Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supp...Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.展开更多
We investigate how the accounting treatment of intangible assets on managers’likelihood of issuing voluntary earnings guidance(MEF).We find that unrecognized intangibles(immediately expensed)are negatively associated...We investigate how the accounting treatment of intangible assets on managers’likelihood of issuing voluntary earnings guidance(MEF).We find that unrecognized intangibles(immediately expensed)are negatively associated with MEF issuance,while recognized intangibles(capitalized)show a positive association.These findings hold across various factors such as analysts’coverage,industry type and for a subsample that excludes software firms permitted to capitalize software development costs under SFAS No.86.In additional,we investigate the cross-sectional determinants of MEF issuance based on the characteristics of firm intangibility.We find a significant increase in the likelihood of MEF issuance for higher unrecognized intangibles with greater earnings uncertainty.This suggests that managers may prioritize delivering valuerelevant information to market participants to alleviate uncertainty.展开更多
In this study,the impact of business and financial information integration(BFⅡ)on the voluntary management earnings forecasts(VMEFs)of listed firms in China between 2008 and 2018 is investigated.Drawing on litigation...In this study,the impact of business and financial information integration(BFⅡ)on the voluntary management earnings forecasts(VMEFs)of listed firms in China between 2008 and 2018 is investigated.Drawing on litigation cost and ability signaling theories,we find that the adoption of BFⅡencourages top managers to disclose VMEFs.BFⅡfirms are identified through the textual analysis of management discussion and analysis(MD&A)reports,and the empirical results indicate that BFⅡfirms have a higher probability and frequency of issuing VMEFs than non-BFⅡfirms.The results remain robust after we identify causality by applying a propensity score matching and difference-in-differences(PSM-DID)test and use an alternate measure of BFⅡ.Further tests show that BFⅡfirms issue more accurate VMEFs and are able to issue them at an earlier stage.We also find that the positive relationship between BFⅡand VMEFs is weakened if the media expresses concern about the uncertainty of BFⅡadoption.展开更多
Due to resource constraints,securities regulators cannot find or punish all firms that have conducted irregular or even illegal activities(hereafter referred to as fraud).Those who study securities regulations can onl...Due to resource constraints,securities regulators cannot find or punish all firms that have conducted irregular or even illegal activities(hereafter referred to as fraud).Those who study securities regulations can only find the instances of fraud that have been punished,not those that have not been punished,and it is these unknown cases that would make the best control sample for studies of enforcement action criteria.China's mandatory management earnings forecasts solve this sampling problem.In the A-share market,firms that have not forecasted as mandated are likely in a position to be punished by securities regulators or are attempting to escape punishment,and their identification allows researchers to build suitable study and control samples when examining securities regulations.Our results indicate that enforcement actions taken by securities regulators are selective.The probability that a firm will be punished for irregular management forecasting is significantly related to proxies for survival rates.Specifically,fraudulent firms with lower return on assets(ROAs) or higher cash flow risk are more likely to be punished.Further analysis shows that selective enforcement of regulations has had little positive effect on the quality of listed firms' management forecasts.展开更多
We examine whether management earnings forecasts(MEFs)help reduce the stock return seasonality associated with earnings seasonality around earnings announcements(EAs)in Chinese A-share markets.We find that firms in hi...We examine whether management earnings forecasts(MEFs)help reduce the stock return seasonality associated with earnings seasonality around earnings announcements(EAs)in Chinese A-share markets.We find that firms in historically low earnings seasons outperform firms in high earnings seasons by 2.1%around MEFs.Firms in low earnings seasons also have higher trading volume and return volatility than their counterparts around EAs and MEFs.MEFs significantly reduce the ability of historical seasonal earnings rankings to negatively predict announcement returns,volume and volatility around EAs.The reduction effects are stronger when MEFs are voluntary or made closer to EAs.The evidence suggests that MEFs facilitate the correction of investors’tendency to extrapolate earnings seasonality and its resulted stock mispricing.展开更多
This paper examines whether customer base composition in the US,that is,whether a firm's major customers are government entities or publicly traded companies,affects the properties of its management earnings forec...This paper examines whether customer base composition in the US,that is,whether a firm's major customers are government entities or publicly traded companies,affects the properties of its management earnings forecasts(MEFs).Using a sample of 1,168 MEFs from 1998 to 2014,we find that firms whose major customers are government entities(i.e.,govemment suppliers)issue more precise and more accurate MEFs than firms whose major customers are public companies(i.e.,corporate suppliers).Moreover,when managers disclose negative information to the market,earnings forecasts issued by government suppliers have greater price impact than those issued by corporate suppliers.Collectively,our empirical results suggest that having major govemment customers has a positive impact on the quality of MEFs.展开更多
Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and beh...Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.展开更多
Customers are one of the key external stakeholders for a company.Using Chinese listed companies’data from 2007 to 2015,this paper examines the impact of customer concentration on information disclosure from the pers...Customers are one of the key external stakeholders for a company.Using Chinese listed companies’data from 2007 to 2015,this paper examines the impact of customer concentration on information disclosure from the perspective of management earnings forecast.Empirical results show that a more concentrated customer base induces companies to disclose more positive earnings forecast.In addition,the positive association between customer concentration and management earnings forecast is more pronounced with higher economic policy uncertainty.Further analyses reveal that companies issue more positive earnings forecast to reduce the financing risk and protect their relationshipspecific investment.Also,customer concentration reduces the consistency of earnings forecast since the deviation of the forecast performance and the actual performance is greater.This paper enriches the literature on the determinants of management earnings forecast and the effects of stakeholders on corporate financial behaviour.Our findings also provide implications for investors,regulators,and various stakeholders to understand management earnings forecast decisions.展开更多
This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management ...This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.展开更多
文摘Forecasting is predicting or estimating a future event or trend.Supply chains have been constantly growing in most countries ever since the industrial revolution of the 18th century.As the competitiveness between supply chains intensifies day by day,companies are shifting their focus to predictive analytics techniques to minimize costs and boost productivity and profits.Excessive inventory(overstock)and stock outs are very significant issues for suppliers.Excessive inventory levels can lead to loss of revenue because the company's capital is tied up in excess inventory.Excess inventory can also lead to increased storage,insurance costs and labor as well as lower and degraded quality based on the nature of the product.Shortages or out of stock can lead to lost sales and a decline in customer contentment and loyalty to the store.If clients are unable to find the right products on the shelves,they may switch to another vendor or purchase alternative items.Demand forecasting is valuable for planning,scheduling and improving the coordination of all supply chain activities.This paper discusses the use of neural networks for seasonal time series forecasting.Our objective is to evaluate the contribution of the correct choice of the transfer function by proposing a new form of the transfer function to improve the quality of the forecast.
文摘We investigate how the accounting treatment of intangible assets on managers’likelihood of issuing voluntary earnings guidance(MEF).We find that unrecognized intangibles(immediately expensed)are negatively associated with MEF issuance,while recognized intangibles(capitalized)show a positive association.These findings hold across various factors such as analysts’coverage,industry type and for a subsample that excludes software firms permitted to capitalize software development costs under SFAS No.86.In additional,we investigate the cross-sectional determinants of MEF issuance based on the characteristics of firm intangibility.We find a significant increase in the likelihood of MEF issuance for higher unrecognized intangibles with greater earnings uncertainty.This suggests that managers may prioritize delivering valuerelevant information to market participants to alleviate uncertainty.
基金financial support from the National Natural Science Foundation of China(Grant No.71902210)the Youth Research Fund of the Ministry of Education for Humanities and Social Sciences(Grant No.19YJC630092)+2 种基金the Program for Innovation Research in Central University of Finance and Economics(Grant No.CUFE 20190111)Social Science Foundation of Guangdong Province of China(Grant No.GD19CGL05)Graduate Research and Innovation Fund Project of Central University of Finance and Economics(Grant No.20182Y006)
文摘In this study,the impact of business and financial information integration(BFⅡ)on the voluntary management earnings forecasts(VMEFs)of listed firms in China between 2008 and 2018 is investigated.Drawing on litigation cost and ability signaling theories,we find that the adoption of BFⅡencourages top managers to disclose VMEFs.BFⅡfirms are identified through the textual analysis of management discussion and analysis(MD&A)reports,and the empirical results indicate that BFⅡfirms have a higher probability and frequency of issuing VMEFs than non-BFⅡfirms.The results remain robust after we identify causality by applying a propensity score matching and difference-in-differences(PSM-DID)test and use an alternate measure of BFⅡ.Further tests show that BFⅡfirms issue more accurate VMEFs and are able to issue them at an earlier stage.We also find that the positive relationship between BFⅡand VMEFs is weakened if the media expresses concern about the uncertainty of BFⅡadoption.
