Risk factor analysis combined with deep learning in the risk assessment of overseas investment of enterprises

Xu, Xiuyan

doi:10.1371/journal.pone.0239635

Cited by 6 publications

(3 citation statements)

References 29 publications

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“…With this faster iteration e ciency were said to be attained. Yet another deep learning method was introduced in [20] to analyze and validate various risks involved by means of numerous factors. Also as categorical data cannot be utilized in a direct manner, numerical data conversion was performed in [21] employing one-hot encoding function.…”

Section: Organizationmentioning

confidence: 99%

Box Jenkins Neural Network Keynecian Reinforcement Learning Based Financial Big Data Analysis for Optimal Prediction

Zhang

2022

Preprint

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In the digital era, Economic and Finance information is huge and attainable. The financial industry is a key to stimulating the evolution of the national economy, and with the immense availability of huge data makes the process of financial data analysis both a time consuming and tedious decision making in the financial market. By analyzing this Economic and Finance information significantly and easily, valuable perceptions can be provided. For rational economic growth, information pertaining to economic affairs and likelihoods is critical to decision-makers like governmental bodies, financial institutions and banks. However, precise predictions have been demand owing to the intricacy and unpredictability of financial and economic systems in the midst of persistent changes in economic environments. This work provides to approaches for better economic prediction and decision-making using a novel method called, Multivariate Box Jenkins Neural Network-based Keynecian Reinforcement Learning (MBJNN-KRL). In this work, Financial Data Analysis Deep Economic Prediction model that contains a Multivariate Box Jenkins Long Short Term Memory Forecasting model, for better economic prediction and convergence speed even in case of large data is first designed. The deep reinforcement learning technique is then adapted to retrain neural networks and rebalance the aggregate expenditure periodically. We combined the multivariate Multivariate Box Jenkins Long Short Term Memory Forecasting and the Keynecian Reinforcement Learning models into a single integrated reinforcement learning model named Multivariate Box Jenkins Neural Network-based Keynecian Reinforcement Learning (MBJNN-KRL). Finally, distinct sets of experiments are carried out on real large-scale data set, and the results entirely prove the efficiency and robustness of the proposed method in arbitrary economic prediction based on financial data. Our empirical data revealed that the MBJNN-KRL method could attain combative financial performance compared to traditional methods in terms of predictive accuracy, convergence speed and prediction error.

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Section: Organizationmentioning

confidence: 99%

Box Jenkins Neural Network Keynecian Reinforcement Learning Based Financial Big Data Analysis for Optimal Prediction

Zhang

2022

Preprint

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“…However, with the policy guidance of International Financial Reporting Standards (IFRS), overseas investment has been greatly affected. IFRS can promote the healthy development of overseas investment and effectively prevent the results of various risks, which play a key role in strengthening the safety and prudence of overseas investment in the capital market ( Xu, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Risk Assessment of Haier Group’s Overseas Investment Under International Financial Reporting Standards

et al. 2022

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With the development of economic globalization and the policy guidance of International Financial Reporting Standards (IFRS), the overseas investment of Chinese enterprises has been greatly affected. To study the overseas investment risks of Chinese enterprises, this paper applies a risk analysis model to summarize and analyze the results of overseas investment of Haier from 2008 to 2020. This paper defines the risk analysis model as risk identification, risk assessment, and risk response, and studies overseas investment risks including political, economic, cultural, legal, cost, epidemic, and business risks. (1) The existence of various risks is identified in the company’s overseas investment projects; (2). The overseas investment risks of Haier are evaluated, through a comprehensive evaluation method combining qualitative and quantitative, financial data, and market response; (3). In view of the deficiencies of the existing risk prevention measures in the overseas investment of enterprises, some reasonable suggestions are purposed. The innovation of this paper are: This paper combines IFRS 9, IFRS 15, and IFRS 16, selects theories, and combines cases to evaluate the risks of overseas investment that are difficult to quantify, and analyzes the risks of overseas investment by Z-value models and market effects. This research method has pertinence and applicability to enterprises investing overseas.

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“…Another group of methods widespread in risk assessment are methods based on neural network approaches. For example, the deep neural network was used in [7] to evaluate the overseas investment risks of enterprises. Likewise, Zhang [8] used the fuzzy neural network model of artificial intelligence method to assess credit risk of enterprises.…”

Section: Introductionmentioning

confidence: 99%

Selection of Optimal Investment Variant Based on Monte Carlo Simulations

Janeková¹,

Fabianová²,

Kádárová³

2021

Int. j. simul. model.

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This paper presents a methodology for the selection of an optimal investment variant using Monte Carlo simulation and OptQuest optimization. The decision-making process also includes risk analysis. Investment variants involve renewal and development of production equipment. Two approaches to investment decision making are introduced. The first approach is based on the analysis of the distribution function of Net Present Value (NPV), and the rule of mean value and coefficient of variation is used as the decision criterion for determining the profitability of investment variants. The second approach, based on the cumulative probability distribution of NPV, provides a comparative assessment of the investment variants using stochastic dominance rules. Both approaches lead to the choice of the same investment variant. In order to increase the profitability of the selected investment variant and reduce its risk, OptQuest optimization is subsequently implemented. The introduced approaches can be a useful support tool in investment decision-making.

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Risk factor analysis combined with deep learning in the risk assessment of overseas investment of enterprises

Cited by 6 publications

References 29 publications

Box Jenkins Neural Network Keynecian Reinforcement Learning Based Financial Big Data Analysis for Optimal Prediction

Box Jenkins Neural Network Keynecian Reinforcement Learning Based Financial Big Data Analysis for Optimal Prediction

Risk Assessment of Haier Group’s Overseas Investment Under International Financial Reporting Standards

Selection of Optimal Investment Variant Based on Monte Carlo Simulations

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