Hasil untuk "Finance"

A. Demirguc-Kunt, T. Beck, P. Honohan

M. Ayyagari, A. Demirguc-Kunt, V. Maksimovic

China is often mentioned as a counterexample to the findings in the finance and growth literature since, despite the weaknesses in its banking system, it is one of the fastest growing economies in the world. The fast growth of Chinese private sector firms is taken as evidence that it is alternative financing and governance mechanisms that support China's growth. This paper takes a closer look at firm financing patterns and growth using a database of 2,400 Chinese firms. The authors find that a relatively small percentage of firms in the sample utilize formal bank finance with a much greater reliance on informal sources. However, the results suggest that despite its weaknesses, financing from the formal financial system is associated with faster firm growth, whereas fund raising from alternative channels is not. Using a selection model, the authors find no evidence that these results arise because of the selection of firms that have access to the formal financial system. Although firms report bank corruption, there is no evidence that it significantly affects the allocation of credit or the performance of firms that receive the credit. The findings suggest that the role of reputation and relationship based financing and governance mechanisms in financing the fastest growing firms in China is likely to be overestimated.

Vikas Jain

Robert E. Carpenter, Bruce C. Petersen

R. C. Merton, P. Samuelson

T. Beck, A. Demirguc-Kunt, R. Levine

R. Levine

B. Mandelbrot

A. Auerbach

D. Duffie, D. Filipovi´c, And W Schachermayer

J. B. Heaton

R. Gencay, F. Selçuk, Brandon Whitcher

Amir Sufi

Stephen G. Cecchetti, Stephen G. Cecchetti, Stephen G. Cecchetti et al.

This paper unveils a new resource for macroeconomic research: a long-run dataset covering disaggregated bank credit for 17 advanced economies since 1870. The new data show that the share of mortgages on banks’ balance sheets doubled in the course of the 20th century, driven by a sharp rise of mortgage lending to households. Household debt to asset ratios have risen substantially in many countries. Financial stability risks have been increasingly linked to real estate lending booms which are typically followed by deeper recessions and slower recoveries. Housing finance has come to play a central role in the modern macroeconomy.

C. Kearney, Shan Liu

We survey the textual sentiment literature, comparing and contrasting the various information sources, content analysis methods, and empirical models that have been used to date. We summarize the important and influential findings about how textual sentiment impacts on individual, firm-level and market-level behavior and performance, and vice versa. We point to what is agreed and what remains controversial. Promising directions for future research are emerging from the availability of more accurate and efficient sentiment measures resulting from increasingly sophisticated textual content analysis coupled with more extensive field-specific dictionaries. This is enabling more wide-ranging studies that use increasingly sophisticated models to help us better understand behavioral finance patterns across individuals, institutions and markets.

Pol Antrs, C. Foley, Matthew C. Johnson et al.

This paper theoretically and empirically analyzes the financing terms that support international trade. The choice of trade finance terms balances the risk that an importer defaults on an exporter and the possibility that an exporter does not deliver goods as specified. Analysis of transaction-level data from a US exporter reveals that importers located in countries with weak enforcement of contracts typically finance transactions, but these firms are able to overcome the constraints of such environments if they can establish a relationship with the exporter. Furthermore, the manner in which trade is financed shapes the impact of crises.

Eric Vansteenberghe

These lecture notes provide a comprehensive introduction to Quantitative Methods in Finance (QMF), designed for graduate students in finance and economics with heterogeneous programming backgrounds. The material develops a unified toolkit combining probability theory, statistics, numerical methods, and empirical modeling, with a strong emphasis on implementation in Python. Core topics include random variables and distributions, moments and dependence, simulation and Monte Carlo methods, numerical optimization, root-finding, and time-series models commonly used in finance and macro-finance. Particular attention is paid to translating theoretical concepts into reproducible code, emphasizing vectorization, numerical stability, and interpretation of outputs. The notes progressively bridge theory and practice through worked examples and exercises covering asset pricing intuition, risk measurement, forecasting, and empirical analysis. By focusing on clarity, minimal prerequisites, and hands-on computation, these lecture notes aim to serve both as a pedagogical entry point for non-programmers and as a practical reference for applied researchers seeking transparent and replicable quantitative methods in finance.

Yaxuan Kong, Hoyoung Lee, Yoontae Hwang et al.

Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases can inflate performance, contaminate backtests, and make reported results useless for any deployment claim. We identify five recurring biases in financial LLM applications. They include look-ahead bias, survivorship bias, narrative bias, objective bias, and cost bias. These biases break financial tasks in distinct ways and they often compound to create an illusion of validity. We reviewed 164 papers from 2023 to 2025 and found that no single bias is discussed in more than 28 percent of studies. This position paper argues that bias in financial LLM systems requires explicit attention and that structural validity should be enforced before any result is used to support a deployment claim. We propose a Structural Validity Framework and an evaluation checklist with minimal requirements for bias diagnosis and future system design. The material is available at https://github.com/Eleanorkong/Awesome-Financial-LLM-Bias-Mitigation.

Gimena Cruz, Yover Fernández, Miluska Villar-Guevara et al.

IntroductionCorporate social responsibility (CSR) has always played an essential role in the market, contributing to a more competitive and efficient business environment that must be incorporated across sectors, including financial institutions. This research examined whether CSR, customer satisfaction, and corporate image are related to customer loyalty.MethodsAn explanatory study was conducted with 424 Peruvians aged 18–68 (M = 32.70, SD = 10.66). Data were collected using a self-report scale of CSR, corporate image, satisfaction, and customer loyalty. The theoretical model was evaluated using the Partial Least Squares Structural Equation Modeling (PLS-SEM).ResultsA measurement model with adequate fit was obtained (α = 0.901–0.950; CR = 0.902–0.950; AVE = 0.746–0.910). Based on the results, a positive related was demonstrated between CSR with the customer loyalty (β = 0.264; p < 0.000; t = 4.593) and corporate image (β = 0.235; p < 0.000; t = 4.615); between satisfaction with the customer loyalty (β = 0.317; p < 0.000; t = 4.061) and corporate image (β = 0.645; p < 0.000; t = 12.766), and between corporate image with the customer loyalty (β = 0.235; p = 0.005; t = 2.810).DiscussionThe study offers a valuable theoretical contribution by situating the proposed model within the integrative framework of sustainability psychology, stakeholder theory, and the Triple Bottom Line (TBL). Furthermore, it provides a significant empirical contribution by arguing that strategic investments in CSR, such as environmental finance, digitalization, and inclusive initiatives, yield tangible environmental and social benefits. These practices align with the Sustainable Development Goals (SDGs) and strengthen customer loyalty. The findings encourage decision-makers to integrate measurable sustainability actions informed by sustainability psychology to understand the individual and collective psychological processes that promote sustainable practices across organizational, social, and environmental contexts.