Developing a hierarchical system for energy corporate risk factors based on textual risk disclosures

Wei, Lu; Li, Guowen; Zhu, Xiaoqian; Sun, Xiaolei; Li, Jianping

doi:10.1016/j.eneco.2019.01.020

Cited by 77 publications

(30 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Firms' level of disclosure can depend on a transparency motivation such that more detailed disclosure will lower information asymmetry, or on an opaqueness motivation that will ensure only generic risks are disclosed to avoid uncertainty among shareholders about the risk level (Ott 2020). The economic theory's prediction that firm risks are affected by various risk factors also explains the motivation behind RFDs (Wei, Li, Zhu, Sun, and Li 2019). Studies on asset prices cite the asset theory prediction that bonds and stock prices are affected by sensitivity to changes in market conditions (Beatty et al 2019) and the arbitrage pricing theory prediction that firms' cash flows are subject to exogenous factors (Heinle et al 2018).…”

Section: Theoretical Framework Of Rfd Researchmentioning

confidence: 99%

“…Focusing on US lodging companies, Penela and Serrasqueiro (2019) find that while the focus of risk disclosure was on economic and financial conditions in 2008, the themes in 2016 focused on risks that could affect company results. Other industry level studies have identified 66 types of risk factors in the energy industry (Wei, Li, Zhu, Sun, and Li 2019) and 21 risk factors in the banking industry, with four of the risk factors unique to that industry ). 6 Also within the banking industry, but specific to the financial crisis period, Shabestari et al (2020) identify 20 financial crisis dictionary (FCD) words for commercial banks.…”

Section: Rfd Contentmentioning

confidence: 99%

See 1 more Smart Citation

Risk Factor Disclosures: A Review and Directions for Future Research*

Isiaka

2021

Accounting Perspectives

View full text Add to dashboard Cite

This paper assesses the current state of scholarly work on risk factor disclosures (RFDs) with the goal of synthesizing existing literature and stimulating further research in this area. To a large extent, prior research studies have examined different aspects of the disclosure of corporate risk information in annual reports. Beginning in 2005, the US SEC proposed changes to the disclosure of risk information in annual 10-K reports. The changes mandated large firms in the United States to disclose risk factors in Item 1A of their 10-Ks. While research studies on the impact of the changes are still ongoing, there are concerns among stakeholders that RFDs are vague, repetitive, and boilerplate. As a result, the SEC called on firms to ensure that they clearly disclose all the risks they faced. The SEC's call resulted in the release of an amendment that provides directions to further improve firms' RFDs. Using a systematic literature review method, this paper classifies the RFD literature into five research themes: (i) contents, (ii) informativeness, (iii) determinants, (iv) quality, and (v) effects on firm performance. The paper also reviews theories that have been dominantly applied in RFD studies and provides suggestions for future research.

show abstract

Section: Theoretical Framework Of Rfd Researchmentioning

confidence: 99%

Section: Rfd Contentmentioning

confidence: 99%

Risk Factor Disclosures: A Review and Directions for Future Research*

Isiaka

2021

Accounting Perspectives

View full text Add to dashboard Cite

show abstract

“…In the last few decades, energy demand forecasting has turned into an active research area due to its significant impact on the energy security and socioeconomic development of a country [13,14]. A series of prominent research studies have reported on energy demand forecasting, including factor selection [7,15,16] and method determination [13,17].…”

Section: Energy Demand Influencing Factorsmentioning

confidence: 99%

A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm

2020

Self Cite

View full text Add to dashboard Cite

Accurate forecasting of the energy demand is crucial for the rational formulation of energy policies for energy management. In this paper, a novel ensemble forecasting model based on the artificial bee colony (ABC) algorithm for the energy demand was proposed and adopted. The ensemble model forecasts were based on multiple time variables, such as the gross domestic product (GDP), industrial structure, energy structure, technological innovation, urbanization rate, population, consumer price index, and past energy demand. The model was trained and tested using the primary energy demand data collected in China. Seven base models, including the regression-based model and machine learning models, were utilized and compared to verify the superior performance of the ensemble forecasting model proposed herein. The results revealed that (1) the proposed ensemble model is significantly superior to the benchmark prediction models and the simple average ensemble prediction model just in terms of the forecasting accuracy and hypothesis test, (2) the proposed ensemble approach with the ABC algorithm can be employed as a promising framework for energy demand forecasting in terms of the forecasting accuracy and hypothesis test, and (3) the forecasting results obtained for the future energy demand by the ensemble model revealed that the future energy demand of China will maintain a steady growth trend.

show abstract

“…Recently, managers and researchers have found that the qualitative textual risk disclosures reported in financial statements are important sources of corporate risk factors (Bao and Datta, ; Campbell et al ., ; Wei et al ., ). Beginning in 2005, the US Securities and Exchange Commission (SEC) required public companies to add a separate section 1A in their SEC Form 10‐K to discuss ‘the most significant factors that make the offering speculative or risky’ (SEC, ).…”

Section: Introductionmentioning

confidence: 97%

Discovering bank risk factors from financial statements based on a new semi‐supervised text mining algorithm

Wei

Zhu

et al. 2019

Accounting & Finance

Self Cite

View full text Add to dashboard Cite

This paper aims to comprehensively uncover bank risk factors from qualitative textual risk disclosures reported in financial statements, which contain a huge amount of information on bank risks. We propose a new semi-supervised text mining approach named naive collision algorithm to analyse the textual risk disclosures, which can more accurately identify bank risk factors compared with the typical unsupervised text mining approach. We identified 21 bank risk factors in total, which is far more than identified in previous studies. We further analyse the importance of each bank risk factor and how the importance of each risk factor changes over time.

show abstract

Developing a hierarchical system for energy corporate risk factors based on textual risk disclosures

Cited by 77 publications

References 22 publications

Risk Factor Disclosures: A Review and Directions for Future Research*

Risk Factor Disclosures: A Review and Directions for Future Research*

A Novel Ensemble Approach for the Forecasting of Energy Demand Based on the Artificial Bee Colony Algorithm

Discovering bank risk factors from financial statements based on a new semi‐supervised text mining algorithm

Contact Info

Product

Resources

About