An Empirical Analysis of the Extreme Cherry Picking Behavior of Consumers in the Frequently Purchased Goods Market

Extreme cherry pickers are customers who seek price deals and excessively avail themselves of deep discount offers, which generates negative profits for retailers. This study uses market transaction and primary consumer survey data to provide insights into the determinants, prevalence, and profit impacts of such behavior in the frequently purchased goods market. We find that the extreme cherry picking segment is small (about 2% of all shoppers), but its relative value varies across stores, and consumers manifest this behavior only in secondary stores. An inverse U-shaped relationship marks consumers’ opportunity costs for cross-store price search and likelihood of extreme cherry picking behavior. Finally, we also find that a loss leader promotional strategy adds to retailers’ bottom lines, despite the pure loss generated by extreme cherry pickers.     

On the severity of ruin in a Markov-modulated risk model

We consider a Markov-modulated risk model in which the claim inter-arrivals, amounts and premiums are influenced by an external Markovian environment process. A system of Laplace transforms of the probabilities of the severity of ruin, given the initial environment state, is established from a system of integro-differential equations derived by Snoussi [The severity of ruin in Markov-modulated risk models Schweiz Aktuarver. Mitt., 2002, 1, 31–43]. In the two-state model, explicit formulas for probabilities of the severity of ruin are derived, when the initial reserve is zero or when both claim amount distributions are from the rational family. Numerical illustrations are also given.     

Loss given default models incorporating macroeconomic variables for credit cards

Based on UK data for major retail credit cards, we build several models of Loss Given Default based on account level data, including Tobit, a decision tree model, a Beta and fractional logit transformation. We find that Ordinary Least Squares models with macroeconomic variables perform best for forecasting Loss Given Default at the account and portfolio levels on independent hold-out data sets. The inclusion of macroeconomic conditions in the model is important, since it provides a means to model Loss Given Default in downturn conditions, as required by Basel II, and enables stress testing. We find that bank interest rates and the unemployment level significantly affect LGD.     

Evaluation periods and asset prices: Myopic loss aversion at the financial marketplace

Examining myopic loss aversion (MLA [Benartzi, S., Thaler, R., 1995. Myopic loss aversion and the equity premium puzzle. Quarterly Journal of Economics 110, 73–92]) in real financial markets has several merits: in repeated situations investors may learn from each other, aggregate market prices may eliminate individual violations of expected utility, and individuals may decide differently in real situations than in laboratories. We utilize a special feature at the Tel Aviv stock exchange (TASE): occasional shifts of securities from daily to weekly trading. If investors’ decisions are influenced by trading frequency manipulation, then returns should be predictably affected. MLA results in a negative relation between risk aversion and the length of the evaluation period. Thus, the longer the evaluation period is, the lower the expected return is. This intuition also suggests reduced sensitivity to economic events in longer evaluation periods. We find strong support for MLA in the marketplace when testing expected return, as well as return sensitivity.     

A hybrid clustering and classification approach for predicting crash injury severity on rural roads

As a threat for transportation system, traffic crashes have a wide range of social consequences for governments. Traffic crashes are increasing in developing countries and Iran as a developing country is not immune from this risk. There are several researches in the literature to predict traffic crash severity based on artificial neural networks (ANNs), support vector machines and decision trees. This paper attempts to investigate the crash injury severity of rural roads by using a hybrid clustering and classification approach to compare the performance of classification algorithms before and after applying the clustering. In this paper, a novel rule-based genetic algorithm (GA) is proposed to predict crash injury severity, which is evaluated by performance criteria in comparison with classification algorithms like ANN. The results obtained from analysis of 13,673 crashes (5600 property damage, 778 fatal crashes, 4690 slight injuries and 2605 severe injuries) on rural roads in Tehran Province of Iran during 2011–2013 revealed that the proposed GA method outperforms other classification algorithms based on classification metrics like precision (86%), recall (88%) and accuracy (87%). Moreover, the proposed GA method has the highest level of interpretation, is easy to understand and provides feedback to analysts.     

