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The purpose of this paper is to investigate the determinants of the timing of bank failure in North Cyprus over the period of 1984‐2002 using a discrete‐time logistic survival analysis.

The empirical methodology employed in the paper allows for the determination of the factors that influence the time to bank failure. The model links the time of bank failure to a set of bank‐specific factors and macro‐environment that may have exacerbated the internal troubles of the financial institutions.

An empirical examination of the results on survival analysis reveal that the three variables, namely: low asset quality (total loan as a percentage of total assets), low liquidity (total liquid asset as a percentage of total assets), and high credit extended to the private sector (ratio of the private credit to gross domestic product) are the main factors that explain the survival time of banks in North Cyprus.

For further research this paper may better distinguish time to bank failure if it extends the time period and if it uses exchange pressure from Turkey that may have a direct effect on bank failure in North Cyprus.

Nowadays bank failure is an important problem in the world. Using time technique to investigate bank failure will help to learn the factors that determine time to bank failure, which will further help to take precautions and prevent the cost of bank failure.

The analysis would appear to be the first to provide evidence and investigate the time to bank failure in the North Cyprus banking sector.

The purpose of this paper is to examine the survivability of 810 reverse splits during the 1995-2006 period and show that companies that undertake reverse stock splits often fail within a relatively short time following the split.

Applying both a logit model and an adapted version of the Hensler et al. (1997) accelerated failure time model to 810 reverse splits during the 1995-2006 period, the authors are the first to study the survivability of reverse split companies.

The paper finds that the market reaction to the reverse split on the ex-date is an important predictor of the likelihood of survival and of survival time. The paper finds that the likelihood of survival also depends on firm size, pre-split firm returns, and the post-split share price level. The paper finds that post-split survival time also depends on firm size, pre-split operating performance as measured by return on assets, pre-split firm returns, leverage, and the post-split share price level.

The study may be of interest to investors considering investing in stocks that have undergone reverse splits.

The research sheds light on which reverse splitting firms are most likely to survive and for how long.

This study examines the issue of whether audit quality contracted by issuers at the time of going public is associated with post‐IPO survival. Survival analysis methodology is applied to estimate the probability of post‐IPO time to failure as a function of audit quality. Through estimation of the Cox‐Proportional Hazards models, we find that audit quality is significantly related to post‐IPO time to failure both in isolation and in the presence of other covariates that influence firm survival. Further, the association between audit quality and post‐IPO survival is stronger when investment bank prestige is low.

The purpose of this paper is to examine how long a real estate investment trust (REIT) initial public offer (IPO) survives until a merger occurs, and to determine the impact of different firm characteristics that exist at the time of the IPO on that survival in the aftermarket period.

The authors apply an accelerated failure time (AFT) duration model to determine how long the IPO will survive until merger occurs.

The results indicate that the time from the IPO to an eventual merger increases with size, the age of the REIT at IPO, and the percentage of institutional ownership. In contrast, the authors find that the time until merger decreases with increased market performance prior to the time of the offering and with the number of additional IPOs occurring at the time of the IPO.

There is a growing body of research that suggests that IPOs might be motivated by subsequent mergers. An understanding of those characteristics that effect the time until a merger occurs these relationships will enable market participants and capital providers to make better decisions about proceeding with, or evaluating, a REIT IPO.

There is a significant body of research on IPOs in general; however, the findings of this research vary depending upon the industry being examined. Further, there are a limited number of papers on IPO aftermarket survival. This is the only paper on REIT IPO aftermarket survival.

The purpose of this paper is to apply survival analysis, using Cox proportional hazards regression (CPHR), to the problem of predicting if and when supply chain (SC) customers or suppliers might file a petition for bankruptcy so that proactive steps may be taken to avoid a SC disruption.

CPHR is first compared to multiple discriminant analysis (MDA) and logistic regression (LR) to assess its suitability and accuracy to SC applications using three years of financial quarterly data for 69 non-bankrupt and 74 bankrupt organizations. A k-means clustering approach is then applied to the survival curves of all 143 organizations to explore heuristics for predicting the timing of bankruptcy petitions.

CPHR makes bankruptcy predictions at least as accurately as MDA and LR. The survival function also provides valuable information on when bankruptcy might occur. This information allows SC members to be prioritized into three groups: financially healthy companies of no immediate risk, companies with imminent risk of bankruptcy and companies with intermediate levels of risk that need monitoring.

