Search Results (41)

In Latin America, over 50% of the population has relied on loans from family members or friends, reflecting the importance of trust-based networks in response to financial exclusion. This study examines how distrust in the formal financial system influences the use of informal borrowing. Using data from 17 countries for the years 2014, 2017, and 2021, and applying a fixed-effects logistic regression model by country and time, we confirm that rising distrust significantly increases the likelihood of turning to loans from personal networks. This relationship intensifies in times of crisis. Beyond this, we find that macroeconomic variables such as GDP per capita and unemployment also significantly affect informal borrowing behavior. This research contributes to the literature by integrating institutional, economic, and social variables, highlighting the role of interpersonal trust as a form of social capital. It also advances the field of personal finance by revealing an everyday strategy of financial resilience. Finally, this study offers relevant implications for public policy, advocating for a more realistic and context-sensitive approach to financial inclusion, especially in regions where credit constraints in the formal sector have pushed households to seek more accessible and flexible alternatives. Full article

This study considers a simple automotive supply chain that includes an automobile manufacturer with demand information and financial advantages and a financially constrained automobile lessor. The manufacturer can decide whether to provide financing support to the lessor, as follows: when the manufacturer offers trade credit contracts, this is seller financing, and the lessor does not need to borrow from banks; if the manufacturer only provides wholesale price contracts, then the lessor must rely on bank financing. By constructing a signaling game, we delve into the interactive relationship between the manufacturer’s contract decisions and the lessor’s optimal financing strategies under both symmetric and asymmetric demand information scenarios. The findings show that, under symmetric information, the decisions of the manufacturer and the lessor are primarily driven by demand price sensitivity, with no significant financing conflicts between the two parties. However, under asymmetric information, their decisions are also closely related to the degree of demand fluctuation, leading to the emergence of financing conflicts. The innovation of this study lies in its incorporation of demand information asymmetry into the analytical framework governing manufacturers’ contract decisions and lessors’ financing strategies. This provides valuable theoretical insights and practical guidance for automotive supply chains operating under financial constraints. Full article

We study an optimal consumption, leisure, and investment problem over a finite horizon in a continuous-time financial market with partial borrowing constraints. The agent derives utility from consumption and leisure, with preferences represented by a Cobb–Douglas utility function. The agent allocates time between work and leisure, earning wage income based on working hours. A key feature of our model is a partial borrowing constraint that limits the agent’s debt capacity to a fraction of the present value of their maximum future labor income. We employ the dual-martingale approach to derive the optimal consumption, leisure, and investment strategies. The problem reduces to solving a variational inequality with a free boundary, which we analyze using analytical and numerical methods. We provide an integral equation representation of the free boundary and solve it numerically via a recursive integration method. Our results highlight the impact of the borrowing constraint on the agent’s optimal decisions and the interplay between labor supply, consumption, and portfolio choice. Full article

This study investigates the role of short sales in mitigating post-shock anomalies in stock returns within the context of China’s evolving short-sales regulations. Utilizing a unique dataset of daily short-sale volumes, this research examines how short sellers influence stock price behavior following significant price shocks. The findings reveal that short sellers act as informed arbitragers, reducing post-shock anomalies, particularly in news-driven events, and supporting Diamond and Verrecchia’s hypothesis that short-sale constraints slow price adjustments to information. This study fills a critical gap in the literature, offering insights into price efficiency and implications for regulators and investors. By highlighting the unintended consequences of restrictive short-sale policies, this paper recommends reforms to reduce borrowing costs, enhance lending programs, and promote effective short-selling practices. These results contribute to the broader understanding of market dynamics, particularly in emerging markets with tight short-sale restrictions like China. Full article

In this paper, we address the utility maximization problem of an infinitely lived agent who has the option to increase their income. The agent can increase their income at any time, but doing so incurs a wealth cost proportional to the amount of the increase. To prevent the agent from infinitely increasing their income and borrowing against future income, we additionally consider a non-negative wealth constraint that prohibits borrowing based on future income. This utility maximization problem is a mixture of stochastic control, where the agent chooses consumption and investment, and singular control, where the agent chooses a non-decreasing income process. To solve this non-trivial and challenging problem, we derive the Hamilton–Jacobi–Bellman (HJB) equation with a gradient constraint using the dynamic programming principle (DPP). Then, using the guess-and-verify method and a linearization technique, we obtain a closed-form solution to the HJB equation and, based on this, find the optimal strategy. Full article

