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The purpose of this paper is to examine the relationship between financial regulatory transparency and financial development. To achieve this goal, we collect data for 33 middle-low-income countries over the period 1998–2014 and estimate a dynamic panel by the two-step system generalized method of moments. We find that, beyond the standard factors, a higher disclosure of information about macroprudential data of financial institutions is a prerequisite for a well-developed financial system. The positive association between financial regulatory transparency and financial development implies that a transparency-based policy is mandatory to ensure the development of financial systems in middle-low-income economies. We further analyze the potential mediating role of two proxies of financial instability (banking crisis and non-performing loans (NPLs)) in explaining the effect of financial regulatory transparency on financial development. In doing so, our paper provides new evidence and insights into the long-standing discussion of the determinants of financial development.

It has become popular recently to apply the multifractal formalism of statistical physics (scaling analysis of structure functions and f(α) singularity spectrum analysis) to financial data. The outcome of such studies is a nonlinear shape of the structure function and a nontrivial behavior of the spectrum. Eventually, this literature has moved from basic data analysis to estimation of particular variants of multifractal models for asset returns via fitting of the empirical τ(q) and f(α) functions. Here, we reinvestigate earlier claims of multifractality using four long time series of important financial markets. Taking the recently proposed multifractal models of asset returns as our starting point, we show that the typical "scaling estimators" used in the physics literature are unable to distinguish between spurious and "true" multiscaling of financial data. Designing explicit tests for multiscaling, we can in no case reject the null hypothesis that the apparent curvature of both the scaling function and the Hölder spectrum are spuriously generated by the particular fat-tailed distribution of financial data. Given the well-known overwhelming evidence in favor of different degrees of long-term dependence in the powers of returns, we interpret this inability to reject the null hypothesis of multiscaling as a lack of discriminatory power of the standard approach rather than as a true rejection of multiscaling. However, the complete "failure" of the multifractal apparatus in this setting also raises the question whether results in other areas (like geophysics) suffer from similar shortcomings of the traditional methodology.

We evaluate the Arrows Classification Method (ACM) for grouping objects based on the similarity of their data. This is a new method, which aims to achieve a balance between the conflicting objectives of maximizing internal cohesion and external isolation in the output groups. The method is widely applicable, especially in simulation input and output modelling, and has previously been used for grouping machines on an assembly line, based on data on time-to-repair; and hospital procedures, based on length-of-stay data. The similarity of the data from a pair of objects is measured using the two-sample Cramér-von-Mises goodness of fit statistic, with bootstrapping employed to find the significance or p-value of the calculated statistic. The p-values coming from the paired comparisons serve as inputs to the ACM, and allow the objects to be classified such that no pair of objects that are grouped together have significantly different data. In this article, we give the technical details of the method and evaluate its use through testing with specially generated samples. We will also demonstrate its practical application with two real examples.

This paper proposes a body-biased bootstrapped CMOS driver for subthreshold applications. The proposed driver has been implemented with the same number of transistors as conventional bootstrapped CMOS driver. The performance of the subthreshold bootstrapped CMOS driver has been compared with the conventional bootstrapped CMOS driver. Our results show that the proposed body-biased subthreshold bootstrapped CMOS driver has 37% reduction in delay and 39% reduction in power dissipation compared to conventional bootstrapped CMOS driver. The proposed driver is more suitable to drive large loads compared to the conventional driver and operates better at subthreshold region.

This paper presents a 32-GS/s front-end sampling circuit (FESC) in 65-nm CMOS. The FESC is designed for a 32-channel time-interleaved analog-to-digital converter (ADC), and the $4\times 8$ two-stage interleaving structure leads to a good trade-off between bandwidth and linearity. The analysis and cancellation of charge injection, clock feedthrough, and signal feedthrough are presented. Inductor peaking technique is adopted to extend the bandwidth of the buffer between the first and the second stage. Based on the simulation results, the proposed FESC consumes 136mW at 32 GS/s, and the signal-to-noise ratio (SNDR) is up to 39.55 dB at Nyquist input, achieving a state-of-the-art power efficiency.

Consider the Milne problem with geometric correction in a 3D convex domain. Via bootstrapping arguments, we establish $W^{1,\infty }$-regularity for its solutions. Combined with a uniform $L^6$-estimate, such regularity leads to the validity of diffusive expansion for the neutron transport equation with diffusive boundary conditions.

Traditional text summarization systems have not used the category information of documents to be summarized. However, the estimated weights of each word can be often biased on small data such as a single document. Thus we proposed an effective feature-weighting method for document summarization that utilizes category information and solves the biased probability problem. The method uses a category-based smoothing method and a bootstrapping framework. As a result, in our experiments, our proposed summarization method achieves better performance than other statistical sentence-extraction methods.

In this paper, we take an algebraic approach to the generalized DGHV scheme without using bootstrapping technique. We investigate the homomorphic evaluation algorithm in detail and provide several sufficient conditions for correct homomorphic evaluation of arbitrary polynomial circuits. Compared with the bootstrapping procedure, we show that our approach is much simpler and more efficient. Moreover, we prove that both of the key sizes are actually bounded above and that these upper-bounds depend only on the plaintext space and the security parameter. Hence we need only one pair of secret key and public key for correct homomorphic evaluation of all polynomial circuits, which implies the generalized DGHV scheme is a fully homomorphic encryption in itself. Thus our approach shows that the generalized DGHV scheme provides extremely simple and efficient algorithms.

