Hasil untuk "Finance"

David Hirshleifer

Behavioral finance studies the application of psychology to finance, with a focus on individual-level cognitive biases. I describe here the sources of judgment and decision biases, how they affect trading and market prices, the role of arbitrage and flows of wealth between more rational and less rational investors, how firms exploit inefficient prices and incite misvaluation, and the effects of managerial judgment biases. There is need for more theory and testing of the effects of feelings on financial decisions and aggregate outcomes. Especially, the time has come to move beyond behavioral finance to "social finance", which studies the structure of social interactions, how financial ideas spread and evolve, and how social processes affect financial outcomes.

Jie Li, Yu Wu, J. Xiao

Abstract Using panel data from the China Household Finance Survey (CHFS) in 2013, 2015, and 2017 and the digital inclusive finance index developed by Peking University, this study examined impacts of the digital inclusive finance on household consumption and explored its mechanisms. Results suggest that the digital inclusive finance could promote households consumption. A heterogeneity analysis showed that households with fewer assets, lower income, less financial literacy and in third- and fourth-tier cities experienced larger facilitating effects of digital finance on consumption compared to their counterparts. For consumption categories, digital finance was positively correlated with food, clothing, house maintenance, medical care, and education and entertainment expenditures. In terms of consumption structure, digital finance mainly promoted the recurring household expenditures rather than the non-recurring expenditures. Further analyses based on the mediating model found that online shopping, digital payment, obtainment of online credit, purchase of financing products on the internet and business insurance, were the main mediating variables through which digital finance affected household consumption.

Monica Billio, Mila Getmansky, A. Lo et al.

We propose several econometric measures of connectedness based on principal-components analysis and Granger-causality networks, and apply them to the monthly returns of hedge funds, banks, broker/dealers, and insurance companies. We find that all four sectors have become highly interrelated over the past decade, likely increasing the level of systemic risk in the finance and insurance industries through a complex and time-varying network of relationships. These measures can also identify and quantify financial crisis periods, and seem to contain predictive power in out-of-sample tests. Our results show an asymmetry in the degree of connectedness among the four sectors, with banks playing a much more important role in transmitting shocks than other financial institutions.

Shengling Zhang, Zihao Wu, Yao Wang et al.

Yuanchun Chen, C. Bellavitis

Abstract Blockchain technology can reduce transaction costs, generate distributed trust, and empower decentralized platforms, potentially becoming a new foundation for decentralized business models. In the financial industry, blockchain technology allows for the rise of decentralized financial services, which tend to be more decentralized, innovative, interoperable, borderless, and transparent. Empowered by blockchain technology, decentralized financial services have the potential to broaden financial inclusion, facilitate open access, encourage permissionless innovation, and create new opportunities for entrepreneurs and innovators. In this article, we assess the benefits of decentralized finance, identify existing business models, and evaluate potential challenges and limits. As a new area of financial technology, decentralized finance may reshape the structure of modern finance and create a new landscape for entrepreneurship and innovation, showcasing the promises and challenges of decentralized business models.

John W. Goodell, Satish Kumar, Weng Marc Lim et al.

Abstract Artificial intelligence (AI) and machine learning (ML) are two related technologies that are emergent in financial scholarship. However, no review, to date, has offered a wholistic retrospection of this research. To address this gap, we provide an overview of AI and ML research in finance. Using both co-citation and bibliometric-coupling analyses, we infer the thematic structure of AI and ML research in finance for 1986–April 2021. By uncovering nine (co-citation) and eight (bibliometric coupling) specific clusters of finance that apply AI and ML, we further identify three overarching groups of finance scholarship that are roughly equivalent for both forms of analysis: (1) portfolio construction, valuation, and investor behavior; (2) financial fraud and distress; and (3) sentiment inference, forecasting, and planning. Additionally, using co-occurrence and confluence analyses, we highlight trends and research directions regarding AI and ML in finance research. Our results provide assessment of  AI and ML in finance research.

Tadiwanashe Muganyi, Linnan Yan, Hua-ping Sun

This paper is one of the first to offer a comprehensive analysis of the impact of green finance related policies in China, utilizing text analysis and panel data from 290 cities between 2011 and 2018. Employing the Semi-parametric Difference-in-Differences (SDID) we show that overall China's green finance related policies have led to a significant reduction in industrial gas emissions in the review period. Additionally, we found that Fintech development contributes to the depletion of sulphur dioxide emissions and has a positive impact on environmental protection investment initiatives. China is poised to be a global leader in green finance policy implementation and regulators need to accelerate the formulation of green finance products and enhance the capacity of financial institutions to offer green credit. While minimizing the systemic risk fintech poses, policy makers should encourage fintechs to actively participate in environmental protection initiatives that promote green consumption.

Muhammad Saeed Meo, M. Karim

Abstract This study examines the relationship between green finance and carbon dioxide (CO2) emissions in the top ten economies that support green finance (Canada, Denmark, Hong Kong, Japan, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States). This study uses quantile on quantile regression (QQR), introduced by Sim and Zhou (2015), to examine the dependence structure between different quantiles of green finance and CO2 emissions. Our overall findings confirm the negative impact of green finance on CO2 emissions; however, this relationship varies across the different quantiles of the two variables. This variation might be due to green finance market conditions (e.g., bearish or bullish) and country-specific market conditions. The findings in the study confirm that green finance is the best financial strategy for reducing CO2 emissions.