Public debt is determined by borrowings undertaken by a government to finance its short- or long-term financial needs and to ensure that macroeconomic objectives are met within budgetary constraints. In Romania, public debt has been on an upward trajectory, a trend that has been further exacerbated in recent years by the COVID-19 pandemic. Additionally, a significant non-economic event influencing Romania’s public debt is the war in Ukraine. To analyze this, a stochastic debt sustainability analysis was conducted, incorporating the unique characteristics of Romania’s emerging market into the research methodology. The projections focused on achieving satisfactory results by following two lines of research. The first direction involved developing four scenarios to assess the risks presented by macroeconomic shocks. Particular emphasis was placed on an unusual negative shock, specifically the war in Ukraine, with forecasts indicating that the debt-to-GDP ratio could reach 102% by 2026. However, if policymakers implement discretionary measures, this level could be contained below 88%. The second direction of research aimed to establish the maximum safe limit of public debt for Romania, which was determined to be 70%. This threshold would allow the emerging economy to manage a reasonable level of risk without requiring excessive fiscal efforts to maintain long-term stability. Full article

With the rapid evolution of the rural digital economy, analyzing the impact of digital literacy level on farm households’ formal borrowing is crucial for easing credit constraints and fostering rural economic growth. Leveraging the data from the 2020 China Family Panel Studies (CFPSs) and applying binary probit models and the Karlson–Holm–Breen (KHB) method, this study delineates the positive correlation between the digital literacy level and increased formal borrowing among farm households. The findings, which were robust against endogeneity and robustness tests, underscore the role of digital literacy level in augmenting farmers’ earnings and social networks, with a notably stronger mediation by earnings. The effects are particularly significant for middle-aged and older, less educated males in the central and western regions, in contrast with younger, highly educated females in the east. This research advocates for enhancing rural digital infrastructure and education, alongside financial system reforms, to advance rural financial development and support sustainable rural revitalization. Full article

With the rapid advancement of digital technology, the banking industry has embarked on a journey of digital transformation. While existing literature primarily examines how these changes impact the banks themselves, our study focuses on a relatively unexplored aspect: the direct influence of bank digital transformation on the performance and behavior of borrowing enterprises. The research objective of this study is to explore the influence of bank digital transformation on the innovation performance of borrowing enterprises and the underlying mechanisms. Leveraging data from Chinese listed companies and commercial banks, we find a positive effect of bank digital transformation on enterprise innovation output as measured by firms’ patent applications. The findings remain robust across alternative model specifications, controls for regional digital economy development levels, and bank financial performance, as well as alternative measures of bank digital transformation. Mechanism tests show that bank digital transformation contributes to corporate innovation by alleviating corporate financial constraints and improving corporate governance. Further research demonstrates that bank digital transformation also helps promote corporate innovation efficiency as measured by the proportion of patent output to total R&D input and corporate innovation output as measured by firms’ invention patent applications and patent grants. Additionally, borrowing firms’ own digital transformations may substitute for bank digital transformation in their effect on innovations. Full article

This paper presents an analysis of syntactic collocations that include the constraints on an adjective phrase (AP) and adverbs or final particles (X) in the ɬwa:n⁵ + AP + X construction, as well as its pragmatic features and grammatical functions in the Kam language. ɬwa:n⁵ functions as an exclamative marker (EM) that conveys the expressive meaning of the speaker’s attitude. The primary lexical meaning of ɬwa:n⁵ is ‘to calculate’ or ‘to regard as’, and the word is borrowed from Chinese (算 suàn, ‘to count’, ‘to calculate’, ‘to regard as’). The role of ɬwa:n⁵ in the construction ɬwa:n⁵ + AP + X will mainly be discussed in this paper. In addition, the adjectives that can collocate with ɬwa:n⁵ are subject to a gradeability restriction, that is, only gradable adjectives can collocate with ɬwa:n⁵. In general, the ɬwa:n⁵ construction is used to express that the speaker expected an existing situation with a gradable property; in reality, the degree follows a particular scalar and surpasses the expectation. Full article

