Current Account Adjustment: Some New Theory and Evidence

This paper aims to provide a theory of current account adjustment that generalizes the textbook version of the intertemporal approach to current account and places domestic labor market institutions at the center stage. In general, in response to a shock, an economy adjusts through a combination of a change in the composition of goods trade (i.e., intra-temporal trade channel) and a change in the current account (i.e., intertemporal trade channel). The more rigid the labor market, the slower the speed of adjustment of the current account towards its long-run equilibrium. Three pieces of evidence are provided that are consistent with the theory.

A Solution to Two Paradoxes of International Capital Flows

International capital flows from rich to poor countries can be regarded as either too small (the Lucas paradox in a one-sector model) or too large (when compared with the logic of factor price equalization in a two-sector model). To resolve the paradoxes, we introduce a non-neo-classical model which features financial contracts and firm heterogeneity. In our model, free trade in goods does not imply equal returns to capital across countries. In addition, rich patterns of gross capital flows emerge as a function of financial and property rights institutions. A poor country with an inefficient financial system may simultaneously experience an outflow of financial capital but an inflow of FDI, resulting in a small net flow. In comparison, a country with a low capital-to-labor ratio but a high risk of expropriation may experience outflow of financial capital without compensating inflow of FDI.

Domestic Institutions and the Bypass Effect of Financial Globalization

This paper proposes a simple model to study the relationship between domestic institutions - financial system, corporate governance, and property rights protection - and patterns of international capital flows. It studies conditions under which financial globalization can be a substitute for reforms of domestic financial system. Inefficient financial system and poor corporate governance in a country may be completely bypassed by two-way capital flows in which domestic savings leave the country in the form of financial capital outflows but domestic investment takes place via inward foreign direct investment. While financial globalization always improves the welfare of a developed country with a good financial system, its effect is ambiguous for a developing country with an inefficient financial sector/poor corporate governance. However, the net effect for a developing country is more likely to be positive, the stronger its property rights protection. This is consistent with the observation that developed countries are often more enthusiastic about capital account liberalization around the world than many developing countries. A noteworthy feature of this theory is that financial and property rights institutions can have different effects on capital flows.

On the Connections between Intertemporal and Intra-temporal Trades

This paper develops a new theory of international economics by introducing Heckscher-Ohlin features of intra-temporal trade into an intertemporal trade approach of current account. To do so, we consider a dynamic general equilibrium model with tradable sectors of different factor intensities, which allows for substitution between intertemporal trade (current account adjustment) and intra-temporal trade (goods trade). An economy's response to a shock generally involves a combination of a change in the composition of goods trade and a change in the current account. Flexible factor markets reduce the need for the current account to adjust. On the other hand, the more rigid the factor markets, the larger the size of current account adjustment relative to the volume of goods trade, and the slower the speed of adjustment of the current account towards its long-run equilibrium. We present empirical evidence consistent with the theory.

When Is Quality of Financial System a Source of Comparative Advantage?

Does finance follow the real economy, or the other way around? This paper unites the two competing schools of thought in a general equilibrium framework. Our key result is that there are threshold effects defined by a set of deep institutional parameters (cost of financial intermediation, quality of corporate governance, and level of property rights protection) which can be used to separate economies of high-quality institutions from those of low-quality institutions. On one hand, for economies with high-quality institutions, the view that finance follows the real economy is essentially correct. Equilibrium output and prices are determined by factor endowment. Further improvement in the institutions does not affect patterns of output. On the other hand, for economies with low-quality institutions, the view that finance is a key driver of the real economy is essentially correct. Not only is finance a source of comparative advantage, but an increase in capital endowment has no effect on outputs and prices. Our model extends a standard one-sector, partial equilibrium model of corporate finance to a multi-sector, general equilibrium analysis. Surprisingly, but consistent with data, we show that the size of financial markets (relative to GDP) does not change monotonically with either the quality of institutions or with the factor endowment. Free trade may reduce the aggregate income of an economy with low-quality institutions. Financial capital tends to flow from economies with low-quality institutions to those with high-quality institutions.