基金the National Natural Science Foundation of China(ProjectNumber:71102084)for their financial support of this work
文摘Due to resource constraints,securities regulators cannot find or punish all firms that have conducted irregular or even illegal activities(hereafter referred to as fraud).Those who study securities regulations can only find the instances of fraud that have been punished,not those that have not been punished,and it is these unknown cases that would make the best control sample for studies of enforcement action criteria.China's mandatory management earnings forecasts solve this sampling problem.In the A-share market,firms that have not forecasted as mandated are likely in a position to be punished by securities regulators or are attempting to escape punishment,and their identification allows researchers to build suitable study and control samples when examining securities regulations.Our results indicate that enforcement actions taken by securities regulators are selective.The probability that a firm will be punished for irregular management forecasting is significantly related to proxies for survival rates.Specifically,fraudulent firms with lower return on assets(ROAs) or higher cash flow risk are more likely to be punished.Further analysis shows that selective enforcement of regulations has had little positive effect on the quality of listed firms' management forecasts.
基金the financial support of the National Natural Science Foundation of China(NSFC)(Grant#91746109,#71773100 and#72073109)
文摘We examine whether management earnings forecasts(MEFs)help reduce the stock return seasonality associated with earnings seasonality around earnings announcements(EAs)in Chinese A-share markets.We find that firms in historically low earnings seasons outperform firms in high earnings seasons by 2.1%around MEFs.Firms in low earnings seasons also have higher trading volume and return volatility than their counterparts around EAs and MEFs.MEFs significantly reduce the ability of historical seasonal earnings rankings to negatively predict announcement returns,volume and volatility around EAs.The reduction effects are stronger when MEFs are voluntary or made closer to EAs.The evidence suggests that MEFs facilitate the correction of investors’tendency to extrapolate earnings seasonality and its resulted stock mispricing.
基金the financial support for the research project No.15507217 by the General Research Fund from the Research Grants Council of the Hong Kong Special Administrative Region Government.
文摘This paper examines whether customer base composition in the US,that is,whether a firm's major customers are government entities or publicly traded companies,affects the properties of its management earnings forecasts(MEFs).Using a sample of 1,168 MEFs from 1998 to 2014,we find that firms whose major customers are government entities(i.e.,govemment suppliers)issue more precise and more accurate MEFs than firms whose major customers are public companies(i.e.,corporate suppliers).Moreover,when managers disclose negative information to the market,earnings forecasts issued by government suppliers have greater price impact than those issued by corporate suppliers.Collectively,our empirical results suggest that having major govemment customers has a positive impact on the quality of MEFs.
文摘Demand forecasting and big data analytics in supply chain management are gaining interest.This is attributed to the wide range of big data analytics in supply chain management,in addition to demand forecasting,and behavioral analysis.In this article,we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications,identify gaps,and provide ideas for future research.Algorithms will then be classified and then applied in supply chain management such as neural networks,k-nearest neighbors,time series forecasting,clustering,regression analysis,support vector regression and support vector machines.An extensive hierarchical model for short-term auto parts demand assess-ment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series.The concept of extensive relevance assessment was proposed,and subsequently methods to reflect the relevance of automotive demand factors were discussed.Using a wide range of skills,the factors and co-factors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components.Then,it is compared with the existing data and predicted the short-term historical data.The result proved the predictive error is less than 6%,which supports the validity of the prediction method.This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.
基金This research is supported by the Fundamental Research Funds for the Central U niversities and the Research Funds of Renmin U niversity of China(China)[ Grant no.19XNL014].
文摘Customers are one of the key external stakeholders for a company.Using Chinese listed companies’data from 2007 to 2015,this paper examines the impact of customer concentration on information disclosure from the perspective of management earnings forecast.Empirical results show that a more concentrated customer base induces companies to disclose more positive earnings forecast.In addition,the positive association between customer concentration and management earnings forecast is more pronounced with higher economic policy uncertainty.Further analyses reveal that companies issue more positive earnings forecast to reduce the financing risk and protect their relationshipspecific investment.Also,customer concentration reduces the consistency of earnings forecast since the deviation of the forecast performance and the actual performance is greater.This paper enriches the literature on the determinants of management earnings forecast and the effects of stakeholders on corporate financial behaviour.Our findings also provide implications for investors,regulators,and various stakeholders to understand management earnings forecast decisions.
文摘This study provides details of the energy management architecture used in the Goldwind microgrid test bed. A complete mathematical model, including all constraints and objectives, for microgrid operational management is first described using a modified prediction interval scheme. Forecasting results are then achieved every 10 min using the modified fuzzy prediction interval model, which is trained by particle swarm optimization.A scenario set is also generated using an unserved power profile and coverage grades of forecasting to compare the feasibility of the proposed method with that of the deterministic approach. The worst case operating points are achieved by the scenario with the maximum transaction cost. In summary, selection of the maximum transaction operating point from all the scenarios provides a cushion against uncertainties in renewable generation and load demand.
