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Prior research has documented inconclusive and/or mixed empirical evidence on the timing performance of hybrid funds. Their performance inferences generally do not efficiently control for fixed-income exposure, conditioning information, and cross-correlations in fund returns. This study examines the stock and bond timing performances of hybrid funds while controlling and accounting for these important issues. It also discusses the inferential implications of using alternative bootstrap resampling approaches.

We examine the stock and bond timing performances of hybrid funds using (un)conditional multi-factor benchmark models with robust estimation inferences. We also rely on the block bootstrap method to account for cross-correlations in fund returns and to separate the effects of luck or sampling variation from manager skill.

We find that the timing performance of portfolios of funds is neutral and sensitive to controlling for fixed-income exposures and choice of the timing measurement model. The block-bootstrap analyses of funds in the tails of the distributions of stock timing performances suggest that sampling variation explains the underperformance of extreme left tail funds and confirms the good and bad luck in the bond timing management of tail funds. We report inference changes based on whether the Kosowski et al. or the Fama and French bootstrap approach is used.

This study provides extensive and robust evidence on the stock and bond timing performances of hybrid funds and their sensitivity based on (un)conditional linear multi-factor benchmark models. It examines the timing performances in the extreme tails funds using the block bootstrap method to efficiently identify (un)skilled fund managers. It also highlights the sensitivity of inferences to the choice of testing methodology.

Successive Approximation Register-Analog to Digital Converter (SAR-ADC) has been achieved notable technological advancement since the past couple of decades. However, it’s not accurate in terms of size, energy, and time consumption. Many projects proposed to make it energy efficient and time-efficient. Such designs are unable to deliver two parallel outputs.

To this end, this study introduced an ultra-low-power circuitry for the two blocks (bootstrap and comparator) of 11-bit SAR-ADC. The bootstrap has three sub-parts: back-bone, left-wing and right-wing, named as bat-bootstrap. The comparator block has a circuitry of the two comparators and an amplifier, named as comp-lifier. In a bat-bootstrap, the authors plant two capacitors in the back-bone block to avoid the patristic capacitance. The switching system of the proposed design highly synchronized with the short pulses of the clocks for high accuracy. This study simulates the proposed circuits using a built-in Cadence 90 nm Complementary Metal Oxide Semiconductor library.

The results suggested that the response time of two bat-bootstrap wings and comp-lifier are 80 ns, 120 ns, and 90 ns, respectively. The supply voltage is 0.7 V, wherever the power consumption of bat-bootstrap, comp-lifier and SAR-ADC are 0.3561µW, 0.257µW and 35.76µW, respectively. Signal to Noise and Distortion Ratio is 65 dB with 5 MHz frequency and 25 KS/s sampling rate. The input referred noise of the amplifier and two comparators are 98µVrms, 224µVrms and 224µVrms, respectively.

Two basic circuit blocks for SAR-ADC are introduced, which fulfill the duality approach and delivered two outputs with highly synchronized clock pulses. The circuit sharing concept introduced for the high performance SAR-ADCs.

The purpose of this paper is to look at the power of the Student t-test applied to two independent samples when returns from AR(1) process are categorized into two samples by moving average buy-sell trading rule.

Simulation and empirical study for returns from NASDAQ via bootstrapping resampling method are conducted.

The authors conclude that applying the MA Trading Rule followed by Student t-test is not appropriate for analyzing market efficiency.

Moving average buy-sell trading rule is widely used in finance to test if the market is efficient. In this paper, it is one of the first kind of research to examine the power of the test via simulation and empirical study.

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.

In this chapter we discuss model selection and predictive accuracy tests in the context of parameter and model uncertainty under recursive and rolling estimation schemes. We begin by summarizing some recent theoretical findings, with particular emphasis on the construction of valid bootstrap procedures for calculating the impact of parameter estimation error. We then discuss the Corradi and Swanson (2002) (CS) test of (non)linear out-of-sample Granger causality. Thereafter, we carry out a series of Monte Carlo experiments examining the properties of the CS and a variety of other related predictive accuracy and model selection type tests. Finally, we present the results of an empirical investigation of the marginal predictive content of money for income, in the spirit of Stock and Watson (1989), Swanson (1998) and Amato and Swanson (2001).

Classical unit root tests are known to suffer from potentially crippling size distortions, and a range of procedures have been proposed to attenuate this problem, including the use of bootstrap procedures. It is also known that the estimating equation’s functional form can affect the outcome of the test, and various model selection procedures have been proposed to overcome this limitation. In this chapter, the authors adopt a model averaging procedure to deal with model uncertainty at the testing stage. In addition, the authors leverage an automatic model-free dependent bootstrap procedure where the null is imposed by simple differencing (the block length is automatically determined using recent developments for bootstrapping dependent processes). Monte Carlo simulations indicate that this approach exhibits the lowest size distortions among its peers in settings that confound existing approaches, while it has superior power relative to those peers whose size distortions do not preclude their general use. The proposed approach is fully automatic, and there are no nuisance parameters that have to be set by the user, which ought to appeal to practitioners.

This chapter proposes a test for a parametric specification of the autoregressive function of a given stationary autoregressive time series. This test is based on the integrated square difference between the empirical distribution function estimate and a convolution-type distribution function estimate of the stationary distribution function obtained from the autoregressive residuals. Some asymptotic properties of the proposed convolution-type distribution function estimate are studied when the model’s innovation density is unknown. These properties are in turn used to derive the asymptotic null distribution of the proposed test statistic. We also discuss some finite sample properties of the test statistic based on the block bootstrap methodology. A simulation study shows that the proposed test competes favorably with some existing tests in terms of the empirical level and power.

The purpose of this paper is to examine three different forms of returns based on the price difference, percentage change, and difference in logarithm price from moving average buy-sell trading rule. Statistical linear correlation, the means of returns from buy/sell days, and the flexibility of long-term moving periods are examined.

Traditional linear correlations, pairwise student t-test, and ϕ coefficient for two binary buy/sell decision variables are studied from the simple block bootstrap (convenience) sampling from S&P, Dow Jones, and NASDAQ price indices from January 29, 1985 to January 6, 2016.

The authors find that different forms of returns from MA(1-50) are strongly linearly correlated via 150 simple block bootstrap (convenience) samples from S&P, Dow Jones, and NASDAQ price indices from January 29, 1985 to January 6, 2016. In other words, the price differences, the percentage returns, and logarithmic returns are exchangeable for returns from S&P, Dow Jones, and NASDAQ. The authors refute the claims from Metghalchi et al.’s (2005, 2011) papers and Brock et al.’s (1992) paper. The authors conclude that the market is efficient and investors cannot gain benefits from moving average technical trading rule. Lastly, the authors find that the decisions from MA(1-50) and MA(1-200) are highly correlated; therefore, the length of periods used in long-period moving average is flexible.

It is one of the first studies about different forms of returns, their conclusions on the market efficiency, and the flexibility of long-term moving period for moving average buy/sell technical rules.

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.

This chapter extends the work of Baltagi, Bresson, Chaturvedi, and Lacroix (2018) to the popular dynamic panel data model. The authors investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, the authors consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner’s (1986) g-priors for the variance–covariance matrices. The authors propose a general “toolbox” for a wide range of specifications which includes the dynamic panel model with random effects, with cross-correlated effects à la Chamberlain, for the Hausman–Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using a Monte Carlo simulation study, the authors compare the finite sample properties of the proposed estimator to those of standard classical estimators. The chapter contributes to the dynamic panel data literature by proposing a general robust Bayesian framework which encompasses the conventional frequentist specifications and their associated estimation methods as special cases.