Determinants and levels of working capital financing (WCF) in the manufacturing sector have been empirically proven to impact firm profitability across emerging as well as developed nations. With time, firms adjust toward financing their working capital requirement (WCR), although the speed of adjustment, financing constraints, and bargaining power are subject to variations. In this study, we estimate the effect of bank credit and firm foreign currency borrowing on working capital financing with three distinct models for manufacturing firms in India. We examine the relationship between short-term foreign currency borrowings and WCF. Further, we investigate if the internal capital market affects WCF in the form of business group affiliation; lastly, we assess the impact of bank dependency and financial distress on WCF. We conclude that the debt–equity ratio becomes relevant, whereas firm characteristics such as age, size, and asset tangibility become irrelevant. Our original contribution to the literature is the finding that even smaller emerging market firms with well-managed, low debt exposure have improved access to WCF. Our results support that financial distress negatively impacts WCF but deviates from macroeconomic fundamentals, such as the GDP growth rate. This indicates deterioration in the health of Indian manufacturing, as a capital-intensive sector. Bank dependency remains significant, wherein smaller firms and those without a dividend pay-out continue to have longer cash conversion cycles and less efficient WCR. As a unique finding, we note foreign currency borrowings significantly contribute to WCF in the case of less developed credit markets in emerging economies such as India. Full article

In this paper, we investigate the influential factors of Federal Housing Administration (FHA) mortgage loans, focusing our research interest on peer-to-peer (P2P) lending, the most successful FinTech lending model. We consider P2P lending an alternative source of financing that marginal borrowers use to pay the increased mortgage down payment, making them eligible to receive a mortgage from conventional banks. In other words, we examine whether and to what extent P2P lending has a positive impact on the FHA loans volume by providing the ability to circumvent the loan-to-value (LTV) cap policy. As a result, P2P lending can be seen as a means for ”rationed” borrowers to have access to the market by reducing inequalities and promoting financial inclusion, thus achieving Sustainable Development Goals (SDGs). We employ hand-collected data from FHA mortgages, P2P loans, and other economic factors from all 50 U.S. states during 2007–2017 and use panel data techniques for this purpose. Research shows that P2P lending, GDP per capita, population growth, broad money growth rate, interest rate, unemployment rate, new housing units, and consumer confidence Index produce effects on FHA loans. We show that P2P lending, a nonconventional determinant, is causally associated with a significant increase in the count and volume of FHA loans, implying that P2P lending has a positive impact on them. The ability of P2P to bypass mortgage supply constraints (tightened LTV caps) by providing small loans to borrowers to meet the increased down payment requirements is very important to policy-makers, as it shows that constraining the volume of mortgage loans may be not achieved. Macroprudential tools designed to control credit growth may prove ineffective, as the use of alternative forms of lending helps circumvent them and ultimately leads to excessive household leverage with all the risks that it poses to the financial system. Full article

Semi-supervised consensus clustering is a promising strategy to compensate for the subjectivity of clustering and its sensitivity to design factors, with various techniques being recently proposed to integrate domain knowledge and multiple clustering partitions. In this article, we present a new approach that makes double use of domain knowledge, namely to build the initial partitions, as well as to combine them. In particular, we show how to model and integrate must-link and cannot-link constraints into the objective function of a generic consensus clustering ($CC$) framework that maximizes the similarity between the consensus partition and the input partitions, which have, in turn, been enriched with the same constraints. In addition, borrowing from the theory of functional dependencies, the integrated framework exploits the notions of deductive closure and minimal cover to take full advantage of the logical implication between constraints. Using standard UCI benchmarks, we found that the resulting algorithm, termed $CCC$ double-constrained consensus clustering), was more effective than plain $CC$ at combining base-constrained partitions, with an average performance improvement of 5.54%. We then argue that $CCC$ is especially well-suited for profiling counterfeit e-commerce websites, as constraints can be acquired by leveraging specific domain features, and demonstrate its potential for detecting affiliate marketing programs. Taken together, our experiments suggest that $CCC$ makes the process of clustering more robust and able to withstand changes in clustering algorithms, datasets, and features, with a remarkable improvement in average performance. Full article

