Multivariate Return Decomposition: Theory and Implications

In this paper, we propose a model based on multivariate decomposition of multiplicative – absolute values and signs – components of asset returns. In the m-variate case, the marginals for the m absolute values and the binary marginals for the m directions are linked through a 2m-dimensional copula. The approach is detailed in the case of a bivariate decomposition. We outline the construction of the likelihood function and the computation of different conditional measures. The finite-sample properties of the maximum likelihood estimator are assessed by simulation. An application to predicting bond returns illustrates the usefulness of the proposed method.     

On smoothness of Tukey depth contours

The smoothness of Tukey depth contours is a regularity condition often encountered in asymptotic theory, among others. This condition ensures that the Tukey depth fully characterizes the underlying multivariate probability distribution. In this paper we demonstrate that this regularity condition is rarely satisfied. It is shown that even well-behaved probability distributions with symmetrical, smooth and (strictly) quasi-concave densities may have non-smooth Tukey depth contours, and that the smoothness behaviour of depth contours is fairly unpredictable.     

Feature Selection for Consistent Biclustering via Fractional 0–1 Programming

Biclustering consists in simultaneous partitioning of the set of samples and the set of their attributes (features) into subsets (classes). Samples and features classified together are supposed to have a high relevance to each other which can be observed by intensity of their expressions. We define the notion of consistency for biclustering using interrelation between centroids of sample and feature classes. We prove that consistent biclustering implies separability of the classes by convex cones. While previous works on biclustering concentrated on unsupervised learning and did not consider employing a training set, whose classification is given, we propose a model for supervised biclustering, whose consistency is achieved by feature selection. The developed model involves solution of a fractional 0–1 programming problem. Preliminary computational results on microarray data mining problems are reported.This research work was partially supported by NSF, NIH and AirForce grants.     

Does state ownership really matter? The dynamic alignment of China's resource environment and firm internationalization strategies

Recent research suggests that unequal access to home country institutional resources affects firm internationalization strategies. We add to this debate, based on an analysis of state-owned (SOEs) and non-state-owned (NSOEs) Chinese mining firms, by developing a more dynamic and multi-layered understanding of this interplay. We find that home institutional support can be ownership-based or performance-based, whereby the former benefits SOEs and the latter favors high-performing NSOEs. Combined, these support structures serve as institutional control mechanisms in promoting competition, performance and loyalty to national policies. In addition, they establish a permanent link between firm- and home country-specific assets and incentivize firms to develop dynamic resource access capabilities at home and abroad. Our findings inform our understanding of the firms-institutions nexus and Chinese foreign direct investment dynamics, especially in industries of strategic importance to the state.     

Modeling and forecasting realized covariance matrices with accounting for leverage

The existing dynamic models for realized covariance matrices do not account for an asymmetry with respect to price directions. We modify the recently proposed conditional autoregressive Wishart (CAW) model to allow for the leverage effect. In the conditional threshold autoregressive Wishart (CTAW) model and its variations the parameters governing each asset's volatility and covolatility dynamics are subject to switches that depend on signs of previous asset returns or previous market returns. We evaluate the predictive ability of the CTAW model and its restricted and extended specifications from both statistical and economic points of view. We find strong evidence that many CTAW specifications have a better in-sample fit and tend to have a better out-of-sample predictive ability than the original CAW model and its modifications.     

Flexible Work Options and Mothers' Perceptions of Career Harm

Whether or not mothers, who often struggle with balancing work and parenting responsibilities, perceive that they face career harm in exchange for control over flexible work options at their jobs is an unanswered question. Using 2009 original data from a random‐digit‐dial telephone survey of 441 mothers located across the United States, this study focuses on how control over two latent variables measuring flexibility, flexible work arrangements (such as scheduling and place of work) and time‐off options, influences mothers' career harm perceptions in a total of three work domains: (1) wages/earnings, (2) raises or promotions, and (3) job evaluations. We find perceptions of career harm among only one‐fifth of mothers; in addition, control over time‐off options reduced perceived career damage related to parenting duties. Mothers may have less to fear than previously hypothesized about the potential sacrifices they have to make when they have significant control over certain flexibility options.     

Finding checkerboard patterns via fractional 0–1 programming

Biclustering is a data mining technique used to simultaneously partition the set of samples and the set of their attributes (features) into subsets (clusters). Samples and features clustered together are supposed to have a high relevance to each other. In this paper we provide a new mathematical programming formulation for unsupervised biclustering. The proposed model involves the solution of a fractional 0–1 programming problem. A linear-mixed 0–1 reformulation as well as two heuristic-based approaches are developed. Encouraging computational results on clustering real DNA microarray data sets are presented. In addition, we also discuss theoretical computational complexity issues related to biclustering.