Portfolio choice and mental accounts: A comparison with traditional approaches

peer reviewedThis paper analyses the ability of a realistic mental accounting model for portfolio choice (presented in Hübner and Lejeune (2021)) to compete with traditional utility alternatives. Das, Markowitz, Scheid, and Statman (2010) and Hübner and Lejeune (2021) have shown that the mental accounting framework embeds optimal allocations derived from quadratic utility optimization and/or the Gaussian distribution assumption. We complement their work by demonstrating here the flexibility and numerical superiority of the optimization outputs that a non-Gaussian version of HAMA generates with respect to two traditional expected utility maximization rules that produce a wide and relevant spectrum of portfolio allocation rules for a variety of realistic investor types: the decay rate approach proposed by Stutzer (2003), which is analogous to maximizing expected utility, and the class of flexible three-parameter utility functions (FTP) introduced by Conniffe (2007) that encompasses a wide set of popular utility functions

What Factors Shape Spatial Distribution of Biomass in Riparian Forests? Insights from a LiDAR Survey over a Large Area

Riparian ecosystems are home to a remarkable biodiversity, but have been degraded in many regions of the world. Vegetation biomass is central to several key functions of riparian systems. It is influenced by multiple factors, such as soil waterlogging, sediment input, flood, and human disturbance. However, knowledge is lacking on how these factors interact to shape spatial distribution of biomass in riparian forests. In this study, LiDAR data were used in an individual tree approach to map the aboveground biomass in riparian forests along 200 km of rivers in the Meuse catchment, in southern Belgium (Western Europe). Two approaches were tested, relying either on a LiDAR Canopy Height Model alone or in conjunction with a LiDAR point cloud. Cross-validated biomass relative mean square errors for 0.3 ha plots were, respectively, 27% and 22% for the two approaches. Spatial distribution of biomass patterns were driven by parcel history (and particularly vegetation age), followed by land use and topographical or geomorphological variables. Overall, anthropogenic factors were dominant over natural factors. However, vegetation patches located in the lower parts of the riparian zone exhibited a lower biomass than those in higher locations at the same age, presumably due to a combination of a more intense disturbance regime and more limiting growing conditions in the lower parts of the riparian zone. Similar approaches to ours could be deployed in other regions in order to better understand how biomass distribution patterns vary according to the climatic, geological or cultural contexts.Les écosystèmes riverains abritent une biodiversité remarquable, mais ils ont été dégradés dans de nombreuses régions du monde. La biomasse végétale est essentielle à plusieurs fonctions clés des systèmes riverains. Elle est influencée par de multiples facteurs, tels que l'engorgement du sol, l'apport de sédiments, les inondations et les perturbations humaines. Cependant, les connaissances concernant la façon dont ces facteurs interagissent pour façonner la distribution spatiale de la biomasse dans les forêts riveraines sont fragmentaires. Dans cette étude, les données LiDAR ont été utilisées dans une approche à l’échelle de l’arbre pour cartographier la biomasse aérienne dans les forêts riveraines le long de 200 km de rivières dans le bassin versant de la Meuse, dans le sud de la Belgique (Europe occidentale). Deux approches ont été testées, s'appuyant sur un modèle numérique de hauteur LiDAR seul ou en conjonction avec un nuage de points LiDAR. Les erreurs quadratiques moyennes relatives de la biomasse pour des parcelles de 0,3 ha étaient respectivement de 27 % et 22 % pour les deux approches. La distribution spatiale des modèles de biomasse était surtout influencée par l'historique des parcelles (et en particulier l'âge de la végétation), suivie par l'utilisation des terres et les variables topographiques ou géomorphologiques. Dans l'ensemble, les facteurs anthropiques étaient dominants par rapport aux facteurs naturels. Cependant, les parcelles de végétation situées dans les parties inférieures de la zone riveraine présentaient une biomasse plus faible que celles situées dans les parties supérieures au même âge, probablement en raison de la combinaison d'un régime de perturbation plus intense et de conditions de croissance plus limitantes dans les parties inférieures de la zone riveraine. Des approches similaires à la nôtre pourraient être déployées dans d'autres régions afin de mieux comprendre comment les schémas de distribution de la biomasse varient en fonction des contextes climatiques, géologiques ou culturels.Peer reviewe

The Influence of Manga on the Graphic Novel

This material has been published in The Cambridge History of the Graphic Novel edited by Jan Baetens, Hugo Frey, Stephen E. Tabachnick. This version is free to view and download for personal use only. Not for re-distribution, re-sale or use in derivative works. © Cambridge University PressProviding a range of cogent examples, this chapter describes the influences of the Manga genre of comics strip on the Graphic Novel genre, over the last 35 years, considering the functions of domestication, foreignisation and transmedia on readers, markets and forms

Iterative multi-path tracking for video and volume segmentation with sparse point supervision

Recent machine learning strategies for segmentation tasks have shown great ability when trained on large pixel-wise annotated image datasets. It remains a major challenge however to aggregate such datasets, as the time and monetary cost associated with collecting extensive annotations is extremely high. This is particularly the case for generating precise pixel-wise annotations in video and volumetric image data. To this end, this work presents a novel framework to produce pixel-wise segmentations using minimal supervision. Our method relies on 2D point supervision, whereby a single 2D location within an object of interest is provided on each image of the data. Our method then estimates the object appearance in a semi-supervised fashion by learning object-image-specific features and by using these in a semi-supervised learning framework. Our object model is then used in a graph-based optimization problem that takes into account all provided locations and the image data in order to infer the complete pixel-wise segmentation. In practice, we solve this optimally as a tracking problem using a K-shortest path approach. Both the object model and segmentation are then refined iteratively to further improve the final segmentation. We show that by collecting 2D locations using a gaze tracker, our approach can provide state-of-the-art segmentations on a range of objects and image modalities (video and 3D volumes), and that these can then be used to train supervised machine learning classifiers

Mental accounts with horizon and asymmetry preferences

This paper extends mental accounting theory with an investment horizon and asymmetric trade-off between extreme gains and losses. This horizon-asymmetry mental accounting (HAMA) framework widens the spectrum of investors’ optimal portfolio choices considerably. Risk aversion, implied from the mean-variance portfolio theory, and the bond-to-stock ratio decline with the investment horizon. HAMA investors with a large gain–loss asymmetry trade-off are more concerned about skewness and kurtosis rather than variance. To apply the model to United States stock data, we develop a parsimonious semi-parametric version of HAMA that relies on the moments of return distributions. The analysis of optimal portfolios shows that investors who care significantly about upside potential hold asymmetric, leptokurtic, and less diversified allocations

Risk Horizon and Expected Market Returns

The paper proposes an equilibrium asset pricing model that accounts of the incomplete information on returns distribution and investors' preferences. Only moments up to order four of unknown unconditional distribution can be observed, and the model does not impose that portfolio diversi fication or moments preference should hold. Using Chebyshev-type of inequalities, an intuitive risk measure (risk horizon) is introduced with reference to the speed of convergence of a security's mean return to its expectations. By an arbitrage argument, this risk measure is related to the horizon of treasury securities in a system of equations that allows the calibration of the model parameters using term structure information. In particular, the expected return on the market portfolio can be endogenously estimated inside this system. The model calibration on U.S. market data provides plausible parameters estimates and interesting cyclical patterns in the time series of the expected return. The empirical relevance of these estimates is examined with tests of statistical and economic predictive ability for stock excess returns. The results provide signi ficant evidence on the added value of the estimates when compared to popular predictors found in the literature (see a.o. Lettau and Ludvigson, 2001; Rapach and Wohar, 2006; Goyal and Welch, 2008)