Search Results (663)

This article presents the results of a bibliometric review of the study of real estate bubbles in the scientific literature indexed in Web of Science and Scopus, from 2007 to 2022. The analysis was developed using a sample of 2276 documents, which were reviewed in R software and analyzed with the assistance of the Bibliometrix package of the same software. The results indicate that there has been considerable productivity on the topic of real estate bubbles since 2007, with an emphasis on housing price formation processes and the social effects when bubbles burst. The authors found that there were not many case studies located in Latin America or Africa, nor were there approaches with advanced predictive modeling techniques using machine learning or artificial intelligence. The article provides an understanding of the state of the art in real estate bubble research and situates new research in front of the influential literature previously published. Full article

This study focused on testing the existence of an apartment price bubble in Hanoi (Vietnam) and on determining the factors that affected it in the period between 2010 and 2021. Using the fundamental factor approach, the authors applied VAR regression using time series data. Specifically, we used the ADF unit test to test the stationarity of the variables based on the following criteria: AIC (Akaike information criterion); LR (likelihood ratio); FPE (final prediction error); HQ (Hanan–Quinn information criterion); and Schwarz (SC) to find the optimal lag (Lag) for the model. We also applied the Granger causality test to determine the correlation between the economic variables that appeared in the model with the PR index. We present the results of the research model through the push–response function and the variance decomposition to consider and evaluate the impact of the PR index shock on itself and the other variables. The literature in this field includes many studies that are similar to this one; however, no research has been conducted that has focused on analysing whether variables, such as per capita income and the urbanisation rate, influence the formation of real estate bubbles. This focus is especially relevant in Hanoi, which is an important part of the Vietnamese real estate market. Through this study, we aimed to fill this gap and to contribute to the references on the Hanoi real estate market and its influencing factors. Full article

Participatory budgeting has been advocated as an advanced tool of civic participation and a travelling innovation for more than three decades. This paper provides a bibliometric review of the concurrent body of knowledge on participatory budgeting (PB), explaining how this democratic innovation ‘travelled’ through time and over different scientific fields. This study was based on a dataset of 396 papers on PB published from 1989 to January 2023. The study finds that research in PB has reached its peak of scholarly attention in pre-COVID-19 pandemic years. The study also finds that the research on PB has migrated from the field of political science to other fields, such as economics, management science, law, urban planning, environmental science, and technology. Full article

A partial risk guarantee (PRG) is one of the critical instruments in the blended finance approach that provides partial assurance to the risk investor to lend leveraged capital to the borrower. Under the PRG scheme, philanthropic capital is employed as a risk guarantee to create financial and economic additionality through the multiplier effect. This study examines the current trends in PRG and blended finance ecosystem research. This study also aims to identify future research areas to work upon. The bibliometric analysis highlights the need and advantages of blended finance and PRG. The study highlights themes, such as climate finance, SDGs, impact investments, and blended finance/PRGs, from the literature on blended finance. This study illustrates the impact for researchers and managers regarding the future direction to undertake and the domains where PRG can work wonders. The research allows for a comprehensive view of the leading trends, such as utilising blended finance tools such as PRG in funding the work in climate financing, SDGs, water, sanitation, and impact investment. This is perhaps the first study to conduct a bibliometric analysis of the developing area of blended finance partial risk guarantee literature to highlight its importance and advantages. Full article

As opposed to developed markets, financing constraints are a more pressing issue among Small and Medium-Sized Enterprises (SMEs) in emerging markets. We explore the severity of financing constraints on SMEs, and examine the role of supply chain finance (SCF) in alleviating those constraints, with the focus on a large emerging market: China. Using the panel data of SMEs listed on Shenzhen Stock Exchange from 2014 to 2020, we employ robust estimations of panel-corrected standard errors (PCSEs) and robust fixed-effects methods to analyze the issue. Our cash–cash-flow sensitivity model points out that listed SMEs in China show significant cash–cash-flow sensitivity, and financing constraints are prevalent. We document that the development of SCF has a mitigation effect on the financing constraints on the SMEs. Our robustness test with Yohai’s MM-estimator is also supportive of the main finding. Our study indicates the importance of supply chain finance development in alleviating the financing constraints on SMEs and, subsequently, supporting their sustainability journey. Overall, our findings have important policy implications for the stakeholders involved in emerging markets, and there are lessons to be learned from the Chinese experience. There is still much to be explored in the nexus of SCF and the financing difficulties of SMEs in China at present, with much of the extant literature concentrating only on specific financing mechanisms. Thus, our study fills the gap by providing a broad and comprehensive analysis of the issue. Full article

