Sparse Signals in the Cross‐Section of Returns

This paper applies the Least Absolute Shrinkage and Selection Operator (LASSO) to make rolling one‐minute‐ahead return forecasts using the entire cross‐section of lagged returns as candidate predictors. The LASSO increases both out‐of‐sample fit and forecast‐implied Sharpe ratios. This out‐of‐sample success comes from identifying predictors that are unexpected, short‐lived, and sparse. Although the LASSO uses a statistical rule rather than economic intuition to identify predictors, the predictors it identifies are nevertheless associated with economically meaningful events: the LASSO tends to identify as predictors stocks with news about fundamentals.     

EDITORIAL: TAX REFORM AND SIMPLIFICATION

Major tax reform to simplify the UK tax system is possible. Merging income taxes, social security taxes and corporation tax into only a single flat-rate of tax on all incomes would be a radical start. Avoiding taxes on income and expenditure which are 'too high' requires major reductions in government spending, mainly on the welfare state. Given the political will, over a period of years we really can hugely improve the UK tax system.     

Gender and Piece Rates, Commissions, and Bonuses

Previous work shows that establishments with higher proportions of women are more likely to use piece rates but that individual women are less likely to receive performance pay. We present a model in which lower expected tenure and labor force attachment are positively associated with piece rates but are negatively associated with other forms of performance pay. Analysis of the National Longitudinal Survey of Youth (NLSY) confirms that women are more likely to be paid piece rates and simultaneously less likely to be paid commissions and bonuses.     

Identifying “best” applicants in recruiting using data envelopment analysis

Selecting the most promising candidates to fill an open position can be a difficult task when there are many applicants. Each applicant achieves certain performance levels in various categories and the resulting information can be overwhelming. We demonstrate how data envelopment analysis (DEA) can be used as a fair screening and sorting tool to support the candidate selection and decision-making process. Each applicant is viewed as an entity with multiple achievements. Without any a priori preference or information on the multiple achievements, DEA identifies the non-dominated solutions, which, in our case, represent the “best” candidates. A DEA-aided recruiting process was developed that (1) determines the performance levels of the “best” candidates relative to other applicants; (2) evaluates the degree of excellence of “best” candidates’ performance; (3) forms consistent tradeoff information on multiple recruiting criteria among search committee members, and, then, (4) clusters the applicants.     

A Paleoeconomic Theory of Co-Evolution and Extinction of Domesticable Animals

One Pleistocene mystery is why early North Americans eradicated their large, potentially domesticable animals (e.g., horses), whereas early Europeans did not. A commonly‐held hypothesis is that European species were evasive due to co‐evolution with hominids, whereas North American animals were naïve and unable to adapt quickly enough when experienced human hunters arrived from Eurasia. We explore this hypothesis with a paleoeconomic model of co‐evolution that integrates human hunting investments and wildlife population responses. We find that investments in hunting ability, based on the relative scarcity of prey species, could have mattered more than wildlife ‘naivety’ in explaining the extinction.     

Negotiation support using the Graph Model for Conflict Resolution

The Graph Model for Conflict Resolution is a flexible methodology for systematically studying strategic conflicts in the real world, and is therefore a natural tool for negotiation support. The basic definitions underlying the graph model are reviewed, and the techniques for analysis and interpretation are discussed. The modeling and analysis of a case study, an international trade negotiation concerning the export of Canadian softwood lumber to the United States, are used to demonstrate the practical application of the Graph Model for Conflict Resolution as a negotiation support tool. The modeling and analysis is carried out using the GMCR software system. The ability of the Graph Model for Conflict Resolution to provide insights and advice to negotiators is emphasized.