A Deep Learning-Based Approach to Constructing a Domain Sentiment Lexicon: a Case Study in Financial Distress Prediction

Li, Shixuan; Shi, Wenxuan; Wang, Jiancheng; Zhou, Heshen

doi:10.1016/j.ipm.2021.102673

Cited by 66 publications

(35 citation statements)

References 37 publications

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“…From the perspective of fiscal forewarning, early research mainly depends on personal development experience, and there will always be some deviation in financial alert [2]. With the development of financial alert theory and the introduction of statistical theory, development of big data technology provides new research methods for risk warning in various fields [3,4]. Although there are many researches on enterprise financial alert at home and abroad at this stage, there are still no good migration request research results.…”

Section: Introductionmentioning

confidence: 99%

Application of Optimized BP Neural Network In Financial Alert System

Qiaoyi

2022

Journal of Function Spaces

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The development of economic globalization not only brings development opportunities for enterprise development but also causes the rapid changes of economic environment. Global enterprises are facing many development challenges. It is very significant to implement financial alert. However, most of the existing financial alert systems are based on traditional quantitative analysis and cannot face the demand of big data growth. Based on this, main body of a book studies and analyzes the request of optimized BP neural network in financial alert system. Based on the financial early warning and a brief analysis of the development requirements of BP neural network, this paper establishes a financial early warning model for BP neural network. Considering the shortcomings of BP neural network, genetic algorithm is used to optimize the specification. Finally, a new reference scheme is provided for economic early warning. And use the following steps to create a list, taking into account the volume analysis in the conceptual design. The emulation results indicates that the optimized BP neural network can accelerate the convergence, has strong stability, and has higher accuracy in financial alert.

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Section: Introductionmentioning

confidence: 99%

Application of Optimized BP Neural Network In Financial Alert System

Qiaoyi

2022

Journal of Function Spaces

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“…Stevenson et al (2021) used deep learning and natural language processing (NLP) techniques to assess the credit risk of MSMEs and made some recommendations. Li et al (2021) proposed a deep learning‐based framework for building domain sentiment dictionaries using lexical vector models and deep learning‐based classifiers for financial risk early warning of listed companies in 2021. Dr. Srivastava et al (2021) used deep neural networks and time series methods to predict the volatility of the Indian stock market in 2021, illustrating the bright future of deep learning in multi‐parameter time series forecasting.…”

Section: Related Workmentioning

confidence: 99%

Research on corporate financial performance prediction based on self‐organizing and convolutional neural networks

Zhou

Liu

2022

Expert Systems

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Economic risks faced by manufacturing enterprises are gradually increasing and risk reduction whilst maintaining high financial performance has become key to their survival and development of enterprises. Enterprise performance affects not only enterprise development but also does the interests of investors and creditors. Therefore, a well‐performing model for financial performance prediction is particularly important. In this paper, we combine unsupervised and supervised learning, fusing self‐organizing mapping neural networks and convolutional neural networks, and apply deep learning to financial analysis to construct a new financial performance prediction model, called SNN‐CNN. This paper uses crawler technology to obtain financial data of listed manufacturing enterprises and classifies their financial performance into five levels. It finds that enterprises with high financial performance tend to have balanced financial indicators, strong corporate vitality and stable development of various capabilities, while enterprises with low financial performance have poor repayment and profitability, significant risks in corporate operation and limited growth and development. Compared with traditional risk prediction models, the SOM‐CNN model has a higher accuracy rate, up to 95.69%.

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“…The results of Olafsson's research show that factors such as enterprise size, financial structure, operating performance, and liquidity are highly correlated with the probability of financial distress [ 17 ]. Li et al synthesized the academic definition of financial distress and divided it into four situations: failure, insolvency, default, and bankruptcy [ 18 ].…”

Section: Related Workmentioning

confidence: 99%

Neural Network Technology-Based Optimization Framework of Financial and Management Accounting Model

Zeng

2022

Computational Intelligence and Neuroscience

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Traditional financial accounting has gradually evolved into management accounting in order to adapt to changing times and developments. To avoid being obliterated by the times, accountants must gradually improve their professional and comprehensive abilities in order to create greater value for businesses in the AI (Artificial Intelligence) era. This article presents an AI-based financial management optimization design and proposes an AI-based accounts receivable management optimization framework based on the existing information system. A typical financial distress early-warning model is built using the BPNN (BP Neural Network) model, and the training samples of listed companies’ financial data are processed iteratively using the neural network algorithm to realize the visual modeling of the object-oriented neural network and learn the training samples. Finally, the network’s ability to provide early warning is put to the test. The results show that BPNN’s prediction accuracy is significantly higher than that of other types, especially after years of data, with prediction results exceeding 90%. The results show that the BPNN-based financial early-warning method is feasible.

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A Deep Learning-Based Approach to Constructing a Domain Sentiment Lexicon: a Case Study in Financial Distress Prediction

Cited by 66 publications

References 37 publications

Application of Optimized BP Neural Network In Financial Alert System

Application of Optimized BP Neural Network In Financial Alert System

Research on corporate financial performance prediction based on self‐organizing and convolutional neural networks

Neural Network Technology-Based Optimization Framework of Financial and Management Accounting Model

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