Impact of financial literacy, perceived access to finance, ICT use, and digitization on credit constraints: evidence from Qatari MSME importersFinancial Innovation - Tập 10 - Trang 1-37 - 2024
Lanouar Charfeddine, Mohamed Ismail Umlai, Mazen El-Masri
This study investigates the role of financial literacy (FL), perceived access to finance (PAF), information communication technology (ICT) usage, and digitization in alleviating the level of credit constraint that micro, small, and medium enterprises (MSMEs) face in an emerging market. We draw on the economic research theories of human capital, knowledge-based view, and lifecycle hypothesis to explain the relationship between the variables. Using survey data collected from 333 MSME importers in Qatar—a country with heavy reliance on foreign goods—we find strong evidence that FL, PAF, ICT usage, and digitization are key determinants of Qatari MSME access to credit. In particular, PAF and FL are significant and have their expected signs in almost all the Probit regressions. For ICT usage and digitization, although they are key determinants of credit constraints, the findings are more sensitive and dependent on the type of financing and the resulting type of credit constraint.
Modelling trust evolution within small business lending relationshipsFinancial Innovation - Tập 4 - Trang 1-18 - 2018
Ying Tang, Andrea Moro, Sandro Sozzo, Zhiyong Li
Trust is a key dimension in the principal-agent relationship and it has been studied extensively. However, the dynamics, evolution, and intrinsic motivation and mechanisms have received less attention. This paper investigates the intrinsic motivation of trust and it proposes a theoretical model of trust evolution that is based on the notion of ‘trust response’ and ‘trust spiral’. We then specifically focus on trust within the lending relationship between banks and small businesses, and we run numerical simulations to further illustrate the evolution of involved mutual trust over time. Our model provides implications for future research in both trust evolution and small business lending relationships.
Is a correlation-based investment strategy beneficial for long-term international portfolio investors?Financial Innovation - Tập 9 - Trang 1-26 - 2023
Seema Wati Narayan, Mobeen Ur Rehman, Yi-Shuai Ren, Chaoqun Ma
Using negative to low-correlated assets to manage short-term portfolio risk is not uncommon among investors, although the long-term benefits of this strategy remain unclear. This study examines the long-term benefits of the correlation strategy for portfolios based on the stock market in Asia, Central and Eastern Europe, the Middle East and North Africa, and Latin America from 2000 to 2016. Our strategy is as follows. We develop five portfolios based on the average unconditional correlation between domestic and foreign assets from 2000 to 2016. This yields five regional portfolios based on low to high correlations. In the presence of selected economic and financial conditions, long-term diversification gains for each regional portfolio are evaluated using a panel cointegration-based testing method. Consistent across all portfolios and regions, our key cointegration results suggest that selecting a low-correlated portfolio to maximize diversification gains does not necessarily result in long-term diversification gains. Our empirical method, which also permits the estimation of cointegrating regressions, provides the opportunity to evaluate the impact of oil prices, U.S. stock market fluctuations, and investor sentiments on regional portfolios, as well as to hedge against these fluctuations. Finally, we extend our data to cover the years 2017–2022 and find that our main findings are robust.
DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market predictionFinancial Innovation - - 2021
Suppawong Tuarob, Poom Wettayakorn, Ponpat Phetchai, Siripong Traivijitkhun, Sunghoon Lim, Thanapon Noraset, Tipajin Thaipisutikul
The explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.
