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on Payment Systems and Financial Technology |
| By: | Anika Tahsin Meem; Mst. Shapna Akter; Deponker Sarker Depto; M. R. C. Mahdy |
| Abstract: | In modern times, the cryptocurrency market is one of the world's most rapidly rising financial markets. The cryptocurrency market is regarded to be more volatile and illiquid than traditional markets such as equities, foreign exchange, and commodities. The risk of this market creates an uncertain condition among the investors. The purpose of this research is to predict the magnitude of the risk factor of the cryptocurrency market. Risk factor is also called volatility. Our approach will assist people who invest in the cryptocurrency market by overcoming the problems and difficulties they experience. Our approach starts with calculating the risk factor of the cryptocurrency market from the existing parameters. In twenty elements of the cryptocurrency market, the risk factor has been predicted using different machine learning algorithms such as CNN, LSTM, BiLSTM, and GRU. All of the models have been applied to the calculated risk factor parameter. A new model has been developed to predict better than the existing models. Our proposed model gives the highest RMSE value of 1.3229 and the lowest RMSE value of 0.0089. Following our model, it will be easier for investors to trade in complicated and challenging financial assets like bitcoin, Ethereum, dogecoin, etc. Where the other existing models, the highest RMSE was 14.5092, and the lower was 0.02769. So, the proposed model performs much better than models with proper generalization. Using our approach, it will be easier for investors to trade in complicated and challenging financial assets like Bitcoin, Ethereum, and Dogecoin. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13615 |
| By: | Toncheva, Rossitsa |
| Abstract: | Money, as the quantitative representative of almost everything, decently occupies the central position in the distribution mechanism and as an instrument for imposing order. At the stage where global solutions are sought to adapt the orthodox monetary system to the current productive forces, many opportunities are being opened for testing new forms of social models, as one could also call that of "private non-bank money". The term "private non-bank money" is a conceptual successor to the term of "barter money" . Both are formal, synthetic words created as a tool to get better sense of the pretty wide variety of monetary experiments with complementary currency systems (ССS), not only as practical cases but also as theoretical interpretations. The use of new term partly solves not only the above limitation, but also the confusion resulting from the fact that "money" and "currency" are often perceived as synonymous. "Money" is defined and perceived with too wide a scope, which can even give rise to a cognitive fallacy. Regardless of the predominantly social characteristics of ССS, all of them, except for the time banks, have a powerful financial feature, and it is the monetary instrument that places them also in the field of monetary theory. The search for a common distinguishing property of the CCS has led to the need to include a new concept, by which the difficulty is largely removed. The concept of "private non-bank money" is instrumental, and as such it represents a relatively more limited notion of money - as a specific form of it, suitable for understanding the meaning, content and significance of the relations that have been created in the various models of CCS. |
| Keywords: | Private Non-bank Money, Distribution, Money, complementary currency |
| JEL: | E40 E50 G2 P40 |
| Date: | 2024–11–08 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:122689 |
| By: | Tong Pu; Yunran Wei; Yiying Zhang |
| Abstract: | We introduce a novel class of systemic risk measures, the Vulnerability Conditional risk measures, which try to capture the "tail risk" of a risky position in scenarios where one or more market participants is experiencing financial distress. Various theoretical properties of Vulnerability Conditional risk measures, along with a series of related contribution measures, have been considered in this paper. We further introduce the backtesting procedures of VCoES and MCoES. Through numerical examples, we validate our theoretical insights and further apply our newly proposed risk measures to the empirical analysis of cryptocurrencies, demonstrating their practical relevance and utility in capturing systemic risk. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.09676 |
| By: | YUVRAJ SUNECHER (UNIVERSITY OF TECHNOLOGY MAURITIUS); Mevin Luchoo (University of Technology Mauritius) |
| Abstract: | This study investigates the degree of financial awareness and literacy in Mauritius. A survey was conducted to find out about the population's understanding of financial products, investment alternatives, borrowing, saving, investing, and financial abilities. The population's degree of savings knowledge is high, whereas their understanding of general finance, investments, and insurance is low to average, according to the study's conclusion. This study also looks into the population's financial literacy and awareness as well as the steps that the appropriate authorities should take to make sure that people are taught not only how to budget and save money but also how to invest in assets, protect their finances, and?most importantly?how to manage their money sensibly by forming good financial habits. |
| Keywords: | Financial Literacy, Population, Awareness, Mauritius |
| URL: | https://d.repec.org/n?u=RePEc:sek:iefpro:14716502 |
| By: | Dawei Cheng; Yao Zou; Sheng Xiang; Changjun Jiang |
| Abstract: | The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting and managing financial fraud. This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the suitability of GNNs for financial fraud detection, their deployment in real-world scenarios, and the design considerations that enhance their effectiveness. This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks, significantly outperforming traditional fraud detection methods. Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially, our review provides a comprehensive, structured analysis, distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection. This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems. Through a structured review of over 100 studies, this review paper contributes to the understanding of GNN applications in financial fraud detection, offering insights into their adaptability and potential integration strategies. |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.05815 |
