What firm risk factors drive bank loan pricing and other terms? Evidence from China

Jin, Hong-min; Wang, Lu; Xiao, Zuoping; Fung, Hung‐Gay

doi:10.1111/acfi.13001

Cited by 3 publications

(2 citation statements)

References 81 publications

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“…We follow the “Guidelines for the Industry Classification of Listed Companies” issued by CSRC in 2012 to classify industries. Specifically, we use the two‐digital industry code to define the manufacturing industry and one‐digital industry code for other industries (Chen et al, 2021; Jin et al, 2022; Wei, 2021). We provide detailed variable definitions in Appendix Table A2.…”

Section: Sample and Variablesmentioning

confidence: 99%

Institutional investors' corporate site visits and firm management earnings forecasts

Gao

Wang

Zhang

2023

Accounting & Finance

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Institutional investors' site visits may reduce investors' demand for information, resulting in managers' reluctance to disclose earnings forecasts and exacerbating information asymmetry in the capital market. However, institutional investors may also monitor management through site visits, which increases voluntary management earnings forecasts disclosure and thus reduces information asymmetry. Using a sample of Chinese listed firms from 2012 to 2018, we find that firm management is more likely to voluntarily disclose earnings forecasts after the institutional investors' site visits. Specifically, increasing from the 25th to the 75th percentile of institutional site visits is associated with an increase in the odds of voluntary management earnings forecasts by 24%. We use instrumental variables and Heckman two‐step method to address the endogeneity problems and report similar findings. Additional tests show that the governance effect is amplified among firms with severe type I and type II agency problems or firms with no need for refinancing and alleviating stock price synchronicity. Furthermore, we find that voluntary earnings forecasts are more accurate and more precise after site visits, which means institutional investors' site visits in fact improve the quality of management earnings forecasts. Our findings complement the institutional investors' governance effects on earnings forecasts from the perspective of information acquisition.

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Section: Sample and Variablesmentioning

confidence: 99%

Institutional investors' corporate site visits and firm management earnings forecasts

Gao

Wang

Zhang

2023

Accounting & Finance

View full text Add to dashboard Cite

show abstract

“…Several studies predict the movement of a company's stock price [19], predictions are made using SVM and generate RMSE of 20,281, as well as research [20] which examines stock prices of shipping companies using linear regression and produces RMSE of 7,522, as well as research [1], [21], [22], [23] which predicts the sentiment of analysts on stock prices that use SVM which produces low data.…”

Section: Introductionmentioning

confidence: 99%

Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies

Gunawan,

Andriani,

Anandianskha

et al. 2024

SJI

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Purpose: Share price movements after the COVID-19 pandemic experienced a decline in several sectors, especially in the share prices of the Aneka Tambang Company, which operates in the mining sector, the Wijaya Karya Company in the construction sector, and the Sinar Mas Company, which is a Holding Company. Several factors influence this, including investors' hesitation in investing their money. This research aims to predict stock price movements using a Deep Learning algorithm, which is optimized using Selection optimization at three large companies in Indonesia, namely PT. ANTAM, PT. WIKA, and PT. SINAR MAS. So that it can provide the correct information to investors to avoid losses.Method: research through collecting data from the three companies, preprocessing, and then analyzing research data with several alternatives. The combination of inputs from the three companies using the deep learning method is then optimized using selection optimization to produce the best accuracy and use the results of the RMSE evaluation.Results: The results of this research show that by using the Deep Learning method, the best evaluation results were obtained for the Company PT Wijaya Karya with an RMSE value of 0.432, an MAE value of 0.31505 and an MSE value of 1913.953. These results were then optimized using Selection optimization to obtain an RMSE increase of 0.022, namely 0.410.Novelty: The contribution of this research is to get the best combination of input variables obtained using the windowing process from the three companies, which are then processed using the Deep Learning method to produce the most accurate evaluation results from the three companies, then the results are optimized again using Selection optimization to get the more optimal results.

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Analysis of key influencing factors on the financial performance of Jakarta Islamic Index 30 industrial firms

Kismawadi

2024

JIABR

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Purpose The purpose of this paper is to investigate the impact of size, asset quality, asset management, financial risk, gross domestic product and inflation rate on the financial performance of companies listed on the Jakarta Islamic Index of 30 industrial firms. Design/methodology/approach Based on the selected criteria, this study analysed an unbalanced panel of data from 30 industrial companies on the Indonesian capital market that are members of the Jakarta Islamic index. Profitability is measured using the dependent variables return on assets (ROA), return on equity (ROE) and stock prices. The influence of explanatory variables of internal factors, namely, size, asset quality, asset management, financial risk, gross domestic product and inflation is investigated using pooled OLS, fixed and random effect estimation. Findings The empirical findings indicate that the scale of a company has a significant impact on its performance, asset quality, asset management and financial risk. GDP has a substantial impact on financial performance, particularly as measured by ROA and ROE. This study’s ramifications have substantial effects on a broad spectrum of stakeholders. The results of this study provide the general public and investors with a greater understanding of the factors that influence a company’s performance on the Jakarta Islamic Index 30. Research limitations/implications The implication of this research is that a deeper comprehension of the factors that influence the financial performance of companies within industrial sectors that follow Islamic finance principles can help design more effective strategies and policies. Practical implications This research has significant practical implications in a number of crucial areas. First, it provides a comprehensive comprehension of the company’s financial performance in the industrial sector in accordance with Islamic finance principles. Second, the research findings provide more precise guidance on how company size, asset quality and macroeconomic variables influence the performance of Indonesia's financial market. Originality/value The study’s authenticity and value hold considerable importance. This study introduces novel perspectives on the assessment of corporate financial performance within industrial sectors through the lens of Islamic finance principles. It offers valuable insights that have not yet been extensively investigated by scholars in the field.

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What firm risk factors drive bank loan pricing and other terms? Evidence from China

Cited by 3 publications

References 81 publications

Institutional investors' corporate site visits and firm management earnings forecasts

Institutional investors' corporate site visits and firm management earnings forecasts

Optimization Selection on Deep Learning Algorithm for Stock Price Prediction in Indonesia Companies

Analysis of key influencing factors on the financial performance of Jakarta Islamic Index 30 industrial firms

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