The puzzle of sharecropping: A survey of theories

The geographically pervasive and historically tenacious institution of sharecropping has always been a fruitful source of great economic controversies. While the earlier writings were almost universal in their disapproval of sharecropping (and seemed to ascribe little or no rationale for the persistence of it), an important characteristic of recent writings has been to uphold a plurality of views regarding the virtues of this institution. Not only have the recent papers been quite successful in highlighting the basic rationale behind the continuance of this somewhat enigmatic institution, they have addressed themselves to a number of important issues hitherto unexplored. Many strands of recent theoretical analyses have been brought to bear on the analysis of these problems, ranging from the simple competitive model to the esoteric theory of core, from the tricky Coase theorem of property rights to the intricate theory of portfolio behaviour, the principal-agent formulation and recent advances from the economics of information. Based on the present survey of theories, it seems fair to conclude that these recent sophisticated models have been quite illuminating and hold promise of further insights in future.     

Softlifting: Exploring Determinants of Attitude

Softlifting, or the illegal duplication of copyrighted software by individuals for personal use, is a serious and costly problem for software developers and distributors. Understanding the factors that determine attitude toward softlifting is important in order to ascertain what motivates individuals to engage in the behavior. We examine a number of factors, including personal moral obligation (PMO), perceived usefulness, and awareness of the laws and regulations governing software acquisition and use, along with facets of personal self-identity that may play a role in the development of attitudes and therefore intentions regarding this behavior. These factors are examined across multiple settings expected to be pertinent to our survey respondents: home, work and school. Personal moral obligation and perceived usefulness are significant predictors of attitude across all settings. Past behavior is a significant predictor of intention across all settings, and a significant predictor of attitude in the home setting. We find evidence that awareness of the law causes a less favorable evaluation of softlifting in the school setting only, but has little effect in the home and work settings. As in previous studies, attitude is a significant predictor of intent. We do not find indications that one’s personal self-identity influences one’s attitude towards the behavior and the intention to perform it, except in the case of legal identity, where marginally significant effects are found in the work environment. Dr. Tim Goles is an assistant Professor in the Information Systems Department of the University of Texas-San Antonio. He has numerous publications, most of which pertain to information systems. Dr. Bandula Jayatilaka is an Assistant Professor in the School of Mangement in Binghamton University-SUNY. Most of his publications pertain to information systems. Dr. Beena George is an Assistant Professor at the Cameron School of Business, University of St. Thomas, Houston, Most of her pblications pertain to information systems. Dr. Linda Parsons is an Assistant Professor in the Accounting Department at George Mason University. Most of her publications pertain to accounting information systems and nonprofit organizations. Dr. David S. Taylor is an Assitant Professor at Sam Houston State University. Most of his publications pertain to information systems. Rebecca Brune has a strong accounting background; her work is predominantly in the information systems field.     

Venture Capitalists, Business Angels, and Performance of Entrepreneurial IPOs in the UK and France

Abstract:  Using a unique sample of 444 entrepreneurial IPOs in the UK and France, this paper analyses the investment patterns and the stock-market performance effects of two types of early stage investors: venture capitalists (VCs) and business angels (BAs). Extending existing research, we identify important endogeneity and institutional effects. Our findings indicate that UK IPOs have a higher retained ownership and lower participation ratio by BAs, but a lower retained ownership and participation ratio by VCs than in France. BA and VC investments are substitutes, and they are endogenously determined by a number of firm- and founder-related factors, such as founder ownership and external board 'interlocks', and underwriter reputation. UK VCs are effective third-party certifying agents who reduce underpricing in UK IPOs, whereas in French IPOs they increase it by appearing to engage in grandstanding. This certification effect is more significant in UK IPOs involving both high VC and BA ownership. Finally, underpricing increases with VC participation ratio, where the higher exit of VCs seems to increase the risk premium required by outside investors, in particular in the UK.     

Features selection,data mining and finacial risk classification: a comparative study

The aim of this paper is to compare several predictive models that combine features selection techniques with data mining classifiers in the context of credit risk assessment in terms of accuracy, sensitivity and specificity statistics. The t‐statistic, Battacharrayia statistic, the area between the receiver operating characteristic, Wilcoxon statistic, relative entropy, and genetic algorithms were used for the features selection task. The selected features are used to train the support vector machine (SVM) classifier, backpropagation neural network, radial basis function neural network, linear discriminant analysis and naive Bayes classifier. Results from three datasets using a 10‐fold cross‐validation technique showed that the SVM provides the best accuracy under all features selections techniques adopted in the study for all three datasets. Therefore, the SVM is an attractive classifier to be used in real applications for bankruptcy prediction in corporate finance and financial risk management in financial institutions. In addition, we found that our best results are superior to earlier studies on the same datasets.     

A two‐step system for direct bank telemarketing outcome classification

A two‐step system is presented to improve prediction of telemarketing outcomes and to help the marketing management team effectively manage customer relationships in the banking industry. In the first step, several neural networks are trained with different categories of information to make initial predictions. In the second step, all initial predictions are combined by a single neural network to make a final prediction. Particle swarm optimization is employed to optimize the initial weights of each neural network in the ensemble system. Empirical results indicate that the two‐step system presented performs better than all its individual components. In addition, the two‐step system outperforms a baseline one where all categories of marketing information are used to train a single neural network. As a neural networks ensemble model, the proposed two‐step system is robust to noisy and nonlinear data, easy to interpret, suitable for large and heterogeneous marketing databases, fast and easy to implement.     

Forecasting profitability and earnings: a study of the UK market (1982–2000)

An approach recently developed by Fama and French (2000 Fama, EF and French, KR. 2000. Forecasting profitability and earnings. The Journal of Business, 73: 161–75.  ) is applied to the study of whether UK company profitability is mean-reverting. A sample of roughly 987 firms per year for a period from 1982–2000 is used, drawn from Datastream. In a simple partial adjustment model convergence towards the mean at a rate of about 25% per year is found. The results are very similar in direction to those of Fama and French (2000 Fama, EF and French, KR. 2000. Forecasting profitability and earnings. The Journal of Business, 73: 161–75.  ) but the results do not display significant non-linearities. The change in profitability appears to be more strongly influenced by dividends in the UK.     

A semiparametric stochastic input distance frontier model with application to the Indonesian banking industry

This paper proposes a semiparametric smooth-varying coefficient input distance frontier model with multiple outputs and multiple inputs, panel data, and determinants of technical inefficiency for the Indonesian banking industry during the period 2000 to 2015. The technology parameters are unknown functions of a set of environmental factors that shift the input distance frontier non-neutrally. The computationally simple constraint weighted bootstrapping method is employed to impose the regularity constraints on the distance function. As a by-product, total factor productivity (TFP) growth is estimated and decomposed into technical change, scale component, and efficiency change. The distance elasticities, marginal effects of the environmental factors on the distance elasticities, temporal behavior of technical efficiency, and also TFP growth and its components are investigated.