A Hybrid Deep Learning Approach for Systemic Financial Risk Prediction

2020 ◽  
pp. 859-874
Author(s):  
Yue Zhou ◽  
Jinyao Yan
Keyword(s):  
Deep Learning ◽  
Risk Prediction ◽  
Financial Risk ◽  
Learning Approach

Risk Prediction of Lung Disease Using Deep Learning Approach

2021 ◽  
pp. 462-471
Author(s):  
C. Harinath Reddy ◽  
B. V. Koushik Kumar ◽  
N. Sai Teja Varma ◽  
S. Vidya ◽  
P. Nagaraj ◽  
...  
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Risk Prediction ◽  
Learning Approach

scholarly journals Research on Deep Learning-Based Financial Risk Prediction

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Boning Huang ◽  
Junkang Wei
Keyword(s):  
Deep Learning ◽  
Social Network ◽  
Risk Prediction ◽  
Classification Accuracy ◽  
Financial Risk ◽  
Financial Analysis ◽  
Extraction Process ◽  
Sentiment Classification ◽  
Learning Method ◽  
Speed Up

Financial text-based risk prediction is an important subset for financial analysis. Through automatic analysis of public financial comments, fundamentals on current financial expectations can be evaluated. A deep learning method for financial risk prediction based on sentiment classification is proposed in this paper. The proposed method consists of two steps. Firstly, the abstract of the financial message is extracted according to the seq2seq model. During the extraction process, the seq2seq model can cope with the situation of different input message lengths. After the abstraction, invalid information in the financial messages can be effectively filtered, thus accelerating the subsequent sentiment classification step. The sentiment classification step is performed through the GRU model according to the abstracted texts. The proposed method has the following advantages: (1) it can handle financial messages of different lengths; (2) it can filter out the invalid information of financial messages; (3) because the extracted abstract is more refined, it can speed up the subsequent sentiment classification step; and (4) it has better sentiment classification accuracy. The proposed method in this paper is then verified through financial message dataset from the financial social network StockTwits. By comparing the classification performances, it can be seen that compared with the classical SVM and LSTM methods, the proposed method in this paper can improve the accuracy of sentiment classification by 5.57% and 2.58%, respectively.

scholarly journals A survey on deep learning for financial risk prediction

2021 ◽  
Vol 5 (4) ◽  
pp. 716-737
Author(s):  
Kuashuai Peng ◽  
◽  
Guofeng Yan
Keyword(s):  
Deep Learning ◽  
Risk Prediction ◽  
Language Processing ◽  
Financial Risk ◽  
Rapid Development ◽  
Financial Data ◽  
Financial Risks ◽  
Learning Models ◽  
Main Work ◽  
Data Heterogeneity

<abstract> <p>The rapid development of financial technology not only provides a lot of convenience to people's production and life, but also brings a lot of risks to financial security. To prevent financial risks, a better way is to build an accurate warning model before the financial risk occurs, not to find a solution after the outbreak of the risk. In the past decade, deep learning has made amazing achievements in the fields, such as image recognition, natural language processing. Therefore, some researchers try to apply deep learning methods to financial risk prediction and most of the results are satisfactory. The main work of this paper is to review the predecessors' work of deep learning for financial risk prediction according to three prominent characteristics of financial data: heterogeneity, multi-source, and imbalance. We first briefly introduced some classical deep learning models as the model basis of financial risk prediction. Then we analyzed the reasons for these characteristics of financial data. Meanwhile, we studied the differences of commonly used deep learning models according to different data characteristics. Finally, we pointed out some open issues with research significance in this field and suggested the future implementations that might be feasible.</p> </abstract>

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