How many factors are important in U.K. stock returns?

I use the sequential approach of Harvey and Liu ([2018]. Lucky factors (Working Paper). Duke University) to build linear factor models in U.K. stock returns among a set of 13 candidate factors using individual stocks and three groups of test portfolios between July 1983 and December 2017. My study finds that the Market factor is the dominant factor in reducing mispricing in individual stocks and test portfolios regardless of the pricing error metric used. The Market factor has a bigger impact when using a value weighting pricing error metric. Whether a second factor is used or not depends upon which metric is used for mispricing and the time period examined. My study finds support for a two-factor model for the whole sample period of the Market factor and the Conservative Minus Aggressive (CMA) factor of Fama and French ([2015]. “A five-factor asset pricing model.” Journal of Financial Economics 116: 1–22) when giving greater weight to the mispricing of larger companies.     

Ratio-based design hour determination for airport passenger terminal facilities

To avoid both over-design and under-sizing of airport passenger terminal facilities such as security checkpoints, the infrastructure is designed for a specifically determined design load. As such, the design load is considered for a short period of time, usually an hour of operation, during which peak, though not necessarily maximum, demand occurs. For strategic planning applications, future design loads can be determined by either fictitious flight schedules or ratio-based models which forecast the relationship between design load and annual demand. This study presents two ratio-based methods which allow the direct determination of design hour loads (DHL) for passenger terminal facilities. The unsaturated DHL model considers the relationship between observed passenger flows in the terminal and aggregated annual demand data. The saturated DHL model includes several operational constraints which limit the actual DHL, such as limitations in the runway system or the fleet mix operating at an airport. Both models are applied to two real-world airports, for which the DHL of the security checkpoint facilities is estimated from large datasets covering multiple years. Results are significant at the 5 % level and suggest that the proposed ratio-based methods are appropriate for airport strategic planning applications.     

Optimal granularity for portfolio choice

Many optimization-based portfolio rules fail to beat the simple 1/N rule out-of-sample because of parameter uncertainty. In this paper we suggest a grouping strategy in which we first form groups of equally weighted stocks and then optimize over the resulting groups only. This strategy aims at balancing the trade-off between the benefits from optimization and the losses from estimation risk. We rely on Monte-Carlo simulations to illustrate the performance of the strategy, and we derive the optimal group size for a simplified setup. Furthermore, we show that estimation risk also has an impact via the criterion by which the assets are sorted into groups (like the expected excess returns or betas), but does not negate the grouping approach. We relate our work to linear asset pricing models, and we conduct out of sample back-tests in order to confirm the validity of our grouping strategy empirically.     

Economic interpretations of DEA

Research on productive efficiency at the firm level has developed as an important and active strand of research the last decades, both within operations research, management science and economics. Two apparently different definitions of efficiency are examined, but it is shown that when both estimation methods are based on solving linear programming problems the definitions of efficiency are identical. The purpose of the paper is to give the basic ideas of efficiency analyses using DEA as a tool for researchers not so familiar with efficiency analysis and DEA. The concept of shadow prices is given special attention.     

Stochastic volatility: A tale of co-jumps,non-normality,GMM and high frequency data

In this article we introduce a linear–quadratic volatility model with co-jumps and show how to calibrate this model to a rich dataset. We apply GMM and more specifically match the moments of realized power and multi-power variations, which are obtained from high-frequency stock market data. Our model incorporates two salient features: the setting of simultaneous jumps in both return process and volatility process and the superposition structure of a continuous linear–quadratic volatility process and a Lévy-driven Ornstein–Uhlenbeck process. We compare the quality of fit for several models, and show that our model outperforms the conventional jump diffusion or Bates model. Besides that, we find evidence that the jump sizes are not normally distributed and that our model performs best when the distribution of jump-sizes is only specified through certain (co-) moment conditions. Monte Carlo experiments are employed to confirm this.     

How should we estimate value-relevance models? Insights from European data

We study the consequences of unobserved heterogeneity when employing different econometric methods in the estimation of two major value-relevance models: the Price Regression Model (PRM) and the Return Regression Model (RRM). Leveraging a large panel data set of European listed companies, we first demonstrate that robust Hausman tests and Breusch-Pagan Lagrange Multiplier tests are of fundamental importance to choose correctly among a fixed-effects model, a random-effects model, or a pooled OLS model. Second, we provide evidence that replacing firm fixed-effects with country and industry fixed-effects can lead to large differences in the magnitude of the key coefficients, with serious consequences for the interpretation of the effect of changes in earnings and book values per share on firm value. Finally, we offer recommendations to applied researchers aiming to improve the robustness of their econometric strategy.     

Asymptotics for Autocovariances and Integrated Periodograms for Linear Processes Observed at Lower Frequencies

One of the most frequently used class of processes in time series analysis is the one of linear processes. For many statistical quantities, among them sample autocovariances and sample autocorrelations, central limit theorems are available in the literature. We investigate classical linear processes under a nonstandard observation pattern; namely, we assume that we are only able to observe the linear process at a lower frequency. It is shown that such observation pattern destroys the linear structure of the observations and leads to substantially different asymptotic results for standard statistical quantities. Central limit theorems are given for sample autocovariances and sample autocorrelations as well as more general integrated periodograms and ratio statistics. Moreover, for specific autoregressive processes, the possibilities to estimate the parameters of the underlying autoregression from lower frequency observations are addressed. Finally, we suggest for autoregressions of order 2 a valid bootstrap procedure. A small simulation study demonstrates the performance of the bootstrap proposal for finite sample size.     

Income and Price Elasticities of Demand in South Africa: An Application of the Linear Expenditure System

This paper investigates the expenditure patterns of South African households using detailed cross‐sectional expenditure and price data that varies across region and time. Linear expenditure system parameter estimates are used to calculate income and price elasticities for a number of product categories at different points of the income distribution. We find substantial variation in the price and income elasticities of demand for items across the income distribution, with the bottom quartile being extremely sensitive to increases in the price of food and clothing items, and the top quartile being as sensitive as households in developed countries.     

“Make me feel special”: Are hierarchical loyalty programs a panacea for all brands? The role of brand concept

Despite extant literature, the most effective structure of loyalty programs is still heavily questioned among researchers. Building on the congruence principle, we examine the moderating role of brand concept. Our findings reveal that for symbolic brand concepts, customers perceive hierarchical loyalty program structures (which classify customers into tiers according to spending levels or other purchase activities) to be more congruent; this perception increases firm loyalty intentions. However, for functional brand concepts, customers perceive hierarchical and linear loyalty programs structures as equally congruent. Also for symbolic brand concepts, program structure appears as the most important feature in ensuring perceived congruence between a program and a brand, ahead of program benefits. These findings have important theoretical and managerial implications.     

Dynamic supply chain network design with capacity planning and multi-period pricing

This paper addresses a new problem in designing and planning a multi-echelon and multi-product supply chain network over a multi-period horizon in which customer zones have price-sensitive demands. Based on price-demand relationships, a generic method is presented to obtain price levels for products and then, a mixed-integer linear programming model is developed. Due to the problem intractability, a simulated annealing algorithm that uses some developed linear relaxation-based heuristics for capacity planning and pricing is presented. Numerical results demonstrate the significance of the model as well as the efficiency of the solution algorithm and linear relaxation-based heuristics.