Consumer lending efficiency: commercial banks versus a fintech lenderFinancial Innovation - - 2022
Joseph P. Hughes, Julapa Jagtiani, Choon-Geol Moon
AbstractFintechs are believed to help expand credit access to underserved consumers without taking on additional risk. We compare the performance efficiency of LendingClub’s unsecured personal loans with similar loans originated by banks. Using stochastic frontier estimation, we decompose the observed nonperforming loan (NPL) ratio into three components: the best-practice minimum NPL ratio, the excess NPL ratio, and a statistical noise, the former two of which reflect the lender’sinherent credit riskandlending inefficiency, respectively. As of 2013 and 2016, we find that the higher NPL ratios at the largest banks are driven by inherent credit risk, rather than lending inefficiency. Smaller banks are less efficient. In addition, as of 2013, LendingClub’s observed NPL ratio and lending efficiency were in line with banks with similar lending volume. However, its lending efficiency improved significantly from 2013 to 2016. As of 2016, LendingClub’s performance resembled the largest banks – consistent with an argument that its increased use of alternative data and AI/ML may have improved its credit risk assessment capacity above and beyond its peers using traditional approaches. Furthermore, we also investigate capital market incentives for lenders to take credit risk. Market value regression using the NPL ratio suggests that market discipline provides incentives to make less risky consumer loans. However, the regression using twodecomposed components(inherent credit risk and lending inefficiency) tells a deeper underlying story: market value is significantly positively related to inherent credit risk at most banks, whereas it is significantly negatively related to lending inefficiency at most banks.Market discipline appears to reward exposure to inherent credit risk and punish inefficient lending.
A study of the factors affecting mobile money penetration rates in the West African Economic and Monetary Union (WAEMU) compared with East AfricaFinancial Innovation - - 2021
Sionfou Seydou Coulibaly
AbstractAccording to the 2017 Global Financial Inclusion (Global Findex) database, the average penetration rate of mobile money accounts in East Africa is higher than that of the WAEMU. This study attempts to understand the factors driving the adoption and the use of mobile financial services in the WAEMU compared to East Africa. To achieve this, micro-level data from the 2017 Global Findex database are used to perform probit and multinomial logit estimations. The findings reveal that the same determinants influence the adoption and use of mobile money accounts across the populations of both groups of countries, specifically those related to the least vulnerable social categories (i.e., males, older, more educated, richer and part of the workforce). Therefore, in comparison to East Africa, the delay in the penetration of mobile money accounts observed in the WAEMU may be attributed to insufficient policies for increasing the awareness of the benefits of mobile financial services. The study recommends that governments in WAEMU countries promote the use of mobile money accounts among the working-age population (adults aged between 25 and 64) through the improvement of individual income level, and the introduction of incentives into the education system to encourage their population to attain higher levels of education.
Financial decision-making behaviors of Ethnic Tibetan Households based on mental accountingFinancial Innovation - Tập 9 - Trang 1-26 - 2023
DunGang Zang, Krishna P. Paudel, Yan Liu, Dan Liu, Yating He
Ethnic Tibetans (ETs) typically reside in the remote plateaus of China and possess strong cultural and spiritual values. Their financial decision-making is influenced by economic and physical factors, unique culture, social norms, and psychological motivators. We conducted an in-person survey of 480 randomly selected ET households across four provinces in rural China. The survey data was analyzed using three different econometric models—probit, ordered probit, and ranked ordered logit—to examine the choice of borrowing from formal or informal credit sources, the number of sources borrowed from, and repayment priority. Our findings indicate that mental accounting plays a significant role in the financial decision-making process of ET households. Additionally, we find that the informal credit source is strongly associated with the financial decisions of ET households. The majority of loans from formal financial institutions are used to meet daily needs, as opposed to purchasing productive inputs. Our results also suggest that strong social relationships and religious beliefs prevent households from defaulting, and that loans from formal financial sources receive repayment priority. China would benefit from promoting inclusive finance and encouraging the adoption of improved agricultural practices to support the prosperity of ET and other minority communities.
A simplified model for measuring longevity risk for life insurance productsFinancial Innovation - Tập 10 - Trang 1-30 - 2024
David Atance, Eliseo Navarro
In this paper, we propose a simple dynamic mortality model to fit and forecast mortality rates for measuring longevity and mortality risks. This proposal is based on a methodology for modelling interest rates, which assumes that changes in spot interest rates depend linearly on a small number of factors. These factors are identified as interest rates with a given maturity. Similarly, we assume that changes in mortality rates depend linearly on changes in a specific mortality rate, which we call the key mortality rate. One of the main advantages of this model is that it allows the development of an easy to implement methodology to measure longevity and mortality risks using simulation techniques. Particularly, we employ the model to calculate the Value-at-Risk and Conditional-Value-at-Risk of an insurance product testing the accuracy and robustness of our proposal using out-of-sample data from six different populations.
