possible or it may have a huge cost to the organization. In this paper, we propose Neural Networks and Deep Learning algorithms that can predict the behavior of employees towards punctuality at workplace. The efficacy of the proposed method is tested with traditional machine learning techniques, and the results indicate 90.6% performance in Deep Neural Network as compared to 73.3% performance in a single-layer Neural Network and 82% performance in Decision Tree, SVM, and Random Forest. The proposed model will provide a useful mechanism to organizations that are interested to know the behavior of employees at the time of hiring and can reduce the cost of paying to inefficient or habitually absent employees. This paper is a first study of its kind to analyze the patterns of absenteeism in employees using deep learning algorithms and helps the organization to further improve the quality of life of employees and hence reduce absenteeism.

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“…Our third contribution is methodological. In recent years, the methods such as game theory and machine learning have been applied more and more in the field of economics, but few people apply them to the analysis of financing efficiency [34][35][36][37]. Random forest is an ensemble machine learning methods of classification and regression proposed by Leo Breiman [38].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning

Liu

Zhan

2019

Complexity

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Agricultural enterprises play a significant role in China’s economic development. However, compared with other enterprises, agricultural enterprises are facing serious financial problems. Financing difficulty is essentially a question of financing efficiency. Based on the DEA method, this paper evaluates the financing efficiency of 39 agricultural listed companies in China from 2013 to 2017. The results suggest that the financing efficiency is generally low, and the Total Factor Productivity of agricultural enterprises’ financing has a tendency to decrease first and then increase. The influencing factors of financing efficiency are analyzed using the Tobit regression model and the random forest regression model. And we find the following: (1) The random forest regression model significantly outperformed the Tobit regression model, with determination coefficients (R2) greater than 0.9 in full sample sets. (2) Total liability, financial expenses, return on total assets, and inventory turnover rate are important factors affecting financing efficiency of agricultural listed companies. (3) Return on total assets and inventory turnover rate promote the financing efficiency, while total liability and financial expenses reduce financing efficiency. Finally, the paper makes some suggestions for the financing of agricultural enterprises.

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Stock Price Pattern Prediction Based on Complex Network and Machine Learning

Cited by 31 publications

References 40 publications

Fintech Index Prediction Based on RF‐GA‐DNN Algorithm

Fintech Index Prediction Based on RF‐GA‐DNN Algorithm

An Enhanced Deep Neural Network for Predicting Workplace Absenteeism

Analysis of Financing Efficiency of Chinese Agricultural Listed Companies Based on Machine Learning

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