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The purpose of this paper is to test the performance of momentum strategies and identify the sources of their profits.

To identify the main source of momentum profits, first, the bootstrap method with replacement was used. Then, to eliminate the existence of the small sample bias, the bootstrap method without replacement and the block bootstrap method were employed. In this case, when the authors draw the observations without replacement the random effect is reduced, whereas the resampling procedure is based on the random draw.

The empirical results show the existence of a small sample bias in the bootstrap method with replacement, and that the time‐series relations of stock returns are the main source of momentum profits.

To ensure the random effect of the draws, the authors develop a new resampling procedure called the mixed bootstrap method.

The present paper aims to present the results of a simulation study on the behavior of the four 95 percent bootstrap confidence intervals for estimating C_pk when collected data are from a multiple streams process.

A computer simulation study is developed to present the behavior of four 95 percent bootstrap confidence intervals, i.e. standard bootstrap (SB), percentile bootstrap (PB), biased‐corrected percentile bootstrap (BCPB), and biased‐corrected and accelerated (BCa) bootstrap for estimating the capability index C_pk of a multiple streams process. An analysis of variance using two factorial and three‐stage nested designs is applied for experimental planning and data analysis.

For multiple process streams, the relationship between the true value of C_pk and the required sample size for effective experiment is presented. Based on the simulation study, the two‐stream process always gives a higher coverage percentage of bootstrap confidence interval than the four‐stream process. Meanwhile, BCPB and BCa intervals lead to better coverage percentage than SB and PB intervals.

Since a large number of process streams decreases the coverage percentage of the bootstrap confidence interval, it may be inappropriate to use the bootstrap method for constructing the confidence interval of a process capability index as the number of process streams is large.

The present paper is the first work to explore the behavior of bootstrap confidence intervals for estimating the capability index C_pk of a multiple streams process. It is concluded that the number of process streams definitively affects the performance of bootstrap methods.

In order to develop a feasible and efficient method to acquire the long‐run availability of a parallel system with distribution‐free up and down times, the purpose of this paper is to perform the simulation comparisons on the interval estimations of system availability using four bootstrapping methods.

By using four bootstrap methods; standard bootstrap (SB) confidence interval, percentile bootstrap (PB) confidence interval, bias‐corrected percentile bootstrap (BCPB) confidence interval, and bias‐corrected and accelerated (BCa) confidence interval. A numerical simulation study is carried out in order to demonstrate performance of these proposed bootstrap confidence intervals. Especially, we investigate the accuracy of the four bootstrap confidence intervals by calculating the coverage percentage, the average length, and the relative coverage of confidence intervals.

Among the four bootstrap confidence intervals, the PB method has the largest relative coverage in most situations. That is, the PB method is the best one made by practitioners who want to obtain an efficient interval estimation of availability.

It is the first time that the relative coverage is introduced to evaluate the performance of estimation method, which is more efficient than the existing measures.

This chapter develops a novel bootstrap procedure to obtain robust bias-corrected confidence intervals in regression discontinuity (RD) designs. The procedure uses a wild bootstrap from a second-order local polynomial to estimate the bias of the local linear RD estimator; the bias is then subtracted from the original estimator. The bias-corrected estimator is then bootstrapped itself to generate valid confidence intervals (CIs). The CIs generated by this procedure are valid under conditions similar to Calonico, Cattaneo, and Titiunik’s (2014) analytical correction – that is, when the bias of the naive RD estimator would otherwise prevent valid inference. This chapter also provides simulation evidence that our method is as accurate as the analytical corrections and we demonstrate its use through a reanalysis of Ludwig and Miller’s (2007) Head Start dataset.

Bootstrapping is a practitioner-based term adopted in entrepreneurship to describe the techniques employed in micro, small and medium-sized enterprises (MSMEs) to minimise the need for external funding by securing resources at little or no cost and applying strategies to effectively use resources. Working capital management (WCM) is a term used in financial management to define a set of practices used to manage business resources, including cash management. This paper explores the overlap and divergence between these two disciplinary distinct concepts.

A dual methodology is employed. First, the usage of the two terms in prior literature is analysed and synthesised. Second, the study uses factor analysis to explore how bootstrapping practices described by owners of 167 established MSMEs relate to the components of WCM in financial management.

The factor analysis identifies two main bootstrapping practices employed by MSMEs: (1) delaying payments and owner-related bootstrapping and (2) customer-related bootstrapping. Delaying payments is an integral practice in trade payables management and customer-related bootstrapping includes practices that are integral to trade receivables management. Therefore, links between bootstrapping practices and WCM practices are firmly established.

The study is not without limitations. Based on cross-sectional evidence for established firms in Ireland only, future studies could explore cross-country longitudinal panel data to fully examine life cycle and sectoral effects, as well as other external shocks (for example, COVID-19) on bootstrapping and WCM practices. This study does not explain why some factors (for example, joint utilisation and inventory management) are present in some bootstrapping studies and not in others; further case study research might help explain this. Finally, changes in the business environment facing start-ups and established enterprise, including increased digitalisation, online trading, self-employment, remote hub working and sustainability, offer new avenues for bootstrapping research.

This is the first study to comprehensively explore the conceptual and empirical links between bootstrapping and WCM. This study will enable researchers and practitioners in these two distinct disciplines to learn from each other. Accounting researchers and practitioners can broaden their understanding of how WCM “works” in MSME settings. Similarly, entrepreneurship researchers and practitioners can deepen their understanding of how bootstrapping can be adopted by businesses to manage resources effectively.

The purpose of this paper is to construct a statistical hypotheses test for process capability indices and compare the pairs of them with a fixed sample size.

Since the sampling distribution of the estimators of pairs of two process capability indices (PCIs) is very complex, an exact statistical hypothesis test for them cannot be constructed. Therefore, the authors have proposed a bootstrap method to construct the hypothesis test for them on the basis of p-value.