| By: | Tian Sisi (Data Analyst) |
| Abstract: | With the rapid development of information technology and the deepening of global economic integration, the digital economy has become an important engine for driving global economic growth. However, due to differences in resource endowments, economic foundations, policy support and other factors in different regions, the development level of the digital economy shows obvious spatial heterogeneity in different regions. Therefore, it is necessary and of practical signi |
| Date: | 2024–10–03 |
| URL: | https://d.repec.org/n?u=RePEc:boc:chin24:13 |
| By: | Godwill Chimamiwa |
| Abstract: | This paper presents a critical analysis of current cyber risk management practices in light of new and evolving Artificial Intelligence (AI) and Machine Learning driven adversarial attacks. Many enterprises are constantly grappling with cybersecurity risks and increased threats from phishing, ransomware, and other forms of cyber-attacks, often resulting in substantial financial losses when risks are not adequately addressed. With the advent of AI and ML, such cyber-attacks and incidents are expected to become more prevalent and potentially more devastating to businesses of all sizes. With AI and ML tools at their disposal, cybercriminals can significantly reduce technical barriers to launching cyberattacks. They can easily develop more sophisticated social engineering tactics and "deep fakes" that are challenging to identify, thereby increasing the risks of unauthorized data disclosure. Drawing on a literature review analysis, this research explores current and emerging AI- and ML-driven cyber threats faced by enterprises, assesses the effectiveness of current cyber mitigation measures, and discusses future management practices to enhance the security posture of enterprises. The study evaluates both technical and non-technical cyber risk management and mitigation measures and frameworks. The findings from this study aim to inform enterprise cyber risk managers and practitioners about the enormity of AI- and ML-driven cyber risks and present emerging best practices to adequately mitigate those risks. This study contributes to the growing body of research on how threat actors leverage AI and ML to expand cyber threats and how enterprises and organizations should respond to these ever-evolving cyber risks. |
| Keywords: | cyber risk management, AI-driven, ML-driven, adversarial attacks, cyber risk frameworks |
| Date: | 2024–10 |
| URL: | https://d.repec.org/n?u=RePEc:bfv:sbsrec:006 |
| By: | Shuoling Liu; Gaoguo Jia; Yuhang Jiang; Liyuan Chen; Qiang Yang |
| Abstract: | Large Language Models (LLMs) have achieved remarkable success recently, displaying exceptional capabilities in creating understandable and organized text. These LLMs have been utilized in diverse fields, such as clinical research, where domain-specific models like Med-Palm have achieved human-level performance. Recently, researchers have employed advanced prompt engineering to enhance the general reasoning ability of LLMs. Despite the remarkable success of zero-shot Chain-of-Thoughts (CoT) in solving general reasoning tasks, the potential of these methods still remains paid limited attention in the financial reasoning task.To address this issue, we explore multiple prompt strategies and incorporated semantic news information to improve LLMs' performance on financial reasoning tasks.To the best of our knowledge, we are the first to explore this important issue by applying ChatGPT to the gold investment.In this work, our aim is to investigate the financial reasoning capabilities of LLMs and their capacity to generate logical and persuasive investment opinions. We will use ChatGPT, one of the most powerful LLMs recently, and prompt engineering to achieve this goal. Our research will focus on understanding the ability of LLMs in sophisticated analysis and reasoning within the context of investment decision-making. Our study finds that ChatGPT with CoT prompt can provide more explainable predictions and overcome behavioral biases, which is crucial in finance-related tasks and can achieve higher investment returns. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.13599 |
| By: | Ritam Chaurey; Ryan Kim; Pravin Krishna |
| Abstract: | This paper examines how liquidity shocks caused by currency shortages impact exports. We explore this in the context of India’s 2016 currency demonetization, a sudden and unexpected policy announcement by the government that large-denomination currency notes—comprising 86% of the country’s currency in circulation—would cease to be legal tender within hours. Our analysis uses novel data, including high-frequency customs transaction records matched with exporting firms and their balance sheets, as well as with inter-district domestic trade. We develop direct measures of exporting firms’ exposure to cash shortages and indirect measures that act through domestic supply chain networks. While the cash shortages do not directly affect exporting firms, we find a significant and immediate decline in real exports for firms whose domestic customers experience liquidity shocks. These findings underscore the critical role of domestic supply chains in transmitting liquidity shocks to exports. |
| JEL: | E50 F1 F14 O16 |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:33142 |
| By: | Aleksandr Simonyan |
| Abstract: | This paper introduces BreakGPT, a novel large language model (LLM) architecture adapted specifically for time series forecasting and the prediction of sharp upward movements in asset prices. By leveraging both the capabilities of LLMs and Transformer-based models, this study evaluates BreakGPT and other Transformer-based models for their ability to address the unique challenges posed by highly volatile financial markets. The primary contribution of this work lies in demonstrating the effectiveness of combining time series representation learning with LLM prediction frameworks. We showcase BreakGPT as a promising solution for financial forecasting with minimal training and as a strong competitor for capturing both local and global temporal dependencies. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.06076 |