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The purpose of this paper is to analyze the differences between the actual mortgage prompt and late payments and their respective expected measures from 2004 to 2010 to spot early symptoms of housing crisis.

This paper explores these discrepancies across the entire US market and along various delinquency lengths of 30, 60 and 90 days. This paper constructs a Bayesian forecasting model that relies on prior distributional properties of diverse time horizons.

Abnormal mortgage delinquency rates are identified in real time and can be served as early symptoms for housing crisis.

The statistical scheme proposed in this paper can function as a valuable predictive tool for lending institutions, bank audit companies, regulatory bodies and real estate professional investors who examine changes in economic settings and trends in short sale leads.

The abnormal mortgage delinquencies can serve as indicators of changes in economic fundamentals and early signs of a mounting housing crisis.

This paper presents a unique statistical technique in the context of mortgage delinquencies.

Currently, the design, technological features of voices, and their analysis of various applications are being simulated with the requirement to communicate at a greater distance or more discreetly. The purpose of this study is to explore how voices and their analyses are used in modern literature to generate a variety of solutions, of which only a few successful models exist.

The mel-frequency cepstral coefficient (MFCC), average magnitude difference function, cepstrum analysis and other voice characteristics are effectively modeled and implemented using mathematical modeling with variable weights parametric for each algorithm, which can be used with or without noises. Improvising the design characteristics and their weights with different supervised algorithms that regulate the design model simulation.

Different data models have been influenced by the parametric range and solution analysis in different space parameters, such as frequency or time model, with features such as without, with and after noise reduction. The frequency response of the current design can be analyzed through the Windowing techniques.

A new model and its implementation scenario with pervasive computational algorithms’ (PCA) (such as the hybrid PCA with AdaBoost (HPCA), PCA with bag of features and improved PCA with bag of features) relating the different features such as MFCC, power spectrum, pitch, Window techniques, etc. are calculated using the HPCA. The features are accumulated on the matrix formulations and govern the design feature comparison and its feature classification for improved performance parameters, as mentioned in the results.

Microfinance plays a crucial role to reduce the poverty in developing countries. For that reason, the effectiveness of microfinance institutions (MFIs) is widely investigated in the literature. This study examines the levels of efficiency of MFIs in Vietnam and their determinants. The research, then, evaluates the relationship between MFIs’ efficiency and poverty reduction. Data are mainly taken from the MIX market website and other empirical researches. Regarding the methodology, two-stage data envelopment analysis (DEA) is used to estimate MFIs’ efficiency scores in Vietnam and their determinants, while meta-analysis and statistic descriptions are employed to examine the relationship between MFIs’ efficiency and poverty reduction. The findings show that technical efficiency (TE) of MFIs in Vietnam is considerably high with the average TE score and efficiency of scale being 85.5% and 94.7%, respectively. Size, age, outreach, and market target of MFIs are found not to be determinants of efficiency, while capital structure is. Also, many researches confirm the impact of MFIs’ efficiency to poverty reduction. However, the relationship is different between countries because of particular characteristics and operational mechanisms.

The purpose of this paper is to explore the linkage between agricultural sector and macroeconomic factors with farm financial health. It considers whether agricultural lenders can more accurately anticipate changes in the credit quality of their portfolios by considering broad economic indicators outside the agriculture sector.

This paper examines firm, sector, and macroeconomic drivers of probability of default (PD) migrations from a sample of 153 grain farms of actual lender data from Farm Credit Mid-America’s portfolio. A series of ordered logit models are developed.

Farm-level and sector-level variables have the most significant impact on PD migrations. Equity to asset ratios, working capital to gross farm income ratios, and gross corn income per acre are found to be the most significant drivers of PD migrations. Macroeconomic variables are shown to unreliably forecast PD migrations, suggesting that agricultural lenders should emphasize firm and sector variables over macroeconomic factors in credit risk models.

This paper builds the literature on agricultural credit risk by testing a broader set of sector and macroeconomic variables than previous articles. Also, prior articles measured the direction but not magnitude of PD migrations; the ordered model in the analysis measures both.

The purpose of this study is to investigate the relationship between predictability in return and investment strategy performance. Two measures that characterize investment strategies within a mean‐variance framework, an activity measure and a style measure, are developed and the performance of alternative strategies (e.g. contrarian, momentum, etc.) is examined when risky asset returns are mean reverting.

Returns are assumed to follow a multivariate Ornstein‐Uhlenbeck process, where reversion to a time‐varying mean is governed by an additional variable set, similar to that proposed by Lo and Wang (1995). Depending on its parameterization, this process is capable of producing an autocorrelation pattern consistent with empirical evidence, that is, positive autocorrelation in short‐horizon returns and negative autocorrelation in long‐horizon returns.

The results, for four uninformed investment strategies and assuming that returns are generated by a simple univariate Ornstein‐Uhlenbeck process, show that the unadjusted returns from the contrarian (momentum) strategy are greater than those from the other strategies when the mean reversion parameter, α, is greater than (less than) one. The results are expected, given the relationship between α and the first‐order autocorrelation in returns. The risk level (measured by either the standard deviation of returns or beta) of the contrarian strategy is the lowest at essentially all levels of mean reversion and the risk‐adjusted returns from the contrarian strategy, measured by the both the Sharpe and Treynor ratios, dominate those from the other strategies.

