London Business School

About: London Business School is a education organization based out in London, England, United Kingdom. It is known for research contribution in the topics: Portfolio & Equity (finance). The organization has 1138 authors who have published 5118 publications receiving 437980 citations. The organization is also known as: LBS.

The Effect of Operational Performance and Focus on Profitability: A Longitudinal Study of the U.S. Airline Industry

Abstract: We study the impact of operational performance on profitability in the context of the U.S. domestic airline industry. In addition, we investigate the impact of focus [Skinner, W. 1974. The focused factory. Harvard Bus. Rev.52(3) 113--121] on profitability in services. We use quarterly data on all major carriers, available since the introduction of required reporting of service indicators to the U.S. Department of Transportation. Our analysis demonstrates two main points. First, the relationship between operational performance and profitability is contingent on a company's operating model; “focused” airlines show a link between late arrivals and profitability while full-service airlines do not. Also, capacity utilization is a stronger driver of profitability for full-service airlines than for focused airlines. Second, focused airlines outperform the rest of the industry in terms of profitability.

A Model of Mortgage Default

Abstract: This paper solves a dynamic model of households' mortgage decisions incorporating labor income, house price, inflation, and interest rate risk. It uses a zero-profit condition for mortgage lenders to solve for equilibrium mortgage rates given borrower characteristics and optimal decisions. The model quantifies the effects of adjustable vs. fixed mortgage rates, loan-to-value ratios, and mortgage affordability measures on mortgage premia and default. Mortgage selection by heterogeneous borrowers helps the model explain the higher default rates on adjustable-rate mortgages during the recent US housing downturn, and the variation in mortgage premia with the level of interest rates.

Size Matters: Optimal Calibration of Shrinkage Estimators for Portfolio Selection

Abstract: We carry out a comprehensive investigation of shrinkage estimators for asset allocation, and we find that size matters -- the shrinkage intensity plays a significant role in the performance of the resulting estimated optimal portfolios. We study both portfolios computed from shrinkage estimators of the moments of asset returns (shrinkage moments), as well as shrinkage portfolios obtained by shrinking the portfolio weights directly. We make several contributions in this field. First, we propose two novel calibration criteria for the vector of means and the inverse covariance matrix. Second, for the covariance matrix we propose a novel calibration criterion that takes the condition number optimally into account. Third, for shrinkage portfolios we study two novel calibration criteria. Fourth, we propose a simple multivariate smoothed bootstrap approach to construct the optimal shrinkage intensity. Finally, we carry out an extensive out-of-sample analysis with simulated and empirical datasets, and we characterize the performance of the different shrinkage estimators for portfolio selection.