News on the stock market contains positive or negative sentiments depending on whether the information provided is favorable or unfavorable to the stock market. This study aims to discover news sentiments and classify news according to its sentiments with the application of PhoBERT, a Natural Language Processing model designed for the Vietnamese language. A collection of nearly 40,000 articles on financial and economic websites is used to train the model. After training, the model succeeds in assigning news to different classes of sentiments with an accuracy level of over 81%. The research also aims to investigate how investors are concerned with the daily news by testing the movements of the market before and after the news is released. The results of the analysis show that there is an insignificant difference in the stock price as a response to the news. However, negative news sentiments can alter the variance of market returns. Full article

This research investigates the relationship between capital structure and firm value for companies listed on the Vietnamese stock market. The study utilizes data from audited financial statements of 769 companies spanning from 2012 to 2022, amounting to 8459 observations. Employing various estimation methods, such as ordinary least squares (OLS), fixed effects model (FEM), random effects model (REM), and generalized least squares (GLS), the impact of capital structure on key financial indicators, namely, return on assets (ROA), return on equity (ROE), and Tobin’s Q, is assessed. The findings indicate that the debt ratio exhibits a positive influence on ROA, ROE, and Tobin’s Q, with Tobin’s Q displaying the most pronounced impact (0.450) and ROA showing the weakest impact (0.011). However, the long-term debt ratio does not significantly affect firm value. Interestingly, both short-term and long-term debt ratios have negative effects on ROA, ROE, and Tobin’s Q, with the most substantial impact on Tobin’s Q reduction (0.562). Based on these research outcomes, the authors offer valuable recommendations to companies, investors, business leaders, and policymakers to make informed decisions in selecting an optimal and sensible capital structure. Full article

The stock markets in developing countries are highly responsive to breaking news and events. Our research explores the impact of economic conditions, financial policies, and politics on the KSE-100 index through daily market news signals. Utilizing simple OLS regression and ARCH/GARCH regression methods, we determine the best model for analysis. The results reveal that political and global news has a significant impact on KSE-100 index. Blue chip stocks are considered safer investments, while short-term panic responses often overshadow rational decision-making in the stock market. Investors tend to quickly react to negative news, making them risk-averse. Our findings suggest that the ARCH/GARCH models are better at predicting stock market fluctuations compared to the simple OLS method. Full article

The landscape of financial credit risk models is changing rapidly. This study takes a brief look into the future of predictive modelling by considering some factors that influence financial credit risk modelling. The first factor is machine learning. As machine learning expands, it becomes necessary to understand how these techniques work and how they can be applied. The second factor is financial crises. Where predictive models view the future as a reflection of the past, financial crises can violate this assumption. This creates a new field of research on how to adjust predictive models to incorporate forward-looking conditions, which include future expected financial crises. The third factor considers the impact of financial technology (Fintech) on the future of predictive modelling. Fintech creates new applications for predictive modelling and therefore broadens the possibilities in the financial predictive modelling field. This changing landscape causes some challenges but also creates a wealth of opportunities. One way of exploiting these opportunities and managing the associated risks is via industry collaboration. Academics should join hands with industry to create industry-focused training and industry-focused research. In summary, this study made three novel contributions to the field of financial credit risk models. Firstly, it conducts an investigation and provides a comprehensive discussion on three factors that contribute to rapid changes in the credit risk predictive models’ landscape. Secondly, it presents a unique discussion of the challenges and opportunities arising from these factors. Lastly, it proposes an innovative solution, specifically collaboration between academic and industry partners, to effectively manage the challenges and take advantage of the opportunities for mutual benefits. Full article

This research addresses the impact of individual investors on the cryptocurrency market, focusing specifically on the development of herd behavior. Although the phenomenon of herd behavior has been studied extensively in the stock market, it has received limited research in the context of cryptocurrencies. This study aims to fill this research gap by examining the impact of liquidity and sentiment on herd behavior using the CSAD model, considering small, medium, and large cryptocurrencies. The results show different outcomes for cryptocurrencies of different sizes, consistently demonstrating that the herding effect is more pronounced under conditions of lower liquidity, as determined by the turnover volume and liquidity ratio of cryptocurrencies. Proxy measures such as the Twitter Hedonometer and CBOE VIX were used to measure investor sentiment and show the prevalence of herding behavior in optimistic times for all cryptocurrencies, regardless of their market capitalization. Consequently, this study provides valuable insights into the manifestation of herd behavior in the cryptocurrency market and highlights the importance of liquidity and sentiment as influencing factors. These findings improve our understanding of investor behavior and provide guidance to market participants and policymakers on how to effectively manage the risks associated with herd effects. Full article

