Risks MDPI

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✔️ A Comparison of Risk Willingness Between Same‑Sex and Different‑Sex Couples: A Quasi‑Experimental Approach

👉 https://brnw.ch/21x2VBe

✍️ by Matthew Jaramillo, Donald Lacombe, Leobardo Diosdado and
Laura Ricaldi, Ph.D., CFP®

Using Survey of Consumer Finances data and propensity score matching, this study finds same‑sex couples report significantly higher financial risk tolerance than different‑sex couples. Findings highlight the importance of recognizing household diversity and avoiding homogeneous risk preference assumptions across household types in financial planning.

A Comparison of Risk Willingness Between Same-Sex and Different-Sex Couples: A Quasi-Experimental Approach Household composition in the United States is increasingly diverse; however, research into the diversity of the financial decision maker’s sexual orientation has yet to be explored. This analysis examines whether there are differences in financial risk tolerance between same-sex and different-sex ...

✨ Sharing on 29 May

🎯 Title: Dynamic Portfolio Optimization with Diversification Analysis and Asset Selection Amidst High Correlation Using Cryptocurrencies and Bank Equities

👉 https://brnw.ch/21x2VjU

✍ by Hamdan Bukenya Ntare et al

This study evaluates cryptocurrency diversification benefits in South African bank equity portfolios (2017–2024). Using multi-asset particle swarm optimizer (MA-PSO) , random optimizer, and equal-weighted models, the authors find dynamic models outperform static approaches. Key insight: cryptocurrencies' pre-COVID hedge role has attenuated post-pandemic. MA-PSO emerges as optimal for diversified portfolios. South African managers must carefully assess risk tolerance.

Dynamic Portfolio Optimization with Diversification Analysis and Asset Selection Amidst High Correlation Using Cryptocurrencies and Bank Equities There has been growing interest among investors to include cryptocurrencies in their portfolios because of their diversification potential. However, the diversification role of cryptocurrencies when added to South African bank equities is yet to be determined. This study rigorously evaluates asset c...

🎯 – Welcoming Our Distinguished Committee Member

We are pleased to welcome Dr. Shengkun Xie from the Ted Rogers School of Management, Toronto Metropolitan University (formerly Ryerson University), Toronto, Canada, to the IOCR2026 Scientific Committee.

Dr. Xie’s research focuses on statistical machine learning, explainable data analytics, risk modeling, rate making, multivariate statistical methods, time series analysis, and predictive analytics. He serves as a committee member and reviewer for S5. Emerging Risks and Interdisciplinary Topics.

📅Key Dates
Registration Deadline: 1 July 2026

🔗Register FREE Now: https://brnw.ch/21x2UYd
🔍Learn More: https://brnw.ch/21x2UYc

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✔️ The Paradox of Cyber Risk Controls: An Empirical Analysis of Readiness and Protection Inefficiencies in Thailand’s Financial Sector

👉 https://brnw.ch/21x2TW7

✍️ by Artid Sringam and Pongpisit Wuttidittachotti

Surveying 53 Thai financial practitioners, this study uncovers a “Protection Paradox”: over‑engineered technical controls negatively impact readiness (β = −0.432), while Identification drives readiness (β = 0.627). A structural disconnect between Governance and Third‑Party Risk Management highlights supply chain vulnerabilities, urging a shift from compliance to risk‑optimized integration.

The Paradox of Cyber Risk Controls: An Empirical Analysis of Readiness and Protection Inefficiencies in Thailand’s Financial Sector As Thailand’s financial sector accelerates its digital transformation, cybersecurity has transitioned from a mere technical support function to a strategic imperative that governs operational risk and financial stability. This study empirically examines the efficacy of cyber risk controls and thei...

✨ Sharing on 28 May

🎯 Title: Advancing Credit Rating Prediction: The Role of Machine Learning in Corporate Credit Rating Assessment

👉 https://brnw.ch/21x2TIA

✍ by Nazário Augusto de Oliveira and Leonardo Fernando Cruz Basso

This study evaluates multiple ML models for corporate credit rating prediction using a seven-year dataset from S&P Capital IQ Pro (20 countries, 51 risk variables). Results show Artificial Neural Networks and Gradient Boosting consistently outperform traditional approaches, particularly in capturing non-linear relationships. A practical advancement for financial institutions seeking more accurate, scalable credit risk assessment frameworks.

