Search Results (658)

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

Prior research has examined how investors view asset securitizations, and shows that investors treat securitizations as borrowings, even when GAAP treats them as sales. Upon the adoption of two new accounting standards relating to securitizations, some off-balance-sheet securitized assets were consolidated back onto firms’ balance sheets. This study investigated how investors viewed assets that firms consolidated under the new standards and those that firms left unconsolidated. I found that investors differentiated between these two types of securitizations, treating the consolidated assets as borrowings and the unconsolidated assets as sales. I conclude that the new accounting standards are more consistent with equity investors’ views of securitizations. I also found that, for the consolidated assets, investors did not distinguish between securitizations going through two different accounting structures. Lastly, this study provides evidence on one information channel that investors use to distinguish between securitizations that may have the economic substance of borrowings versus sales. Full article

This research paper explores the ethical considerations in using financial technology (fintech), focusing on big data, artificial intelligence (AI), and privacy. Using a systematic literature-review methodology, the study identifies ethical and privacy issues related to fintech, including bias, discrimination, privacy, transparency, justice, ownership, and control. The findings emphasize the importance of safeguarding customer data, complying with data protection laws, and promoting corporate digital responsibility. The study provides practical suggestions for companies, including the use of encryption techniques, transparency regarding data collection and usage, the provision of customer opt-out options, and the training of staff on data-protection policies. However, the study is limited by its exclusion of non-English-language studies and the need for additional resources to deepen the findings. To overcome these limitations, future research could expand existing knowledge and collect more comprehensive data to better understand the complex issues examined. Full article

The aim of this study was to investigate the effect of the retention per share compared to the dividend per share by modeling the firm’s market value as a function of the retention per share and the dividend per share for all firms in the Jordanian context using unbalanced panel data analysis for a sample of 2281 firm years covering the period from 2010 to 2021. The results of the pooled sample indicated a strong positive significant effect for dividends per share. However, the retention per share indicated a negative significant effect on the firm’s market value. The other robustness analysis for the two sub-samples and the financial and non-financial sub-samples indicated the same results, consistent with the pooled sample for the two main explanatory variables. Full article

Due to the volatility of the markets and the ongoing crises (COVID-19, the Ukrainian war, etc.), investors are keen to exploit any potential chances to make profits. For this reason, the idea of harvesting data from cryptocurrency market users takes an innovative step. Potential investors in supply chain firms in the fertilizer industry need to know whether the observation of data originating from the cryptocurrency market is capable of explaining their stock price variation. The authors identify the innovative utilization of cryptocurrency markets’ user analytical data to model and predict the stock price of supply chain firms in the fertilizer industry stock price. The main aim of this research is to evaluate the contribution of cryptocurrency market big data as a predicting factor for the stock price of fertilizer market firms. Such a finding improves the knowledge and decision-making of potential investors in the fertilizer market. Moreover, this study seeks to highlight the benefits of utilizing cryptocurrency market big data for other financial purposes, apart from stock price prediction. The analytical data was derived from cryptocurrency websites and applications and was then processed through statistical analysis (correlation and linear regressions), Fuzzy Cognitive Maps (FCM), and Hybrid Modeling (HM) modeling. The hybrid model’s simulation showed that analytical data from the cryptocurrency markets tend to explain and predict the stock price of supply chain firms in the fertilizer industry. Such data refer to Bitcoin’s website organic keywords and traffic costs, as well as paid traffic costs from cryptocurrency trade websites/apps. A rise in Bitcoin and cryptocurrency trade websites’ organic and paid traffic costs tend to increase supply chain firms in the fertilizer industry’s stock prices, while Bitcoin’s website organic keywords variation decreases accordingly. Full article

Our study analyzes the relationship between digital financial inclusion and women’s labor force participation, as well as shedding light on the barriers to women’s digital financial inclusion. We have mobilized a microeconomic database that covers 15,192 African women. Our database is extracted from the Global Findex database, 2021 edition, based on nationally representative surveys of 29 African countries. The Probit model estimation methodology is used to examine the empirical results. Our findings reveal that financial inclusion via the digital channel is positively associated with women’s labor force participation more than the traditional channel. A significant and positive impact of formal financial services channels on the level of women’s participation in the labor market was uncovered. Our research has shown that women face a variety of obstacles when it comes to accessing financial services, both through traditional channels and digital means. These barriers include nonvoluntary obstacles in traditional financial inclusion channels. However, as a woman’s income level increases, the intensity of these barriers decreases. When it comes to digital financial inclusion, women often face a unique set of obstacles, such as the high cost of mobile financial services, lack of money, and lack of access to a cellphone. The study contributes to the existing literature by investigating the impact of digital financial inclusion on women’s labor force participation in African countries and identifying barriers that hinder women’s digital financial inclusion based on individual-level data. It suggests that African policymakers should increase women’s financial inclusion through digital channels to improve their participation in the labor market. Full article