Chinese Equity Market Pricing and Loan Sales Discount in US Banking

Abstract In the recent decades, the Fama–French three-factor (1992, 1993, 1996) and five-factor (2015) models become the most widely used asset pricing models in the world. The U.S. (i.e., developed financial market) country-specific 2 additional factors in the 5-factor model, RMW and CMA or profitability and investment premium, empirically cannot further capture the return variation of the classic three-factor/characteristic in China’s stock market (i.e., developing financial markets). In China, based on our result, therefore, the classic three-factor outperforms the five-factor model. We do not presume that firms in different countries share the same features. Following Liu, Stambaugh, and Yuan (2019), we replaced the price-to-book ratio (PB) with the earnings-price ratio (EP). By using Shanghai and Shenzhen exchange stocks, we suggested that the explanatory uncertainty of HML only exists in the five-factor model. In the Fama MacBeth regression, the SMB and HML are significant factors in the three-factor model, explaining the return variation in China. Surprisingly, though the size effect is impressively persistent in both models, the ratio effect has limited explanatory power. JEL Codes: C5, G1, G2 JEL Keywords: Fama-French Factors; Asset Pricing; Chinese Stock Market Abstract We analyze Federal Deposit Insurance Corporation (FDIC) managed 4273 loan sales transactions between 1994 and 2019 that include two major financial crises of the modern times: the dot.com bubble of 2001, and the global financial crisis of 2008 and 2009. We find that loan sales discounts, asset quality, industry classifications, compositions and buyers interest vary significantly during financial recessions and non-recessionary periods. Industry classifications affect loan sales discount rates. Loan sales discounts are inversely related with asset quality. While the non-performing and lower quality loans are sold at higher discounts, the sub-performing and the performing loans are sold at lower discount. Our evidences backup Demsetz’ s (2000) hypotheses that banks with limited branches and high reputation are more likely participate in the secondary market in order to erode the loan origination problem and diversify the current loan portfolio. JEL Codes: G210, G280 JEL Keywords: Banks; Depository Institutions; Financial Institutions and Services: Government Policy and Regulation; Bailout, FDIC

Applicability of thermal energy storage in future district heating system - Design methodologies and performance evaluations

District heating (DH) enables efficient and economical utilization of energy resources to satisfy the heat and hot water demands in buildings and is, thereby, well-established in Northern European countries. To achieve the future renewable energy system, the current DH systems are proved to undergo transitions towards the future DH systems, with major characteristics including renewable-based heat sources, low temperature networks, lower heating demands and smart controls. An important step is the coordination of heating and electricity sectors to achieve synergies and optimal solutions for the overall energy system, which is also known as the smart energy systems. Such goal could be achieved in a cost-effective manner by the flexibilities added from short-term thermal energy storage (TES) technologies. Despite the importance of TES has been demonstrated in previous studies, giving drastic changes compared to the current systems, the practical applicability of TES in the future DH systems remains unknown. The proposed benefits of TES might deviate from expectation considering the future characteristics, such as the low storage temperature levels and short space-heating period. Furthermore, the current studies about the TES applications have mostly focused on specific case studies. The findings are of limited applicability because they cannot be easily generalized and extrapolated to other future conditions. To explore the practical challenges and optimal applications of short-term TES units in the future, a systematic design framework that considers the diverse factors from top-level targets to bottom-level implementations is developed in this study. The top-level theoretical analysis method is developed to identify the load shifting potentials and associated storage capacities for the whole energy system, by comparing and matching energy supply and demand profiles. Compared to current bottom-up detailed system models, the proposed method requires only the energy profiles, which has resulted in much shorter analysis time. The method is further validated by complex system models, and because a good agreement has been achieved, it can be applied in various scenarios to efficiently pre-study the storage potentials. Then, the design of the practical TES capacity is derived from the theoretical result by considering performance indicators during realistic operations, such as power-to-heat conversion efficiency and heat loss efficiency.On bottom-level implementations, four typical short-term TES technologies were investigated including central water tank (CWT), district heating network inertia (DHNI), domestic hot water tank (DHWT), and building thermal mass (BTM). For this purpose, an integrated bottom-level model to simulate the operation dynamics of the district heating systems and to optimize the use of the TES units is developed. Techno-economic analysis and comparisons of TES technologies were performed on a variety of scenarios, which are representatives of the main characteristics of the current middle-temperature district heating system and future low-temperature district heating system. The changes in the source side, transportation networks and end-use building demands are considered. As a result, a performance map of the TES technologies indicating the strong links between the system characteristics and optimal TES applications has been identified. Based on that, the optimal combinations of TES technologies were proposed for a LTDH system. Consequently, combining this with top-level methods, the overall potentials and roles of short-term TES were identified by a systematic design framework

Mechanical Design of a Cartesian Manipulator for Transducer Placement in Nondestructive Evaluation Experiments