🙌 CallforReading - Closed Special Issue 📚: Stochastic Modelling in Financial Mathematics, 2nd Edition

with 19,682 views

Guest Editor: Prof. Dr. Anatoly Swishchuk, University of Calgary

Featured articles:
✅ Entropic Geometry and Information Dynamics in Green Cryptocurrency Markets
✅ Evaluation and Prediction of in Mexico Using Log-Periodic Power-Law Modeling
✅ From Stochastic Orders to Surfaces: Revisiting the One-X Property
✅ Low Financial Risk of and Productive Use of Through Hidden Markov Models
✅ Minimal Entropy and Entropic : A Unified Framework via Relative Entropy
✅ Pricing of Averaged Variance, Volatility, Covariance and Correlation Swaps with Semi-Markov
✅ The SEV-SV Model—Applications in

🔗 Read the full special issue: https://brnw.ch/21x2TqG

🏆 Sharing on 27 May


🎯 Title: Gaussian Process Regression with a Hybrid Risk Measure for Dynamic Risk Management in the Electricity Market

👉 https://brnw.ch/21x2RIy

✍ by Abhinav Das and Stephan Schlüter

This study introduces a hybrid risk management strategy combining VaR and CVaR to optimize electricity procurement under Germany's dynamic tariff environment (Energiewende). Using Gaussian process regression for hourly price forecasting, the approach enables consumers and traders to make cost-effective, risk-aware decisions while contributing to a sustainable, flexible energy market aligned with Germany's Renewable Energy Act.

🎉 🎉 🎉 We are delighted to have two New Editorial Board members on board!

🎊 Prof. Dr. Giuseppe Torluccio (in banking management, credit risk, risk management, corporate finance), University of Bologna, Italy

🎊 Prof. Dr. Cetin Ciner (in international finance, commodity markets, financial analytics, equity market), University of North Carolina Wilmington, USA

✨ Sharing on 26 May


🎯 Title: The Role of Digital Financial Services in Narrowing the Gender Gap in Low–Middle-Income Economies: A Bayesian Machine Learning Approach

👉 https://brnw.ch/21x2PRq

✍ by Dra. Alicia Fernanda Galindo Manrique and Nuria Patricia Rojas-Vargas

This study examines how digital financial services can reduce the gender gap across 73 low and lower-middle-income economies (2011–2022) using Bayesian regression and the Global Findex Database. Key finding: digital financial services significantly narrow the gender gap in low-income economies by increasing account ownership for women. The analysis considers mobile money, digital payments, internet access, and savings. Relevant to UN SDG 5 (Gender Equality) and SDG 10 (Reduced Inequalities).

🙌 Be part of the 1st International Online Conference on Risks and connect with researchers, professionals, and experts from across the globe.

✨ Here’s why you shouldn’t miss it:
• Gain insights from internationally recognized keynote and invited speakers
• Discover the latest research in risk management, insurance, finance, and actuarial science
• Explore engaging discussions across 5 specialized thematic areas
• Connect and exchange ideas with participants worldwide
• Enjoy free registration and fully online participation

📅 Dates: 6–7 July 2026
🔗 Register for free: https://brnw.ch/21x2PH9

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✔️ Human‑AI Synergy in Statistical Arbitrage: Enhancing Robustness Across Volatile Financial Markets

👉 https://brnw.ch/21x2JfB

✍️ by Binxu Lei, Southwestern University of Finance and Economics

This study reviews the evolution of ML‑driven statistical arbitrage and proposes a conceptual human‑in‑the‑loop framework integrating signal modeling with structured oversight—risk calibration, discretionary intervention, and interpretability review. The framework suggests collaborative systems may enhance resilience over purely AI‑driven alternatives during market stress.