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Evaluating the effectiveness of modern forecasting models in predicting commodity futures prices in volatile economic times

Vancsura, László; Tatay, Tibor; Bareith, Tibor - In: Risks : open access journal 11 (2023) 2, pp. 1-16

The paper seeks to answer the question of how price forecasting can contribute to which techniques gives the most accurate results in the futures commodity market. A total of two families of models (decision trees, artificial intelligence) were used to produce estimates for 2018 and 2022 for 21-...

Persistent link: https://www.econbiz.de/10014233184

Active management of pperational risk in the regimes of the "unknown" : what can machine learning or heuristics deliver?

Milkau, Udo; Bott, Jürgen - In: Risks : open access journal 6 (2018) 2, pp. 1-16

Advanced machine learning has achieved extraordinary success in recent years. “Active” operational risk beyond ex post analysis of measured-data machine learning could provide help beyond the regime of traditional statistical analysis when it comes to the “known unknown” or even the...

Persistent link: https://www.econbiz.de/10011866399

Domain knowledge preservation in financial machine learning : evidence from autocallable note pricing

Ahnouch, Mohammed; Elaachak, Lotfi; Le Saout, Erwan - In: Risks : open access journal 13 (2025) 7, pp. 1-15

Machine learning applications in finance commonly employ feature decorrelation techniques developed for generic statistical problems. We investigate whether this practice appropriately addresses the unique characteristics of financial data, where correlations often encode fundamental economic...

Persistent link: https://www.econbiz.de/10015436793

Integrative analysis of traditional and cash flow financial ratios : insights from a systematic comparative review

Seretidou, Dimitra; Billios, Dimitrios; Stavropoulos, … - In: Risks : open access journal 13 (2025) 4, pp. 1-27

This systematic review analyzes and compares the predictive power between traditional financial ratios and cash flow-based ratios in estimating performance. Although traditional ratios of return on assets and debt to equity have received extensive application, cash flow ratios are increasingly...

Persistent link: https://www.econbiz.de/10015408386

Comparing the effectiveness of machine learning and deep learning models in student credit scoring : a case study in Vietnam

Nguyen Thi Hong Thuy; Nguyen Thi Vinh Ha; Nguyen Nam Trung - In: Risks : open access journal 13 (2025) 5, pp. 1-26

In emerging markets like Vietnam, where student borrowers often lack traditional credit histories, accurately predicting loan eligibility remains a critical yet underexplored challenge. While machine learning and deep learning techniques have shown promise in credit scoring, their comparative...

Persistent link: https://www.econbiz.de/10015408936

Historical perspectives in volatility forecasting methods with machine learning

Qiu, Zhiang; Kownatzki, Clemens; Scalzo, Fabien; Cha, … - In: Risks : open access journal 13 (2025) 5, pp. 1-24

Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital...

Persistent link: https://www.econbiz.de/10015408938

The role of digital financial services in narrowing the gender gap in low-middle-income economies : a Bayesian machine learning approach

Galindo-Manrique, Alicia Fernanda; Rojas-Vargas, Nuria … - In: Risks : open access journal 13 (2025) 5, pp. 1-22

Women in emerging economies face unique constraints rooted in cultural norms, socio-economic disparities, and limited access to education and technology. Narrowing the digital gender gap by ensuring access to financial services may reduce the economic inequalities for women in these countries....

Persistent link: https://www.econbiz.de/10015408940

Financial mechanisms of corporate bankruptcy : are they different or similar across crises?

Tanaka, Katsuyuki; Higashide, Takuo; Kinkyō, Takuji; … - In: Risks : open access journal 13 (2025) 8, pp. 1-25

One primary objective of the early warning system literature is to construct more accurate financial vulnerability prediction models and investigate the mechanisms and key factors that differentiate healthy from vulnerable financial states. Despite the importance of identifying and predicting...

Persistent link: https://www.econbiz.de/10015448878

AI-powered reduced-form model for default rate forecasting

Giacomelli, Jacopo - In: Risks : open access journal 13 (2025) 8, pp. 1-20

This study aims to combine deep and recurrent neural networks with a reduced-form portfolio model to predict future default rates across economic sectors. The industry-specific forecasts for Italian default rates produced with the proposed approach demonstrate its effectiveness, achieving...

Persistent link: https://www.econbiz.de/10015448921

Does ESG predict business failure in Brazil? : An application of machine learning techniques

Mehwish Kaleem; Raza, Hassan; Ashraf, Sumaira; Martins … - In: Risks : open access journal 12 (2024) 12, pp. 1-22

The aim of this study is to explore the influence of environmental, social, and governance (ESG) factors on business … of seven variables that predict business failure. The results indicate that ESG factors significantly predict business … failure in Brazil. This study has implications for investors, policymakers, and business leaders, offering a more precise tool …

Persistent link: https://www.econbiz.de/10015325268

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