Nondestructive evaluation methods often require high accuracy of transducer placement and repetition of multiple experimental steps. This study proposes the design of a Cartesian manipulator robot to accurately move and place the transducers on the test specimen. The electrical and software systems are also developed to allow the automation of the experiment by creating a program. The robot is capable of linear speeds of 250 mm/s and the ranges of motion from 50 to 1000 mm with 0.05-mm position accuracy. The allowable workspace is 900 x 930 x 18 mm in dimensions (length x width x height) and the maximum load capacity is 25 kg. The software allows for 13 commands that can be combined freely in a variety of experiment settings. The manipulator will be used in the nondestructive evaluation of composite aircraft and aerospace structural components in Professor Mal’s laboratory

Transition pathways for future district heating and cooling systems with thermal energy storage

Buildings’ heating and cooling account for more than 20% of the final energy use within the European countries and are dominated by non-renewable resources. Future district energy systems should enable efficient, fossil-free, and economical energy supply at operating temperatures that end users can directly utilize. This can be achieved by lowering the system temperatures and boosting them on the demand side to increase the overall system efficiency. Ultralow-temperature district heating (ULTDH) and bidirectional fifth-generation district heating and cooling (5GDHC) systems are the solutions. However, the transition of district heating and cooling (DHC) systems from current high-temperature configurations to the future solutions is subject to several uncertainties and challenges, such as energy prices, investment costs, thermal energy storage (TES) distribution, and demand profiles. The variations in these uncertainties were not considered in previous studies. Most of the earlier studies only discussed current perspectives, leaving the future applicability of the DHC system unknown.Hence, a generalized methodological framework combining energy system optimization with stochastic simulations, uncertainty analysis, and sensitivity assessment is developed in this study to investigate the effects of these uncertainties. Based on a variety of stochastic cases, the index named cost-saving probability (CSP) is utilized to reflect the potential of being economic attractive when comparing the energy systems. The preferred future conditions for different DHC systems are summarized in the roadmaps via proposed key performance indicators (KPIs), indicating a future promising area for DHC design. Meanwhile, the applications and roles of TES in future DHC systems were investigated. Furthermore, combined with the geographical information system-based methodologies and data sources, the proposed KPIs for the entire European building stock were calculated at the hectare level to identify the potential areas of 5GDHC. The results reveal considerable differences between the systems as different design and operation objectives on least cost and imported electricity are set. The most sensitive factors of the CSP are area demand density, overlapping heating and cooling demand, and linear demand density for the transition to ULTDHC, 5GDHC, and individual systems, respectively. The roadmap also shows the hindering factors for different transitions, as well as the impact of the objective on imported electricity. Besides, the sensitivity analysis results reveal TES’s limited role in integrating variable renewable energy (RE) in high-efficiency DHC systems. In addition, less than 0.1% of the current European building stock has sufficient overlapping heating and cooling demands to efficiently implement 5GDHC. These potential areas are primarily found in city centres involving cooling demands from commercial and industrial processes. While a better energy performance of buildings and warmer climate in the future may decrease the heating and increase the cooling demand, the overlapping part is only slightly increased by around 4%, leading to limited additional application potentials of 5GDHC

Polyhedral Gauss Sums, and polytopes with symmetry

We define certain natural finite sums of $n$'th roots of unity, called $G_P(n)$, that are associated to each convex integer polytope $P$, and which generalize the classical $1$-dimensional Gauss sum $G(n)$ defined over $\mathbb Z/ {n \mathbb Z}$, to higher dimensional abelian groups and integer polytopes. We consider the finite Weyl group $\mathcal{W}$, generated by the reflections with respect to the coordinate hyperplanes, as well as all permutations of the coordinates; further, we let $\mathcal G$ be the group generated by $\mathcal{W}$ as well as all integer translations in $\mathbb Z^d$. We prove that if $P$ multi-tiles $\mathbb R^d$ under the action of $\mathcal G$, then we have the closed form $G_P(n) = \text{vol}(P) G(n)^d$. Conversely, we also prove that if $P$ is a lattice tetrahedron in $\mathbb R^3$, of volume $1/6$, such that $G_P(n) = \text{vol}(P) G(n)^d$, for $n \in \{ 1,2,3,4 \}$, then there is an element $g$ in $\mathcal G$ such that $g(P)$ is the fundamental tetrahedron with vertices $(0,0,0)$, $(1, 0, 0)$, $(1,1,0)$, $(1,1,1)$.Comment: 18 pages, 2 figure

CausE: Towards Causal Knowledge Graph Embedding

Knowledge graph embedding (KGE) focuses on representing the entities and relations of a knowledge graph (KG) into the continuous vector spaces, which can be employed to predict the missing triples to achieve knowledge graph completion (KGC). However, KGE models often only briefly learn structural correlations of triple data and embeddings would be misled by the trivial patterns and noisy links in real-world KGs. To address this issue, we build the new paradigm of KGE in the context of causality and embedding disentanglement. We further propose a Causality-enhanced knowledge graph Embedding (CausE) framework. CausE employs causal intervention to estimate the causal effect of the confounder embeddings and design new training objectives to make stable predictions. Experimental results demonstrate that CausE could outperform the baseline models and achieve state-of-the-art KGC performance. We release our code in https://github.com/zjukg/CausE.Comment: Accepted by CCKS 2023 as a research pape