Motif-aware temporal GCN for fraud detection in signed cryptocurrency trust networks

Graph convolutional networks (GCNs) is a class of artificial neural networks for processing data that can be represented as graphs. Since financial transactions can naturally be constructed as graphs, GCNs are widely applied in the financial industry, especially for financial fraud detection. In this paper, we focus on fraud detection on cryptocurrency truct networks. In the literature, most works focus on static networks. Whereas in this study, we consider the evolving nature of cryptocurrency networks, and use local structural as well as the balance theory to guide the training process. More specifically, we compute motif matrices to capture the local topological information, then use them in the GCN aggregation process. The generated embedding at each snapshot is a weighted average of embeddings within a time window, where the weights are learnable parameters. Since the trust networks is signed on each edge, balance theory is used to guide the training process. Experimental results on bitcoin-alpha and bitcoin-otc datasets show that the proposed model outperforms those in the literature

Semantic-Aware Frame-Event Fusion based Pattern Recognition via Large Vision-Language Models

Pattern recognition through the fusion of RGB frames and Event streams has emerged as a novel research area in recent years. Current methods typically employ backbone networks to individually extract the features of RGB frames and event streams, and subsequently fuse these features for pattern recognition. However, we posit that these methods may suffer from key issues like sematic gaps and small-scale backbone networks. In this study, we introduce a novel pattern recognition framework that consolidates the semantic labels, RGB frames, and event streams, leveraging pre-trained large-scale vision-language models. Specifically, given the input RGB frames, event streams, and all the predefined semantic labels, we employ a pre-trained large-scale vision model (CLIP vision encoder) to extract the RGB and event features. To handle the semantic labels, we initially convert them into language descriptions through prompt engineering, and then obtain the semantic features using the pre-trained large-scale language model (CLIP text encoder). Subsequently, we integrate the RGB/Event features and semantic features using multimodal Transformer networks. The resulting frame and event tokens are further amplified using self-attention layers. Concurrently, we propose to enhance the interactions between text tokens and RGB/Event tokens via cross-attention. Finally, we consolidate all three modalities using self-attention and feed-forward layers for recognition. Comprehensive experiments on the HARDVS and PokerEvent datasets fully substantiate the efficacy of our proposed SAFE model. The source code will be made available at https://github.com/Event-AHU/SAFE_LargeVLM.Comment: In Peer Revie

The impact of high temperature on mechanical properties and behaviors of sandstone

The impact of high temperature environments on the physical and mechanical properties of rocks is a significant factor to consider. The investigation into the impact of elevated temperatures on the physical and mechanical characteristics of rocks holds great importance in the advancement and exploitation of deep-seated mineral reserves, as well as in ensuring the safety and stability of subterranean engineering projects. This study utilizes the state-of-the-art GCTS Mechanical Loading Test System to conduct uniaxial and triaxial compression tests on sandstone after thermal treatment from 25°C to 650°C. In addition, XRD, SEM and nuclear magnetic resonance experiments were carried out on the sandstone after thermal treatment. The aim of the experiments is to provide a quantitative characterization of mechanical properties and behaviors of the rock samples. The results show that the mass, density, and wave velocity of sandstone decrease with increasing temperature, while volume and porosity increase. The mass, volume, and rate of density change of sandstone exhibit a significant increase when subjected to temperatures above 500°C. The uniaxial compressive strength and elastic modulus exhibit an initial increase followed by a subsequent decrease as the temperature rises, with 300°C serving as the critical turning point. The axial peak strain and Poisson’s ratio increase with increasing temperature. The cohesion decreases with increasing temperature, while the internal friction angle increases. Additionally, it is observed that the rate of change for both properties exhibits an increase beyond the temperature threshold of 400°C

An improved particle swarm optimization combined with double-chaos search

Particle swarm optimization (PSO) has been successfully applied to various complex optimization problems due to its simplicity and efficiency. However, the update strategy of the standard PSO algorithm is to learn from the global best particle, making it difficult to maintain diversity in the population and prone to premature convergence due to being trapped in local optima. Chaos search mechanism is an optimization technique based on chaotic dynamics, which utilizes the randomness and nonlinearity of a chaotic system for global search and can escape from local optima. To overcome the limitations of PSO, an improved particle swarm optimization combined with double-chaos search (DCS-PSO) is proposed in this paper. In DCS-PSO, we first introduce double-chaos search mechanism to narrow the search space, which enables PSO to focus on the neighborhood of the optimal solution and reduces the probability that the swarm gets trapped into a local optimum. Second, to enhance the population diversity, the logistic map is employed to perform a global search in the narrowed search space and the best solution found by both the logistic and population search guides the population to converge. Experimental results show that DCS-PSO can effectively narrow the search space and has better convergence accuracy and speed in most cases