Modeling the number of hidden events subject to observation delay

This paper considers the problem of predicting the number of events that have occurred in the past, but which are not yet observed due to a delay. Such delayed events are relevant in predicting the future cost of warranties, pricing maintenance contracts, determining the number of unreported claims in insurance and in modeling the outbreak of diseases. Disregarding these unobserved events results in a systematic underestimation of the event occurrence process. Our approach puts emphasis on modeling the time between the occurrence and observation of the event, the so-called observation delay. We propose a granular model for the heterogeneity in this observation delay based on the occurrence day of the event and on calendar day effects in the observation process, such as weekday and holiday effects. We illustrate this approach on a European general liability insurance data set where the occurrence of an accident is reported to the insurer with delay

Warranty Data Analysis: A Review

Warranty claims and supplementary data contain useful information about product quality and reliability. Analysing such data can therefore be of benefit to manufacturers in identifying early warnings of abnormalities in their products, providing useful information about failure modes to aid design modification, estimating product reliability for deciding on warranty policy and forecasting future warranty claims needed for preparing fiscal plans. In the last two decades, considerable research has been conducted in warranty data analysis (WDA) from several different perspectives. This article attempts to summarise and review the research and developments in WDA with emphasis on models, methods and applications. It concludes with a brief discussion on current practices and possible future trends in WDA

Weighing the Public Interest

In 1981, the AICPA addressed the issue of going concern status through SAS 34, The Auditor\u27s Considerations When a Question Arises About an Entity\u27s Continued Existence. In 1988, the AICPA issued SAS 59, The Auditor\u27s Consideration of an Entity\u27s Ability to Continue as a Going Concern, which remains the authoritative guidance. To determine if additional guidance on the topic of going concern is provided by accounting organizations, the authors contacted the AICPA and the state CPA societies. The authors found that none of these organizations provide additional literature or guidance in this area. Several individuals have criticized the current literature and called for additional guidance in the area of going concern. The authors believe that the Commission on Auditors\u27 Responsibilities\u27 recommendations for improving and specifying the responsibilities of independent auditors should be revisited and the going concern opinion should be eliminated. Considering that litigation is not a serious threat, one can see that eliminating the going concern opinion is the favorable option

Memory-full context-aware predictive mobility management in dual connectivity 5G networks

Network densification with small cell deployment is being considered as one of the dominant themes in the fifth generation (5G) cellular system. Despite the capacity gains, such deployment scenarios raise several challenges from mobility management perspective. The small cell size, which implies a small cell residence time, will increase the handover (HO) rate dramatically. Consequently, the HO latency will become a critical consideration in the 5G era. The latter requires an intelligent, fast and light-weight HO procedure with minimal signalling overhead. In this direction, we propose a memory-full context-aware HO scheme with mobility prediction to achieve the aforementioned objectives. We consider a dual connectivity radio access network architecture with logical separation between control and data planes because it offers relaxed constraints in implementing the predictive approaches. The proposed scheme predicts future HO events along with the expected HO time by combining radio frequency performance to physical proximity along with the user context in terms of speed, direction and HO history. To minimise the processing and the storage requirements whilst improving the prediction performance, a user-specific prediction triggering threshold is proposed. The prediction outcome is utilised to perform advance HO signalling whilst suspending the periodic transmission of measurement reports. Analytical and simulation results show that the proposed scheme provides promising gains over the conventional approach

Hybrid model using logit and nonparametric methods for predicting micro-entity failure

Following the calls from literature on bankruptcy, a parsimonious hybrid bankruptcy model is developed in this paper by combining parametric and non-parametric approaches.To this end, the variables with the highest predictive power to detect bankruptcy are selected using logistic regression (LR). Subsequently, alternative non-parametric methods (Multilayer Perceptron, Rough Set, and Classification-Regression Trees) are applied, in turn, to firms classified as either “bankrupt” or “not bankrupt”. Our findings show that hybrid models, particularly those combining LR and Multilayer Perceptron, offer better accuracy performance and interpretability and converge faster than each method implemented in isolation. Moreover, the authors demonstrate that the introduction of non-financial and macroeconomic variables complement financial ratios for bankruptcy prediction

Earnings quality in ex-post failed firms.

This paper analyses earnings quality in ex-post failed firms. Using a large sample of UK bankrupt firms, we find that failed firms manage earnings upwards in the four years prior to failure. This manipulation is achieved in two ways: (1) through accounting (accruals) manipulation; and (2) by implementing real operating actions that deviate from normal practice. We show that these two types of manipulation lead to reduced earnings reliability. We use conditional conservatism as a proxy for reliability, as prior literature links conditional accounting conservatism to better governance and positive economic outcomes. Our results show that conditional conservatism decreases substantially in the years prior to failure. Finally, we show that accruals manipulation is more pronounced in ex-post bankrupt firms with low ex-ante probability of failure, and that ex-post bankrupt firms with high ex-ante failure probability, having likely exhausted the opportunities for accrual manipulation, manipulate real operations more aggressivelyFirm failure; Accruals management; Real earnings management; Conditional conservatism; Earnings quality; Bankruptcy;

Early Settlement and Errors in Merger Control

We develop a model of remedy offers made to an expert agency which has powers to act before any harm is experienced and is required to decide on the basis of tangible evidence. The model provides a relationship between the factors determining the probability of delay and the type of error in early settlements (i.e. insufficient versus excessive remedy). We apply the model using data from European Commission merger settlements. Our econometric analysis confirms the importance of delay costs and the uncertainty associated with the agency’s findings. Our results are also consistent with the prediction that delay is not systematically related to the inherent competitive harm of the merger proposal. We use our results to identify specific cases of insufficient remedy in early settlements