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A Special Issue of Risks (ISSN 2227-9091).

Deadline for manuscript submissions: 31 October 2026 | Viewed by 7957

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Editors

Dr. Difang Huang

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Guest Editor

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China
Interests: risk management; behavioral finance; experimental economics; financial intermediation; human-algorithm interaction; decision science

Prof. Dr. Jue Wang

E-Mail Website
Guest Editor

1. AMSS Center for Forecasting Science, Chinese Academy of Sciences, Beijing, China
2. University of Chinese Academy of Sciences, Beijing, China
Interests: economic forecasting; machine learning; finance risk management; decision analysis; intelligent computing; operational optimization

Special Issue Information

Dear Colleagues,

Rapid advances in artificial intelligence, machine learning, and data science are reshaping the landscape of financial econometrics and modern risk management. Financial markets now generate unprecedented volumes of granular, high‑frequency, and unstructured data, while institutions face increasingly complex risks arising from geopolitical uncertainty, climate transitions, digital assets, algorithmic trading, and systemic interconnectedness. Traditional econometric models, though foundational, are often insufficient for fully capturing nonlinear dynamics, real‑time dependencies, and structural breaks that characterize today’s financial systems.

This Special Issue aims to bring together cutting‑edge theoretical, methodological, and empirical research at the intersection of AI technologies and financial risk analysis. We welcome contributions that develop novel econometric models enhanced by machine learning, explore data‑driven approaches to risk measurement, or apply AI tools to credit risk, market risk, systemic risk, insurance analytics, and portfolio optimization. Studies integrating the interpretability, robustness, fairness, and regulatory implications of AI-driven modeling are particularly encouraged. Both methodological innovations and high‑impact practical applications are welcome. By bridging financial econometrics with modern AI methodologies, this Special Issue seeks to advance the next generation of risk modeling tools and promote informed decision‑making in increasingly complex financial environments.

Dr. Difang Huang
Prof. Dr. Jue Wang
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Risks is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

financial econometrics
machine learning
artificial intelligence
risk management
systemic risk
volatility modeling
credit and market risk
high frequency data
model uncertainty and robustness
data analytics in finance

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Published Papers (7 papers)

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Research

38 pages, 2489 KB

Open AccessArticle

Model Risk Audit of a Barrier-Triggered Trading System: What Machine Learning Adds Under Coverage Adjustment

by Kamil Staněk, Michal Ježek, Oldřich Trenz, Jakub Kůdela, Michal Dufek and Jiří Šťastný

Risks 2026, 14(10), 238; https://doi.org/10.3390/risks14100238 - 14 Oct 2026

Abstract

This paper proposes a diagnostic audit framework that extends perfect-foresight benchmarking. It places paired ex post oracles, for the original training label and the realized economic outcome, on a fixed population of trading opportunities with a common coverage-adjusted denominator. This construction was not identified within the scope of the search conducted. It is applied to a barrier-triggered FX contract (stop-loss 1.5, take-profit 2.0 times ATR, 336-bar time limit) on 15,476 opportunities across eleven major currency pairs, 2017–2022. The realized system’s win rate is 2.6439 percentage points below its breakeven threshold after cost, distinguishable from sampling noise across 192 tested settings of barriers, time limit and cost, narrowly at zero cost and with timed-out trades outside the threshold at short time limits. The ex post oracles reach a substantially higher value per opportunity, quantified rather than treated as a target for deployable models. A forward-evaluated model, tested on a later-starting subset of 10,268 opportunities, had negative value per opportunity under all three registered targets. The audit documents a measurable mismatch between this contract’s original training label and realized execution outcome. All results are diagnostic measurements within the training period. A registered confirmatory evaluation on held-out 2024 data has not been performed. Full article

(This article belongs to the Special Issue AI-Driven Financial Econometrics and Risk Management)

47 pages, 11565 KB

Open AccessArticle

Artificial Intelligence for Earnings-Based Market Risk Analysis: A Machine Learning Framework for Post-Earnings Announcement Drift

by Mazin Alahmadi, Rimal Abutaha, Talia Kinsara, Danah Almansour, Lama Sindi and Majd Bakhsh

Risks 2026, 14(10), 230; https://doi.org/10.3390/risks14100230 - 5 Oct 2026

Viewed by 244

Abstract

Financial markets react continuously to corporate disclosures, yet stock prices may not adjust immediately to new information. One of the most widely studied anomalies associated with post-announcement return behavior is post-earnings announcement drift (PEAD), in which stock prices continue to move in the direction of earnings surprises after the announcement date. Although PEAD has been extensively examined in developed markets, limited research has investigated post-earnings announcement return behavior in emerging markets, particularly in the Saudi stock market. Existing studies largely rely on traditional statistical approaches or focus on developed-market datasets, leaving a gap in understanding how artificial intelligence (AI)-driven machine learning techniques can support financial risk analysis and data-driven financial econometrics by classifying post-earnings announcement return behavior across different firm sizes and post-announcement horizons. To address this gap, this study develops an AI-driven financial risk analytics framework for classifying operational PEAD-related post-announcement return observations using financial and technical indicators derived from companies listed on the Saudi Stock Exchange (Tadawul). The analysis utilizes five years of historical data (2020–2025) from 60 firms equally distributed across small-cap, mid-cap, and large-cap categories. Cumulative return is employed as the response variable and evaluated over three forward post-announcement horizons of 5, 21, and 30 trading days. Five supervised learning models, namely Logistic Regression, Random Forest, Gradient Boosting, XGBoost, and CatBoost, are trained and evaluated using standard classification metrics. The results show that operational PEAD-related return classifications are observed across all market-capitalization categories, with the proportion of observations assigned to the high-return class ranging from 20.55% to 23.30%. These proportions arise from our study’s predefined upper-quartile return-based classification criterion and therefore should not be interpreted as estimates of the prevalence or persistence of classical PEAD. Classification performance varies across market segments and post-announcement horizons. Because the upper-quartile threshold produces an inherently imbalanced classification problem, accuracy results are interpreted alongside class-specific performance and F1 scores rather than in isolation. CatBoost achieved the highest reported accuracy of 77.8% at the 21-day horizon for large-cap firms, while also delivering the highest overall-market accuracy of 76.9% at the 30-day horizon. These results indicate that the selected financial and technical indicators contain information useful for classifying the predefined high-return post-announcement observations, although the reported accuracy levels should not be interpreted independently of the majority-class baseline. The proposed framework contributes to AI-enabled financial econometrics by providing a data-driven approach for classifying post-announcement return behavior and supporting market risk assessment, investor decision-making, earnings-based trading strategies, and market monitoring in emerging financial markets. Full article