In future research, a number of issues not considered in this study may be investigated. The style measure developed here can be used to determine whether the results obtained hold when an informed, mean‐variance efficient active strategy is employed. In addition, the performance of both the informed and uninformed strategies may be examined under the assumption that the risky return process follows a multivariate Ornstein‐Uhlenbeck process. This work should provide findings that facilitate the separation of fund risk due to dynamic strategies from that due to time‐varying expected returns.

The methodology used here may be easily extended to consider a number of important issues, such as the frequency of portfolio rebalancing, transactions costs, and multiple asset portfolios, that are encountered in practice.

The approach used here provides insight into how predictability affects the relative performance of tactical investment strategies and, thus, may serve as a basis for determining the magnitude and persistence in autocorrelation required for active investment strategies to yield profits significantly different from those of passive strategies. In this sense, this study may have appeal for both academics and investment professionals.

This paper aims to examine the short-term structure of the impact of R&D investments on turnover growth, indicating differences between tangible and intangible investments. The main questions are whether R&D and capital investments accompany firms' growth in the subsequent periods and how this relationship depends on other characteristics of the firms, such as size and industry. In addition, the authors study the relationship between R&D investments and the autocorrelation dynamics of firm growth.

The paper uses the European Industrial R&D Investment Scoreboard as data source. This data source includes 1,000 European companies with information on employees, turnover, sector affiliation and details on capital expenditure and R&D expenditure.

The authors find that R&D activities have, on average, a positive effect on turnover growth, while capital investments show both, positive and negative, relationships with firm growth. The relationship and its temporal structure strongly depend on firm size and industry affiliation as well as whether investments are considered as one-time or permanent activities.

Usually, the impacts of firm characteristics on firm growth are studied without explicitly considering time. Firm characteristics and firm growth are usually measured and examined at the same point in time. In contrast, the study will focus on the short-term structure of the influence of firm characteristics on turnover growth, especially the impact of R&D investments.

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

This study aims to examine whether earnings autocorrelation affects the risk of an accounting restatement.

This paper uses logistic regression and identifies restatements between 2004 and 2016. Following prior research, (Dao et al., 2012; Francis and Michas, 2013; Francis et al., 2013; Lennox and Li, 2014; Lobo and Zhao, 2013; Paterson and Valencia, 2011), this study allows time between the end of our sample period and the date that this study obtained the restatement data because it takes time for material misstatements to be identified.

Bryan et al. (2018) report a negative association between autocorrelation and audit fees, suggesting that auditors view lower autocorrelation as increasing inherent risk. This study finds that autocorrelation is negatively related to accounting restatements, implying that although auditors react to lower autocorrelation by increasing their risk assessments (Bryan et al., 2018), their risk response is not sufficient. This study finds that autocorrelation has a fairly large effect: a shift from the 75th to the 25th percentile of autocorrelation is associated with a 9.38% increase in the likelihood of a restatement.

This study contributes to the stream of research that investigates the determinants of restatements. Not only do this study identifies autocorrelation as a factor that contributes to restatements, but importantly, this study’s results reveal a fairly substantial effect size: a shift from the 75th to the 25th percentile of autocorrelation is associated with a 9.38% increase in the likelihood of a restatement. While Bryan et al. (2018) find that autocorrelation affects audit fees, this study links autocorrelation to a more drastic consequence: accounting restatements.

The phenomenon of positive autocorrelation in daily stock index returns is often viewed as a consequence of stable behavioural patterns of certain investor groups (see, e.g., Sentana & Wadhwani, 1992; Koutmos, 1997). However, such patterns may change due to extreme events, that is, financial crises, and thus affect the autocorrelation in returns. Emerging markets and especially BRIC countries have experienced severe crises in the last 20 years and are therefore a suitable object for studying this effect.

The focus of this chapter is on identifying substantial changes in the autocorrelation of BRIC markets' index returns after experiencing upheavals of the financial system. For this purpose, we look for structural breaks in the parameters of an ARMA–GARCH model with the standard endogenous search procedure.

Our approach yields no statistically significant evidence of the autocorrelation changes due to the crises. Only in India the decline in autocorrelation in 1998 seems to be economically relevant, but is not significant statistically. Significant shifts that we could identify were rather related to microstructural changes, such as abolishment of price change limits by China and the removal of a leading player in India's market in 1992. All in all our results suggest that even though extreme negative events on financial markets may induce changes in feedback trading strategies, their influence on autocorrelation is not pronounced enough. The impact of other factors, in the first place of regulatory changes, seems to be of larger relevance.

The failure of the efficient market hypothesis has a direct bearing on the Geometric Brownian Motion model of asset returns. The current paper aims to investigate the effect that the autocorrelation in the time‐series of returns has on the calculation of expected shortfall (ES) for an asset‐liability investor.

The regression model is selected according to the Akaike and the Schwarz information criterion. A series of tests are used to insure the stability of the autocorrelation parameters. Autocorrelation‐adjusted formulas for volatility and cross‐asset correlations are then employed for the computations.

The presence of autocorrelation changes the values of most of the correlation parameters used in the calculation of the ES of the risk bearing capital (RBC) – in some cases the cross‐asset correlation parameters double. Once the presence of smoothing is accounted for, the ES increases by 1 per cent in relative value.

Other asset classes may also feature smoothed time‐series requiring thus an account of their autocorrelation structure and their interaction with the property asset. An analysis of the time stability of the cross‐asset correlations may also improve the estimation of the optimal RBC.

The proposed method focuses on the proper calculation of the RBC through the judicious estimation of the relevant risk measure for an investor who, while not having access to the underlying data pool from which the property index is computed, cannot adjust the index for the potential presence of temporal aggregation and market illiquidity.