Corporate credit ratings provide multiple strategic, financial, and managerial benefits for decision-makers. Therefore, it is essential to have accurate and up-to-date ratings to continuously monitor companies’ financial situations when making financial credit decisions. Machine learning (ML)-based internal models can be used for the assessment of companies’ financial situations using annual statements. Particularly, it is necessary to check whether these ML models achieve better results compared to statistical methods. Due to the multi-class classification problem when forecasting corporate credit ratings, the development, monitoring, and maintenance of ML-based systems are more challenging compared to simple classifications. This problem becomes even more complex due to the required coordination with financial regulators (e.g., OECD, EBA, BaFin, etc.). Furthermore, the ML models must be updated regularly due to the periodic nature of annual statements as a dataset. To address the problem of the limited dataset, multiple sampling strategies and machine learning algorithms can be combined for accurate and up-to-date forecasting of credit ratings. This paper provides various implications for ML-based forecasting of credit ratings and presents an approach for combining sampling strategies and ML techniques. It also provides design recommendations for ML-based services in the finance industry on how to fulfill the existing regulations. Full article

Researchers in developed countries have questioned whether share repurchase activity influences internal investment. The aim of this study was to investigate the relationship between share repurchases and internal investment (defined as capital expenditure, employment expenditure, and research and development) in South Africa, as little was known about this relationship in developing countries. A quantitative research methodology was followed, employing the data of South African listed companies during the 2002–2017 period. A significant negative relationship was noted between share repurchases and employment expenditure when considering all companies, while high-growth companies exhibited a significant negative relationship between share repurchases and capital expenditure. The negative relationships could indicate that companies increase share repurchases to the detriment of internal investment (especially employment). Alternatively, it may imply that share repurchase and internal investment decisions are determined simultaneously, with companies decreasing internal investment and increasing share repurchases in the absence of identifiable profitable projects (or increasing internal investment and decreasing share repurchases when growth opportunities are available). These findings could be useful to shareholders, corporate governance regulators and activists. Given the high unemployment and income inequality in South Africa, the results support a call for the improved regulation of share repurchases to ensure effective monitoring. Full article

The financial sector has greatly impacted the monetary well-being of consumers, traders, and financial institutions. In the current era, artificial intelligence is redefining the limits of the financial markets based on state-of-the-art machine learning and deep learning algorithms. There is extensive use of these techniques in financial instrument price prediction, market trend analysis, establishing investment opportunities, portfolio optimization, etc. Investors and traders are using machine learning and deep learning models for forecasting financial instrument movements. With the widespread adoption of AI in finance, it is imperative to summarize the recent machine learning and deep learning models, which motivated us to present this comprehensive review of the practical applications of machine learning in the financial industry. This article examines algorithms such as supervised and unsupervised machine learning algorithms, ensemble algorithms, time series analysis algorithms, and deep learning algorithms for stock price prediction and solving classification problems. The contributions of this review article are as follows: (a) it provides a description of machine learning and deep learning models used in the financial sector; (b) it provides a generic framework for stock price prediction and classification; and (c) it implements an ensemble model—“Random Forest + XG-Boost + LSTM”—for forecasting TAINIWALCHM and AGROPHOS stock prices and performs a comparative analysis with popular machine learning and deep learning models. Full article

This paper explores the state of the art in the cryptocurrency literature, with a special emphasis on the links between financial dimensions and ESG features. The study uses bibliometric analysis to illustrate the history of cryptocurrency publication activity, focusing on the most popular subjects and research trends. Between 2014 and 2021, 1442 papers on cryptocurrencies were published in the Web of Science core collection, the most authoritative database, although only a tiny percentage evaluated ESG factors. One of the most common criticisms of cryptocurrencies is the pollution derived from energy consumption in their mining process and their use for illicit purposes due to the absence of effective regulation. The study allows us to suggest future research directions that may be beneficial in illustrating the environmental effect, studying financial behavior, identifying the long-term sustainability of cryptocurrencies, and evaluating their financial success. This study provides an in-depth examination of current research trends in the field of cryptocurrencies, identifying prospective future research directions. Full article

We aim to provide a comprehensive systematic analysis of scholarly publications in the field of board compensation in financial sectors extending through the years 1987 to 2021. Hence, the most notable themes, theories, and contributions to the literature are identified, and research developments over time are evaluated. With the identification of a final sample of 87 research papers indexed in Scopus, we identify research gaps to provide insight into future research following a systematic method. The results revealed that the United States of America received the broadest research interest, along with cross-country research. While the literature lacked to provide investigations for other countries of the world. Although the effect of compensation on organizational outcomes (performance and grow) is still unclear in the literature, several factors have been introduced as key drivers of the compensation, including the country’s level of development, the development of equity markets, the development of banking system, its dependence on foreign capital, collective rights empowering labor, the strength of a country’s welfare institutions, employment market forces, and social order and authority relations. On a theoretical level, agency theory has been most popular in the literature, along with providing multiple theoretical frameworks with agency theory as a slack resources theory, managerial talent theory, and managerial power theory. This is the first research to our knowledge that used a systematic review (SR) of literature to give a complete and comprehensive evaluation of the literature on board compensation in the financial sector. The current study documents the flow of literature on the board’s compensation in the financial sectors over 24 years and establishes future research opportunities. Full article