FinTech

22 pages, 1447 KB

Open AccessArticle

Collapse of Silicon Valley Bank and USDC Depegging: A Machine Learning Experiment

by Papa Ousseynou Diop, Julien Chevallier and Bilel Sanhaji

FinTech 2024, 3(4), 569-590; https://doi.org/10.3390/fintech3040030 - 13 Dec 2024

Cited by 1 | Viewed by 12146

The collapse of Silicon Valley Bank (SVB) on 11 March 2023, and the subsequent depegging of the USDC stablecoin highlighted vulnerabilities in the interconnected financial ecosystem. While prior research has explored the systemic risks of stablecoins and their reliance on traditional banking, there has been limited focus on how banking sector shocks affect digital asset markets. This study addresses this gap by analyzing the impact of SVB’s collapse on the stability of major stablecoins—USDC, DAI, FRAX, and USDD—and their relationships with Bitcoin and Tether. Using daily data from October 2022 to November 2023, we found that the SVB incident triggered a series of depegging events, with varying effects across stablecoins. Our results indicate that USDC, often viewed as one of the safer stablecoins, was particularly vulnerable due to its reliance on SVB reserves. Other stablecoins experienced different impacts based on their collateral structures. These findings challenge the notion of stablecoins as inherently safe assets and underscore the need for improved risk management and regulatory oversight. Additionally, this study illustrates how machine learning models, including gradient boosting and random forests, can enhance our understanding of financial contagion and market stability. Full article

► Show Figures

Figure 1

18 pages, 1944 KB

Open AccessArticle

Comparative Analysis of Deep Learning Models for Stock Price Prediction in the Indian Market

by Moumita Barua, Teerath Kumar, Kislay Raj and Arunabha M. Roy

FinTech 2024, 3(4), 551-568; https://doi.org/10.3390/fintech3040029 - 28 Nov 2024

Cited by 4 | Viewed by 11428

Abstract

This research presents a comparative analysis of various deep learning models—including Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Attention LSTM—in predicting stock prices of major companies in the Indian stock market, specifically HDFC, TCS, ICICI, Reliance, and Nifty. The study evaluates model performance using key regression metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-Squared (R²). The results indicate that CNN and GRU models generally outperform the others, depending on the specific stock, and demonstrate superior capabilities in forecasting stock price movements. This investigation provides insights into the strengths and limitations of each model while highlighting potential avenues for improvement through feature engineering and hyperparameter optimization. Full article

► Show Figures

Figure 1

14 pages, 1431 KB

Open AccessArticle

Using Precious Metals to Reduce the Downside Risk of FinTech Stocks

by Perry Sadorsky

FinTech 2024, 3(4), 537-550; https://doi.org/10.3390/fintech3040028 - 25 Oct 2024

Viewed by 1804

Abstract

FinTech stocks are an important new asset class that reflects the rapidly growing FinTech sector. This paper studies the practical implications of using gold, silver, and basket-of-precious-metals (gold, silver, platinum, palladium) ETFs to diversify risk in FinTech stocks. Downside risk reduction is estimated using relative risk ratios based on CVaR. The analysis shows that gold provides the most downside risk protection. For a 5% CVaR, a 30% portfolio weight for gold reduces the downside risk by about 25%. The minimum variance and minimum correlation three-asset (FinTech, gold, and silver) portfolios (with portfolio weights estimated using a TVP-VAR model) have the highest risk-adjusted returns (Sharpe ratio, Omega ratio) followed by the fixed-weight FinTech and gold portfolio. These results show the benefits of diversifying an investment in FinTech stocks with precious metals. These results are robust to weekly or monthly portfolio rebalancing and reasonable transaction costs. Full article

(This article belongs to the Special Issue Trends and New Developments in FinTech)

► Show Figures

Figure 1

41 pages, 3188 KB

Open AccessArticle

Financial Stability and Innovation: The Role of Non-Performing Loans

by Massimo Arnone, Alberto Costantiello, Angelo Leogrande, Syed Kafait Hussain Naqvi and Cosimo Magazzino

FinTech 2024, 3(4), 496-536; https://doi.org/10.3390/fintech3040027 - 14 Oct 2024

Cited by 2 | Viewed by 7173

Abstract

This study analyses the relationship between non-performing loans (NPLs) and innovation systems at a global level. The data were obtained from the World Bank and the Global Innovation Index over the period 2013–2022 for 149 countries. The k-means algorithm was used to verify the presence of clusters in the data. Since k-means is an unsupervised machine-learning algorithm, we compared the Silhouette coefficient with the Elbow method to find an optimization. The results show that the optimal number of clusters is three, as suggested using the Elbow Method. Furthermore, a panel data analysis was conducted. Results show that the level of NPLs is positively associated with cultural and creative services exports as a percentage of total trade and innovation input sub-index and negatively associated with the Hirsch Index, ICT services exports as a percentage of total trade, ICT services imports as a percentage of total trade, and information and communication technologies. Full article

► Show Figures

Figure 1

Journal Menu

Journal Browser

FinTech, Volume 3, Issue 4 (December 2024) – 4 articles

Further Information

Guidelines

MDPI Initiatives

Follow MDPI