Composite Financial Performance Index Prediction – A Neural Networks Approach

Popa, D.; Popa, Dorina Nicoleta; Bogdan, Victoria; Simuț, Ramona

doi:10.3846/jbem.2021.14000

Cited by 18 publications

(5 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Financial performance is a necessary determinant in measuring a firm's overall performance (Popa et al, 2021). It also affects the managers, investors, and shareholders' decision making (Salehi et al, 2021).…”

Section: Methodsmentioning

confidence: 99%

“…Altman (1968) Other than traditional statistical and economic methods, machine learning and deep learning methods are also effective (Salehi et al, 2021). Popa et al (2021) found that the recurrent neural network model can obtain better accuracy and lower error for predicting financial performance composite index when using seven-year data instead of just one-year data. Lam (2004) applied 16 financial statement variables and 11 macroeconomic variables as predictors to build return on common shareholders' equity return prediction by neural network technique.…”

Section: Financial Performance Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Applying sustainable development goals in financial forecasting using machine learning techniques

Chang,

Lee,

Lee

2023

Corp Soc Responsibility Env

View full text Add to dashboard Cite

This study seeks to identify the impact of sustainable development goals (SDGs) in predicting corporate financial performance (CFP) in the information communications technology (ICT) industry. Data over the period of 2016–2020 that are relevant to financial reporting and corporate social responsibility (CSR) reporting have been extracted for 208 firms in the ICT industry. Important variables have been identified to help predict the financial performance in the following years upon the publication of CSR reports. Drawing on resource‐based view and stakeholder theory, the purpose of this study is to find the quintessential variables that influence the prediction accuracy of financial performance. To better forecast earnings per share (EPS), machine learning feature selection methods have been implemented. The findings suggest that certain variables such as return on total assets, SDGs adoption and whether the firm has established KPI for SDGs achievements can help enhance EPS prediction. With the various predictive models, the artificial neural network model is the most effective in predicting CFPs. Most importantly, the adoption of SDGs can be utilized to sharpen the forecast on financial performance as it enables firms to bolster stakeholder engagement and evaluate environmental, social, and corporate governance efforts.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Financial Performance Predictionmentioning

confidence: 99%

Applying sustainable development goals in financial forecasting using machine learning techniques

Chang,

Lee,

Lee

2023

Corp Soc Responsibility Env

View full text Add to dashboard Cite

show abstract

“…BPNN can use different training functions, including trainlm, traingc, and traingd [45]. Due to the moderate model size, comparative analysis is conducted and the trainlm function is selected to improve the algorithm's efficiency [46], [47]. In conclusion, Figure 4 illustrates the algorithm flow of BPNN in FREW for listed companies.…”

Section: Construction Of a Frew Model For Listed Companies Based On Bpnnmentioning

confidence: 99%

Financial Risk Early Warning Model for Listed Companies Using BP Neural Network and Rough Set Theory

Liu,

Yang

2024

IEEE Access

View full text Add to dashboard Cite

In current financial environment, listed companies are facing increasingly complex markets and ever-changing financial risks. The companies deeply recognize the crucial role of financial risk management in the sustainable development of enterprises. The challenge lies in rapidly changing market conditions, and traditional methods are difficult to predict risks in a timely and accurate manner. Therefore, improving the accuracy and timeliness of financial risk prediction has become an urgent need in the current field to reduce potential losses and maintain the financial health of enterprises. This work aims to enhance the accuracy and timeliness of predicting financial risks for listed companies and reduce potential losses caused by these risks. In the research process, a large volume of data is initially collected, including financial statements, market data, and financial risk event data. Subsequently, the Rough Set Theory (RST) is employed for feature selection to identify financial indicators and market factors highly relevant to financial risk. Finally, a financial risk early warning model on the basis of the Back Propagation Neural Network (BPNN) is built, and then trained and optimized using historical data. Cross-validation analysis is employed to assess the model's performance, and the model is compared with traditional financial risk early warning methods. The findings reveal that the financial risk early warning model based on RST and the BPNN demonstrates high accuracy and reliability in predicting financial risks for listed companies. The model exhibits excellent performance in terms of accuracy, recall, and F1 score, achieving rates of 96%, 95%, and 95.50%, respectively. These research findings are expected to positively impact the financial sector and provide financial decision-makers with more accurate risk early warning and decision support.

show abstract

“…Against the characteristics of China's financial system and the regional characteristics of RFRs, the CI method is used to construct the RFRsoriented EIS. It is used as the basic variable of the financial risk EW model [28]. The CI method can be converted according to variable changes and combined with various risk identification and EW models.…”

Section: Plos Onementioning

confidence: 99%

Early warning model and prevention of regional financial risk integrated into legal system

Zhuang

Wei

2023

PLoS ONE

View full text Add to dashboard Cite

In order to improve the laws and regulations of the financial system, in the construction of laws and regulations, the traditional financial risk Early Warning (EW) model is optimized. The financial prevention and control measures with legal protection are implemented to warn the financial risks, which plays an important role in the construction of the rule of law in the Financial Market (FM) and the establishment of financial risk prevention and control laws and regulations. This paper combines the deep learning model and the Markov regime Switching Vector Auto Regression (MS-VAR) model and constructs a regional financial risk EW model from the following aspects: macroeconomic operation EW indicators, regional economic risk EW indicators, regional financial institution risk EW indicators. The model is empirically researched and analyzed. The results show that the fluctuation trend of the macroeconomic pressure index in the time series is relatively large, and the overall fluctuation of the regional economic pressure index is small, and fluctuates around 0 in most periods. After the financial crisis, local governments stepped up their supervision of non-performing corporate and household loans. From 2011 to 2018, the non-performing loan ratio began to decline, and the overall fluctuation of the regional financial comprehensive stress index was small, fluctuating around 0. Due to the lack of legal regulation, from the perspective of the regional economy, the risk level is more likely to change from low risk to moderate risk, while the risk status is less likely to change from high risk to moderate risk. From the perspective of regional financial institutions, the probabilities of maintaining low risk and moderate risk are 0.98 and 0.97, respectively, which is stronger than maintaining the stability of high risk. From the perspective of the state transition of the regional financial risk composite index, the probability of maintaining low risk and high risk is 0.97 and 0.93, which is higher than maintaining the stability of medium risk. The Deep Learning (DL) regional financial risk EW MS-VAR model has strong risk prediction ability. The model can better analyze the conversion probability of regional financial risk EW index and has better risk EW ability. This paper enhances the role of legal systems in financial risk prevention and control. The regional financial risk EW model incorporating financial legal indicators can better describe the regional financial risk level, and the EW results are basically consistent with the actual situation. In order to effectively prevent financial risks and ensure the safety of the financial system, it is recommended that the government improve local debt management, improve financial regulations and systems, and improve the legislative level of financial legal supervision.

show abstract

Composite Financial Performance Index Prediction – A Neural Networks Approach

Cited by 18 publications

References 53 publications

Applying sustainable development goals in financial forecasting using machine learning techniques

Applying sustainable development goals in financial forecasting using machine learning techniques

Financial Risk Early Warning Model for Listed Companies Using BP Neural Network and Rough Set Theory

Early warning model and prevention of regional financial risk integrated into legal system

Contact Info

Product

Resources

About