A sustainable financing strategy for SMEs should aim to enhance a low-cost collateral-free supply of loans to SMEs with good track records of repayments to banks. In this paper, we suggest two alternative financing models for SMEs that address certain borrowing constraints of SMEs. First, the model incorporates institutional mechanisms involving the government, banks, and SMEs. The strategy employs a two-pronged approach: (i) the government enhances the supply of loanable funds to banks, and (ii) identifies good SME borrowers through skills development programs and introduces them to banks. This model will reduce default risk and allow banks to offer lower-interest and collateral-free credit to SMEs, thereby improving their access to finance and performance. Second, the model could be extended to accommodate digital finance using a data-driven credit risk score of the borrowers to reduce banks’ default risks and transaction costs with or without government funds. The proposed model could resolve the moral hazard and selection bias problems. Our proposed models are based on a public-private partnership approach and therefore could solve certain borrowing constraints of SMEs. Our empirical results support the model outcomes and therefore are consistent with the predictions of our theoretical models. Full article

Computational modeling and simulation of biological systems have become valuable tools for understanding and predicting cellular performance and phenotype generation. This work aimed to construct, model, and dynamically simulate the virulence factor pyoverdine (PVD) biosynthesis in Pseudomonas aeruginosa through a systemic approach, considering that the metabolic pathway of PVD synthesis is regulated by the quorum-sensing (QS) phenomenon. The methodology comprised three main stages: (i) Construction, modeling, and validation of the QS gene regulatory network that controls PVD synthesis in P. aeruginosa strain PAO1; (ii) construction, curating, and modeling of the metabolic network of P. aeruginosa using the flux balance analysis (FBA) approach; (iii) integration and modeling of these two networks into an integrative model using the dynamic flux balance analysis (DFBA) approximation, followed, finally, by an in vitro validation of the integrated model for PVD synthesis in P. aeruginosa as a function of QS signaling. The QS gene network, constructed using the standard System Biology Markup Language, comprised 114 chemical species and 103 reactions and was modeled as a deterministic system following the kinetic based on mass action law. This model showed that the higher the bacterial growth, the higher the extracellular concentration of QS signal molecules, thus emulating the natural behavior of P. aeruginosa PAO1. The P. aeruginosa metabolic network model was constructed based on the iMO1056 model, the P. aeruginosa PAO1 strain genomic annotation, and the metabolic pathway of PVD synthesis. The metabolic network model included the PVD synthesis, transport, exchange reactions, and the QS signal molecules. This metabolic network model was curated and then modeled under the FBA approximation, using biomass maximization as the objective function (optimization problem, a term borrowed from the engineering field). Next, chemical reactions shared by both network models were chosen to combine them into an integrative model. To this end, the fluxes of these reactions, obtained from the QS network model, were fixed in the metabolic network model as constraints of the optimization problem using the DFBA approximation. Finally, simulations of the integrative model (CCBM1146, comprising 1123 reactions and 880 metabolites) were run using the DFBA approximation to get (i) the flux profile for each reaction, (ii) the bacterial growth profile, (iii) the biomass profile, and (iv) the concentration profiles of metabolites of interest such as glucose, PVD, and QS signal molecules. The CCBM1146 model showed that the QS phenomenon directly influences the P. aeruginosa metabolism to PVD biosynthesis as a function of the change in QS signal intensity. The CCBM1146 model made it possible to characterize and explain the complex and emergent behavior generated by the interactions between the two networks, which would have been impossible to do by studying each system’s individual components or scales separately. This work is the first in silico report of an integrative model comprising the QS gene regulatory network and the metabolic network of P. aeruginosa. Full article

The presence of small enterprises in developing countries makes new information on these enterprises substantially valuable for these countries. Governments have put forward numerous action plans and public policies to improve access to external credit. However, despite all technological advances, there are still situations linked to the theory of asymmetric information between lenders and borrowers, which influences the granting of financing. Under this premise, the present research uses latent classes to analyze the financing decision behavior patterns of 1033 business owners who faced the financing process and the constraints faced by lenders based on the asymmetric information theory. The results allowed the construction of a model that identified five profiles of trust in financial institutions among entrepreneurs that affected their financing decisions. Full article