During the COVID-19 outbreak that took place in early 2020, restrictions on human activities were imposed by government-mandated lockdowns and stay-at-home orders. In this study, we analyse the impact of reduced anthropogenic activities on the detection of seismic events. We hypothesise and show that with reduced background noise levels due to the COVID-19 lockdowns, low-magnitude earthquakes are more easily detectable. We investigate the magnitudes of earthquakes recorded at the seismometers before and after COVID-19 lockdowns for two regions — Cascadia Subduction Zone and New Zealand. Gutenberg–Richter law, which gives a relationship between the number of earthquakes and their magnitudes (b-value), was applied in these two areas. Our results point to an increase in detection of smaller-magnitude earthquakes, as observed by an increased b-value during the COVID-19 period compared to those obtained in the pre-COVID time periods. Previous studies have shown that changes in b-value of an area over a sustained period of time affect the short-time probabilistic risk assessment. The variability of b-value also gives useful insights into the prevailing stress state of the region, crustal heterogeneity, pore pressure and tectonic setting of the area.

The classical capital asset pricing model postulates a linear relationship between stock returns and stock risks. However, a number of subsequent empirical studies have revealed some anomalies in this relationship, especially for firms with small size and high book-to-market values. A possible explanation for the anomalies is the existence of threshold effects in the proxies of stock risks. However, conventional threshold models only allow for one threshold variable, which limits their applicability in this context. In this paper, we address this issue by applying the econometric technique developed by Bai et al. (2012). We estimate the joint threshold effects of firm size and book-to-market equity ratio on the stock returns using a sample of 5,271 US firms. The test results yield clear evidence for the existence of threshold effects in both firm features. We find that abnormal returns exist when the firm size falls below 52.04 million USD and the book-to-market ratio exceeds 0.4085.

Machine learning-based intrusion detection systems are suffering from high dimensionality of network traffics. The presence of high dimensional data is resulting in low classification accuracy. In this work, we propose a bootstrap-based homogeneous ensemble feature selection (BHmEFS) method to select a subset of relevant and non-redundant features that improves classification accuracy. In this method, three sample data have been generated from the original dataset during the bootstrapping process. From each of the three acquired sample data, the Chi-square method selects essential features subsets. The intersection method combines these three output subsets employing a homogeneous approach to obtain the ensemble features subset. The performance of BHmEFS and the Chi-square method is evaluated by using the J48 classifier. We use each of the three bootstrap samples and the original dataset to evaluate the Chi-square method. The experimental results in a multi-class NSL-KDD dataset show that the BHmEFS achieves better classification accuracy when compared with the Chi-square and other methods.

A confidence distribution for a scalar parameter provides confidence intervals by its quantiles. A confidence net represents a family of nested confidence regions indexed by degree of confidence. Confidence nets are obtained by mapping the deviance function into the unit interval. For high-dimensional parameters, product confidence nets, represented as families of simultaneous confidence bands, are obtained from bootstrapping utilizing the abc-method. The method is applied to Norwegian personal income data.

This paper reports about the development of a Named Entity Recognition (NER) system in Bengali. A pattern directed bootstrapping method has been used to develop the NER system from a tagged Bengali news corpus, developed from the web. Different tags of the tagged news corpus help to identify the seed data in the system. The training corpus is initially tagged against the different seed data and a lexical contextual seed pattern is generated for each tag. The entire training corpus is shallow parsed to identify the occurrence of these initial seed patterns and further patterns are generated through bootstrapping. Patterns that occur in the entire training corpus above a certain threshold frequency are considered as the final set of patterns learnt from the training corpus. The test corpus is shallow parsed to identify the occurrence of these patterns and estimate the named entities. System has been tested with four manually tagged Bengali news corpus (Gold Standard Test Sets) and it has demonstrated the highest Recall, Precision and F-Score values of 63.3%, 84.8% and 73.2% respectively.

This study investigates two ways that sample selection can impact inferences about market efficiency: (1) unintentional, nonrandom exclusion of observations because of lack of available data for some firm-years (“passive deletion”); and (2) the effects of extreme return observations. The analysis proposes and illustrates a set of simple diagnostic tests designed to assess the sensitivity of estimated hedge portfolio returns to sample composition. These diagnostics are applied to the accrual anomaly and the forecast-to-price anomaly and the results indicate that the forecast-to-price anomaly is not robust to the effects of passive deletion. Moreover, extreme returns — as few as 100 firm-year observations — appear to generate the observed abnormal returns to both strategies.

In this chapter, we shall study bonds and their term structures. Bonds are a major class of investment assets distinct from equities, and they have salient features that will be explained. Continuous time stochastic processes are briefly introduced in this chapter to show their usage in modelling bond prices and therefore the resulting credit spreads and yields. Multiple regression analyses involving explanation of credit spreads and of bond returns are described.