Inter-temporal choices with temporal reference dependence

I develop an intertemporal choice model for rational deviators whose preferences depend not only on their actual consumption but also on comparison to their beliefs about the optimal consumption. The standard decision maker is loss averse with respect to this belief-dependent reference point. When psychologically weighted loss aversion is low, a decision maker deviates from the standard intertemporal choice behavior and over-consumption, as well as the alternative possibility of under-consumption can be rationalized. When the decision maker has time-varying degrees of loss aversion, he re-optimizes the consumption plan through adjusted beliefs as subsequent selves realize that past decision for the present period is no longer optimal. In the dynamic model, I solve for consistent intertemporal optimization rules by which a dynamic deviator should meet rational intertemporal consistency at each point in time. Finally, I demonstrate that the dynamic reference dependent model can solve a puzzling feature in lifecycle consumption data.     

金融稳定是否应纳入中国货币政策目标——基于混频IS-Phillips模型的实证分析

文章首先使用混频动态因子模型（MF-DFM），构建中国首个混频金融稳定指数（MF-FSI），接着把MF-FSI作为金融稳定的代理变量，使用文章新构建的混频IS-Phillips模型，比较分析纳入与不纳入金融稳定的中国货币政策损失函数差异，最后对货币政策目标选择和时效选择进行了敏感性分析，实证分析表明：（1）中国混频金融稳定指数是金融稳定的一个实时性有效测度指标；（2）中国货币政策目标应纳入金融稳定，以减少货币政策福利损失；（3）无论中国货币政策目标偏好和预期偏好怎样变化，央行都应给予金融稳定固定且不可忽略的关注度，但物价稳定和经济增长仍为主要货币政策目标。     

A fine-scale spatial analytics of the assessment and mapping of buildings and population at different risk levels of urban flood

Nowadays, urban flooding is becoming a severe issue in most of the developing and developed countries. The growth of the urbanization rate is also increasing, and the United Nations (UN) projected that 68 % of the world’s population would live in urban areas by 2050. People tend to migrate from rural to urban areas, which expose them more vulnerable to urban floods. The flood-related damages and deaths are increasing every year globally. Using the Birmingham city, Alabama (AL), USA as the study area, the objective of this research is to assess potential damage risks due to flood exposure of buildings and population in an urban area. Different social and environmental factors influence urban floods in an urban area. This paper considered elevation, slope, flow accumulation, land-use, soil types, and distance from the river as significant influential factors to urban flooding. The flood risk model hence can be developed by using an integrated GIS and cartrographic approach, in which we assessed and assigned weights to these factors and formed a GIS risk assessment model, which shows the level of flood risks in the floodplain areas of Birmingham and quantifies and maps both commercial buildings, home buildings, and populations’ exposed to flooding risks. This study found that the Valley Creek area is the highest flood risk zone in Birmingham, and about 48.85 percent of Valley Creek’s floodplain area will face very high flood risk. The findings further reveal that total number of 5602 people are living in high and very high flood risk zones in Birmingham that approximates 44.04 % of the total population in this floodplain area. The physical vulnerability is also assessed, and findings suggest that the Valley Creek zone has the highest percentage of residential (i.e., 56.14 %) and commercial (i.e., 75.34 %) buildings located in very high flood risk areas. Our study providing a GIS risk assessment approach to locating and mapping the areas, buildings, and populations from the most to the least at risks with a fine spatical scale for urban flood risk management. The numbers of vulnerable buildings and populations within each risk category are quantified and their distributions are mapped. Therefore, revealing population’s and buildings’ risks and their geographic information, this flood risk assessment can help local governments and communities prepare better to take actions against future urban flood events in Birmingham, and this integrated GIS and cartographic analysis for fine flooding assessments can be applied to other urban areas for flood mitigation and risk management.     

Loss aversion and market crashes

This study proposes a rational expectation equilibrium model of stock market crashes with information asymmetry and loss averse speculators. We obtain a state-dependent linear optimal trading strategy, which makes the equilibrium price tractable. The model predicts nonlinear market depth and the result that small shocks to fundamentals (e.g., supply or informational shocks) can cause abrupt price movements. We demonstrate that short-sale constraints intensify asset price collapses relative to upward movements. The model also generates contagion between uncorrelated assets. These results are consistent with the main puzzling features observed during market crashes, namely abrupt and asymmetric price movements that are not driven by major news events but coupled with a spillover effect between unrelated markets.