The current paper proposes a new analytical approach to scanning and assessing the financial risk of SC members (suppliers or customers). Traditional models are able to predict if but not when a financial failure will occur. Lacking this information, it is impossible for SC managers to prioritize risk mitigation activities. A simple decision rule is developed to guide SC managers in setting these priorities.

This article will select 25 years of subdivided data to perform Kaplan–Meier survival analysis on the export trade relations of Chinese wooden flooring, use discrete-time cloglog models to analyze influencing factors, use logit and probit models to test the robustness, and try to systematically reveal the duration of China's wood flooring export trade and its influencing factors.

This study used Kaplan–Meier survival function estimation method. In the survival analysis, survival function and hazard rate function are often used to characterize the distribution of survival time.

The continuous average export time of China's wooden flooring is relatively long, about 14 years. China's wooden flooring has a negative time dependency. After the export trade exceeds the threshold value of 15 years, the failure rate of trade greatly decreases, which has a “threshold effect.” Gravity model variables have a significant impact on the duration of China's wooden floor export.

Studying the duration of forest products trade is of great significance for clearing deep-level trade relations and promoting sustainable development of forest products trade.

To review and address current approaches and limitations to modeling change over time in social entrepreneurship research.

The article provides a narrative review of different practices used to assess change over time. It also shows how different research questions require different methodologies for assessing changes over time. Finally, it presents worked examples for modeling these changes.

Our review suggests that there is a lack of research in social entrepreneurship that takes into account the many different considerations for addressing how time influences outcomes.

This chapter introduces an analytic technique to social entrepreneurship that effectively models changes in predictors and outcomes even when data are non-normal or nested across time or levels of analysis.

The purpose of this paper is to present competing risks models and show how dwell times can be applied to predict users’ online behavior. This information enables real-time personalization of web content.

This paper models transitions between pages based upon the dwell time of the initial state and then analyzes data from a web shop, illustrating how pages that are linked “compete” against each other. Relative risks for web page transitions are estimated based on the dwell time within a clickstream and survival analysis is used to predict clickstreams.

Using survival analysis and user dwell times allows for a detailed examination of transition behavior over time for different subgroups of internet users. Differences between buyers and non-buyers are shown.

As opposed to other academic fields, survival analysis has only infrequently been used in internet-related research. This paper illustrates how a novel application of this method yields interesting insights into internet users’ online behavior.

A key goal of any online retailer is to increase their customer conversation rates. Using survival analysis, this paper shows how dwell-time information, which can be easily extracted from any server log file, can be used to predict user behavior in real time. Companies can apply this information to design websites that dynamically adjust to assumed user behavior.

The method shows novel clickstream analysis not previously demonstrated. Importantly, this can support the move from web analytics and “big data” from hype to reality.

The purpose of this paper is to investigate the effect of the control-ownership wedge of controlling shareholders (excess control) on the survival of French initial public offerings (IPOs).

This paper studies a large sample of 434 French IPOs. The empirical analysis uses the Cox proportional hazard and accelerated-failure-time models. Data are manually gathered from IPO prospectuses.

The findings support a positive relation between the control-ownership wedge and IPO survival time, indicating that survival is more likely in firms with high excess control levels. This result is consistent with the view that controlling shareholders with a large control-ownership wedge have incentives to preserve their private benefits of control by increasing firm survival chances. The findings also show that older IPOs are more likely to survive, while riskier and underpriced IPOs are more likely to delist.

The results provide a better understanding of the role of excess control in IPO survival. They also enrich the debate on the efficiency of the one-share-one-vote rule.

The research provides new insights into the role of agency conflicts in IPO survivability. In particular, it explores the effect of dominant shareholders with a control-ownership wedge on survival time.

Survival analysis is statistical technique that uses longitudinal data to model the process that allows an individual or firm to survive to a particular point in time. Despite a large number of studies that use survival analysis to model the duration of time that precedes financial distress, some criticism has suggested that the application of survival analysis to financial distress research provides limited incremental knowledge. This study uses survival analysis to model the duration of time that precedes a firm's initial payment default. The data set consists of firm financial information obtained from a large credit information company in Finland for a five‐year period split into estimation and holdout samples. Financial ratios, size, industry, and age are used as covariates to model the survival process preceding the initial payment default. The hazard is compared to a logistic risk measure estimated from data one year prior to default. The proportional hazards model is shown to give a more accurate forecast of default for the earlier years prior to the onset of financial distress.