R. Mantegna, H. Stanley

Jonathan B. Cohn, Zack Liu, M. I. Wardlaw

J. Stroebel, Jeffrey Wurgler

Abstract We survey 861 finance academics, professionals, and public sector regulators and policy economists about climate finance topics. They identify regulatory risk as the top climate risk to businesses and investors over the next five years, but they view physical risk as the top risk over the next 30 years. By an overwhelming margin, respondents believe that asset prices underestimate climate risks rather than overestimate them. We also tabulate opinions about the expected correlation between growth and climate change, social discount rates appropriate for projects that mitigate the effects of climate change, most influential forces for reducing climate risks, and most important research topics.

Chengchao Lv, B. Bian, Chien‐Chiang Lee et al.

Pengpeng Yue, A. Korkmaz, Zhichao Yin et al.

This study focuses on the impact of digital finance on households. While digital finance has brought financial inclusion, it has also increased the risk of households falling into a debt trap. We provide evidence that supports this notion and explain the channel through which digital finance increases the likelihood of financial distress. Our results show that the widespread use of digital finance increases credit market participation. The broadened access to credit markets increases household consumption by changing the marginal propensity to consume. However, the easier access to credit markets also increases the risk of households falling into a debt trap.

Longbing Cao

AI in finance refers to the applications of AI techniques in financial businesses. This area has attracted attention for decades, with both classic and modern AI techniques applied to increasingly broader areas of finance, economy, and society. In contrast to reviews on discussing the problems, aspects, and opportunities of finance benefited from specific or some new-generation AI and data science (AIDS) techniques or the progress of applying specific techniques to resolving certain financial problems, this review offers a comprehensive and dense landscape of the overwhelming challenges, techniques, and opportunities of AIDS research in finance over the past decades. The challenges of financial businesses and data are first outlined, followed by a comprehensive categorization and a dense overview of the decades of AIDS research in finance. We then structure and illustrate the data-driven analytics and learning of financial businesses and data. A comparison, criticism, and discussion of classic versus modern AIDS techniques for finance follows. Finally, the open issues and opportunities to address future AIDS-empowered finance and finance-motivated AIDS research are discussed.

Weiwei Mu, Kefu Liu, Yunqing Tao et al.

I. Akomea-Frimpong, David Adeabah, Deborah Ofosu et al.

ABSTRACT With growing global concern for environmental protection, climate change and sustainable development, policymakers and researchers have recently focused on green finance. In this study, existing studies on green finance in the context of the banking sector have been reviewed with considerations on products and determinants of green finance. The content analysis approach has been used to critically analyse and summarize forty-six (46) relevant studies. The results found green securities, green investments, climate finance, carbon finance, green insurance, green credit and green infrastructural bonds as part of key green finance products of banks. Pertinent determinants the study found to be influencing green finance policies from banks include environmental and climate change policies, interest rates, religion, risks, social inclusion and social justice as well as banking regulations. In theory, this study provides a guide for further studies. The results of the study will assist banks on the key issues to consider in adopting, developing and granting green finance.

Sam M. Werner, Daniel Perez, L. Gudgeon et al.

Decentralized Finance (DeFi), a blockchain powered peer-to-peer financial system, is mushrooming. Two years ago the total value locked in DeFi systems was approximately 700m USD, now, as of April 2022, it stands at around 150bn USD. The frenetic evolution of the ecosystem has created challenges in understanding the basic principles of these systems and their security risks. In this Systematization of Knowledge (SoK) we delineate the DeFi ecosystem along the following axes: its primitives, its operational protocol types and its security. We provide a distinction between technical security, which has a healthy literature, and economic security, which is largely unexplored, connecting the latter with new models and thereby synthesizing insights from computer science, economics and finance. Finally, we outline the open research challenges in the ecosystem across these security types.

Shamima Ahmed, M. M. Alshater, Anis Ammari et al.

Md Al Mamun, Sabri Boubaker, D. K. Nguyen

B. Almansour, Sabri Elkrghli, A. Almansour

Abstract Modern finance theory assumes that the stock market is efficient, and stock prices reflect all available information. However, behavioral finance theory argues that stock prices can be influenced by psychological and emotional factors. This study aims to examine the impact of behavioral finance factors on investment decisions in the Saudi equity markets through the mediating variable of risk perception. An online questionnaire was distributed to 150 individual investors, out of which 134 were returned and ready for analysis. The data is analyzed using structural equation modeling (SEM). The results show that herding, disposition effect, and blue chip bias have a significant positive impact on risk perception. Overconfidence has a significant positive effect only on investment decision making, but not on risk perception. Risk perception is found to be significantly positively related to investment decision making. All four behavioral finance factors have a significant positive indirect effect on investment decision making through risk perception. This study is conducted in a particular cultural context, namely Saudi Arabia, and may not be generalizable to other cultural contexts. Moreover, this study focused only on four behavioral finance factors, and there may be other factors that could impact risk perception and investment decision making. The results highlight the importance of considering an individual’s perception of risk when making investment decisions, as it can significantly impact their willingness to take risks and ultimately affect the performance of their investment portfolio. The results suggest the need for investors to consider their behavioral biases and for advisors and policymakers to develop strategies to mitigate their impact.