Research articles for the 2019-09-01

Culture and the disposition effect
Bastian Breitmayer,Tim Hasso,Matthias Pelster

We study the relationship between national culture and the disposition effect by investigating international differences in the degree of investors' disposition effect. We utilize brokerage data of 387,993 traders from 83 countries and find great variation in the degree of the disposition effect across the world. We find that the cultural dimensions of long-term orientation and indulgence help to explain why certain nationalities are more prone to the disposition effect. We also find support on an international level for the role of age and gender in explaining the disposition effect.

Dynamic supply adjustment and banking under uncertainty in an Emission Trading Scheme: the Market Stability Reserve
Kollenberg, Sascha,Taschini, Luca
We study the impact of a supply management mechanism (SMM) similar to the Market Stability Reserve proposed in 2015 which preserve the overall emissions cap and we comment on the recent cap-changing amendments. We provide an analytical description of the conditions under which an SMM alters the emissions abatement paths, affecting the expected length of the banking period and its variability. While abatement strategies of risk neutral firms solely depend on the former, for riskaverse firms changes in the latter would lead to higher risk premia, accelerated depletion of the bank and, consequently, further reduction of abatement and allowance prices. Cancellation of part of the reserve could partially outweigh the effect on risk premia sustaining allowance prices.

Growth Dynamics of Value and Cost Trade-off in Competitive Temporal Networks
Sheida Hasani,Razieh Masoomi,Jamshid Ardalankia,Mohammadbashir Sedighi,Hamid Jafari

The question is: What does happen to the real-world networks which cause them not to grow permanently? The idea here is that real-world networks have to pay the cost of growth. We investigate the growth and trade-off between value and cost in the networks with cost and preferential attachment together. Since the preferential attachment in BA model does not consider any stop against the infinite growth of networks, we introduce a modified version of preferential attachment of BA model. This idea makes sense because the growth of real networks may be finite. In the present study, by combining preferential attachment in the science of temporal networks (interval graphs), and, the first-order differential equations of value and cost of making links, the future equilibrium of an evolving network is illustrated. During the process of achieving a winning position, the variables against growth such as the competition cost, besides with the internally structural cost may emerge. In the end, by applying this modified model, we found the circumstances which a trade-off between value and cost emerge.

Predicting Consumer Default: A Deep Learning Approach
Stefania Albanesi,Domonkos F. Vamossy

We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation.

Sampling Distributions of Optimal Portfolio Weights and Characteristics in Low and Large Dimensions
Taras Bodnar,Holger Dette,Nestor Parolya,Erik Thorsén

Optimal portfolio selection problems are determined by the (unknown) parameters of the data generating process. If an investor want to realise the position suggested by the optimal portfolios he/she needs to estimate the unknown parameters and to account the parameter uncertainty into the decision process. Most often, the parameters of interest are the population mean vector and the population covariance matrix of the asset return distribution. In this paper we characterise the exact sampling distribution of the estimated optimal portfolio weights and their characteristics by deriving their sampling distribution which is present in terms of a stochastic representation. This approach possesses several advantages, like (i) it determines the sampling distribution of the estimated optimal portfolio weights by expressions which could be used to draw samples from this distribution efficiently; (ii) the application of the derived stochastic representation provides an easy way to obtain the asymptotic approximation of the sampling distribution. The later property is used to show that the high-dimensional asymptotic distribution of optimal portfolio weights is a multivariate normal and to determine its parameters. Moreover, a consistent estimator of optimal portfolio weights and their characteristics is derived under the high-dimensional settings. Via an extensive simulation study, we investigate the finite-sample performance of the derived asymptotic approximation and study its robustness to the violation of the model assumptions used in the derivation of the theoretical results.