The authors have shown that by increasing n, the bootstrap method has better output relative to other methods and it can be easily implemented. The authors have also demonstrated that sometimes an exact hypotheses test cannot be constructed and need some assumptions.

In the present paper, several methods to test of hypotheses about the difference between two process capability indices have been compared.

Among nascent entrepreneurial ventures, are some types of bootstrapping techniques more successful than others? We compare externally oriented and internally oriented techniques with respect to the likelihood of becoming an operational venture; and we compare cash-increasing and cost-decreasing techniques with respect to becoming operational. Using data from the first Panel Study of Entrepreneurial Dynamics, we find evidence suggesting that when bootstrapping a new venture, the percentage of cash-increasing and cost-decreasing externally oriented bootstrapping techniques that a ventureʼs owners use are positive predictors of subsequent positive cash flow (one and two years later). But, internally oriented techniques are not related to subsequent cash flow.

This paper aims to examine the effect of financial bootstrapping strategies (FBS) and strategic improvisation (SI) on business performance (BP). The study enriches our understanding of the contributions of bootstrapping and improvisation strategies toward resource-constrained small businesses during real economic downturns and crises. The potential moderating effect of SI on the relationship between FBS and its dimensions and performance were also examined.

Using the convenience snowball sampling technique, data were collected from entrepreneurs in Tripoli, Libya. Structural equation modeling by means of partial least square bootstrapping resampling was used for the hypotheses testing of the 147 useable responses.

Statistically significant positive relationships were found in the direct relationships between bootstrapping and improvisation with performance. However, there was no significant association found between the delaying payment related bootstrapping and the owner-related bootstrapping with performance. The moderating effect of improvisation had a significant relationship between bootstrapping as an aggregate construct and its dimensions and performance.

Due to the cross-sectional nature of this study which used a small sample that was randomly selected, generalization to the entire population of business ventures should be made with caution.

The negative moderation effect of improvisation on FBS-BP association suggests that entrepreneurs need to be careful in balancing the two strategies so that efforts are no wasted.

While business performance has been studied in various organizations, its examination with financial bootstrapping strategies as a predictor and strategic improvisation as a moderator contribute nascent theoretical insights.

The Chinese fund market has witnessed significant developments in recent years. However, although there has been a range of studies assessing fund performance in developed industries, the rapidly developing fund industry in China has received very little attention. This study aims to examine the performance of open-end securities investment funds investing in Chinese domestic equity during the period May 2003 to September 2020. Specifically, applying a non-parametric bootstrap methodology from the literature on fund performance, the authors investigate the role of skill versus luck in this rapidly evolving investment funds industry.

This study evaluates the performance of Chinese equity securities investment funds from 2003–2020 using a bootstrap methodology to distinguish skill from luck in performance. The authors consider unconditional and conditional performance models.

The bootstrap methodology incorporates non-normality in the idiosyncratic risk of fund returns, which is a major drawback in “conventional” performance statistics. The evidence does not support the existence of “genuine” skilled fund managers. In addition, it indicates that poor performance is mainly attributable to bad stock picking skills.

The authors find that the top-ranked funds with positive abnormal performance are attributed to “good luck” not “good skill” while the negative abnormal performance of bottom funds is mainly due to “bad skill.” Therefore, sensible advice for most Chinese equity investors would be against trying to “pick winners funds” among Chinese securities investment funds but it would be recommended to avoid holding “losers.” At the present time, investors should consider other types of funds, such as index/tracker funds with lower transactions. In addition, less risk-averse investors may consider Chinese hedge funds [Zhao (2012)] or exchange-traded fund [Han (2012)].

The paper makes several contributions to the literature. First, the authors examine a wide range (over 50) of risk-adjusted performance models, which account for both unconditional and conditional risk factors. The authors also control for the profitability and investment risks in Fama and French (2015). Second, the authors select the “best-fit” model across all risk-adjusted models examined and a single “best-fit” model from each of the three classes. Therefore, the bootstrap analysis, which is mainly based on the selected best-fit models, is more precise and robust. Third, the authors reduce the possibility that findings may be sample-period specific or may be a survivor (upward) biased. Fourth, the authors consider further analysis based on sub-periods and compare fund performance in different market conditions to provide more implications to investors and practitioners. Fifth, the authors carry out extensive robustness checks and show that the findings are robust in relation to different minimum fund histories and serial correlation and heteroscedasticity adjustments. Sixth, the authors use higher frequency weekly data to improve statistical estimation.

The purpose of this paper is to present bootstrapping as an alternative statistical methodology to analyze time studies and input data for discrete‐event simulations. Bootstrapping is a non‐parametric technique to estimate the sampling distribution of a statistic by doing repeated sampling (i.e. resampling) with replacement from an original sample. This paper proposes a relatively simple implementation of bootstrap techniques to time study analysis.

Using an inductive approach, this work selects a typical situation to conduct a time study, applies two bootstrap procedures for the statistical analysis, compares bootstrap to traditional parametric approaches, and extrapolates general advantages of bootstrapping over parametric approaches.

Bootstrap produces accurate inferences when compared to those from parametric methods, and it is an alternative when the underlying parametric assumptions are not met.

Research results contribute to work measurement and simulation fields since bootstrap promises an increase in accuracy in cases where the normality assumption is violated or only small samples are available. Furthermore, this paper shows that electronic spreadsheets are appropriate tools to implement the proposed bootstrap procedures.

In previous work, the standard procedure to analyze time studies and input data for simulations is a parametric approach. Bootstrap permits to obtain both point estimates and estimates of time distributions. Engineers and managers involved in process improvement initiatives could use bootstrap to exploit better the information from available samples.