Modern Finance
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Pricing the common stocks in emerging markets: The role of economic policy uncertainty
Get PDFWe examine the role of news-based policy uncertainty measures in capturing the cross-section of average stock returns in emerging markets. After controlling for the five established risk factors of Fama and French (FF), we find that policy uncertainty factors are redundant in capturing the average returns of portfolios constructed by considering well-known firm characteristics (size, book-to-market ratio, profitability, and investment). The pricing performance of the five factors model, both statistically and economically, does not improve with the addition of policy uncertainty factors. We argue that the news-based factors' information content is contained in FF risk factors. Our results are robust to additional test statistics and various policy uncertainty factors
Unleashing the power of artificial intelligence in Islamic banking: A case study of Bank Syariah Indonesia (BSI)
Get PDFThis research examines the challenges and opportunities of AI integration in Islamic banks through a case study of Bank Syariah Indonesia. A qualitative method was applied using an interview approach. Four experts from the IT division of Bank Syariah Indonesia were interviewed. The results suggest that AI applications offer potential benefits such as automation, improved decision-making and efficiency, customer recommendations, and enhanced customer experience. However, the challenges of AI integration include implementation costs, cyber security risks, Shariah compliance, and ethical issues. The research recommends that stakeholders in Islamic banks invest more in cybersecurity and educate their customers about the importance and usage of AI technology. Additionally, the research suggests that the government implements policies related to the ethical regulation of AI technology. Future research should provide comparative analysis and use a mixed-method approach to better understand the challenges and opportunities of AI integration in Islamic banks
Leverage, capital adequacy, and financial stability in the fintech industry: Evidence from Indonesia
Get PDFThe paper examined the influence of leverage and capital adequacy on fintech's financial stability in Indonesia. We utilize both quantitative and qualitative methods. The findings showed that leverage significantly constrained the financial stability of the fintech industry in the short run. Contrarily, capital adequacy has no significant effect on financial stability. Specifically, the qualitative results indicated that a high liability-to-asset ratio depressed the financial stability of the fintech industry. However, the influence of the asset-to-equity ratio on financial stability depends on asset quality, liquidity, and riskiness. Furthermore, the respondents noted the insufficiency of capital requirements in the fintech industry. Thus, fintech firms should focus on asset quality, while regulators should tighten capital regulation
Elections and bank non-performing loans: Evidence from developed countries
Get PDFThe existing literature has not examined how elections affect bank non-performing loans and its determinants even though banks are often the largest borrowers to fund election campaigns in many countries. This study investigates the determinants of bank non-performing loans (NPL) during election years in 35 developed countries. The fixed effect regression methodology was used to estimate the determinants of bank non-performing loans during election years. It was found that the banking sector experienced high NPLs during election years. Efficient banks operating in robust legal environments have higher non-performing loans during election years. It was also found that capital adequacy ratio, real GDP growth, loan-to-GDP ratio, cost-to-income ratio, political stability, and absence of terrorism are significant determinants of bank non-performing loans. The findings imply that election matters for the persistence of bank non-performing loans in developed countries.
Determinants of non-performing loans in conventional and Islamic banks: Emerging market evidence
Get PDFThis study examines the determinants of non-performing loans (NPLs) among macroeconomic and bank-specific factors for the Islamic and conventional banking sectors in Bangladesh. We implement a dynamic panel data model with a two-stage system GMM for the period 2010-2021. Among the bank-specific factors, this study finds that return on assets, return on equity, bank size, and inefficiency help to reduce NPLs. In contrast, gross loan growth, leverage, and capital adequacy ratios contribute to increasing NPLs. Among macroeconomic determinants, inflation, and GDP growth have a significant negative impact on NPLs. Moreover, unemployment and exchange rates are also found to be significant determinants of NPLs. At the bank level, growth in gross loans reduces NPLs in Islamic banks, while the opposite is true for conventional banks. Our findings have significant implications for depositors and regulators in making appropriate decisions
ChatGPT: Unlocking the future of NLP in finance
Get PDFThis paper reviews the current state of ChatGPT technology in finance and its potential to improve existing NLP-based financial applications. We discuss the ethical and regulatory considerations, as well as potential future research directions in the field. The literature suggests that ChatGPT has the potential to improve NLP-based financial applications, but also raises ethical and regulatory concerns that need to be addressed. The paper highlights the need for research in robustness, interpretability, and ethical considerations to ensure responsible use of ChatGPT technology in finance
Extreme risk spillovers between China and major international stock markets
Get PDFWe examine the complex dependence structure and risk spillovers between the Chinese stock market and twelve major international markets. To this end, we employ three types of vine copulas and tests for the Granger causality in risk of Hong et al. (2009). The results indicate that the R-vine copula is the optimal model to characterize the high-dimensional dependence structure of the markets after China joined the WTO, which suggests obvious structural differences with varying degrees of mainly positive dependences. Moreover, we identify unilateral extreme risk spillovers from China to the United States, France, and Germany, and either from Japan to China. We also detect bilateral spillovers between China and the United States, Japan, as well as Australia
Is tail risk priced in the cross-section of international stock index returns?
Get PDFThis study examines the predictive power of tail risk measures in stock indices returns using a comprehensive dataset covering 50 countries from 1926 to 2021. Our findings reveal that tail risk measures exhibit predictive power when considered independently. However, their forecasting abilities disappear when other risk and return factors are incorporated. This suggests that tail risk measures do not contain incremental information about the cross-section of stock returns beyond the commonly used global factors. Our findings are robust across various considerations, holding for alternative tail risk measure types, estimation periods, and different control variables subsets
Forecasting the equity premium: Do deep neural network models work?
Get PDFoai:ojs2.mf-journal.com:article/2This paper constructs deep neural network (DNN) models for equity-premium forecasting. We compare the forecasting performance of DNN models with that of ordinary least squares (OLS) and historical average (HA) models. The DNN models robustly work best and significantly outperform both OLS and HA models in both in- and out-of-sample tests and asset allocation exercises. Specifically, DNN models generate monthly out-of-sample R2 of 3.42% and an annual utility gain of 2.99% for a mean-variance investor from 2011:1 to 2016:12. Moreover, the forecasting performance of DNN models is enhanced by adding additional 14 variables selected from finance literature
What will ChatGPT revolutionize in the financial industry?
Get PDFThe launch of the open AI chatbot, ChatGPT, in November 2022 has generated widespread excitement around Generative Artificial Intelligence (AI). While researchers have explored ChatGPT's ability to produce content and respond to input, our study takes a different approach and examines its use cases in the financial industry. We aim to understand what ChatGPT offers the financial industry and how it differs from existing banking and financial chatbots. Financial institutions can use ChatGPT for a variety of purposes, including customer engagement, personalization, up-selling and cross-selling, stock forecasting, product development, and financial education. By focusing on the potential of ChatGPT in finance, we hope to spark discussions about its applications in other domains and explore the possibilities of a larger revolution in the future. Finally, this study identifies the challenges associated with the use of Generative Open AI and LLMs-based chatbots in the financial industry and provides recommendations for addressing these challenges