(This article belongs to the Special Issue AI-Driven Financial Econometrics and Risk Management)

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16 pages, 4982 KB

Open AccessArticle

Estimation and Analysis of Trade-Based Money Laundering Using Mirror Trade Data: The Case of South Africa

by William Gaviyau and Jethro Godi

Risks 2026, 14(9), 218; https://doi.org/10.3390/risks14090218 - 18 Sep 2026

Viewed by 290

Abstract

Living in a globalised economy, opportunities arise for cross-border trade. As cross-border trading activities increase, they give rise to trade-based money laundering (TBML). Despite trade growth, TBML continues to be prevalent and not widely recognised compared to other traditional forms of money laundering. Estimation of money laundered globally remains problematic. Therefore, this study estimated and analysed TBML by applying mirror trade data for South Africa (SA) trading with Africa and the European Union (EU). To assist historical quantitative quarterly time series, mirror trade data were gathered covering the period of 2011 to 2025. The findings reveal that the magnitude of SA’s TBML to Africa was highly volatile, while the magnitude of SA’s TBML to the European Union indicated that SA uses EU trade channels for receiving illicit flows into the country. Comparatively, analyses of the structural patterns and trends of SA’s TBML with Africa versus that with the EU over the study period revealed large structural differences, which is a characteristic feature of the two regional economic trading blocs. In conclusion, TBML for SA–Africa was mainly carried out through import over-invoicing, while TBML for SA–EU was carried out through export over-invoicing. The findings contribute to filling the knowledge and practice gaps associated with TBML estimation. Full article

(This article belongs to the Special Issue AI-Driven Financial Econometrics and Risk Management)

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25 pages, 5023 KB

Open AccessArticle

Task-Aware Exchange Rate Forecasting: A Unified Empirical Framework for Level, Return, and Volatility Prediction

by Shamaila Butt, Muhammad Ali Chohan, Mohammad Abrar, Karima Sayari and Shahid Kamal

Risks 2026, 14(9), 207; https://doi.org/10.3390/risks14090207 - 8 Sep 2026

Viewed by 265

Abstract

Exchange rate forecasting is a core issue in empirical finance, but in the majority of studies, the impact of target construction, feature representation, and model structure is not disaggregated, and the evaluation is confined to one prediction problem. The three tasks are: (1) [...] Read more.

E R_{t + 1}

; (2) prediction of

r_{t + 1} = E R_{t + 1} - E R_{t}

; and (3) prediction of future volatility defined as the sample standard deviation of

r_{t + 1}, \dots, r_{t + 10}

, where the horizon is the next 10 recorded observations. The seven model families are evaluated with a strict chronological 70%/15%/15% split;

E R

is excluded as a direct raw predictor, deep models use seeds 7, 42, and 123, and task-specific financial benchmarks are included. The findings reveal markedly different out-of-sample forecasting behavior across the three tasks. For level prediction, the no-change random walk gives RMSE = 0.0005923 and

R^{2} = 0.99815

, while Linear Regression gives RMSE = 0.0005939 and

R^{2} = 0.99814

; the difference is not significant (DM = 0.480,

p = 0.631

). For return prediction, Linear Regression gives RMSE = 0.0005920 and

R^{2} = - 0.00061

and does not significantly outperform the zero-return benchmark (DM =

- 0.083

p = 0.934

). For future volatility, the best average finance-aware learned model is FeatureAttention_Only (RMSE =

0.0004407 \pm 0.0000180

;

R^{2} = - 0.027 \pm 0.085

), and its three-seed ensemble gives RMSE = 0.0004298 and

R^{2} = 0.024

. FeatureAttention_Only improves squared-error loss relative to EWMA and GARCH in the fixed hold-out, but is not significantly superior to GARCH under QLIKE; rankings also vary across forecast origins. Full article

(This article belongs to the Special Issue AI-Driven Financial Econometrics and Risk Management)

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18 pages, 1473 KB

Open AccessArticle

Dual-Parameter Extensions of the Cat-in-a-Grid Approach for Tropical Cyclone Parametric Insurance

by Wenwen Chen, Marc Escoto, Roberto Guidotti, Guillermo Franco, Angel A. Juan and Laura Lemke-Verderame

Risks 2026, 14(9), 196; https://doi.org/10.3390/risks14090196 - 29 Aug 2026

Viewed by 342

Abstract

Parametric insurance products for tropical cyclone risk transfer typically rely on maximum wind speed (MWS) as the only trigger parameter. While interpretable and widely used, it nevertheless leaves a substantial portion of loss variance unexplained. This paper proposes and evaluates two dual-parameter extensions of the cat-in-a-grid parametric framework, combining MWS with either the radius of maximum wind speed (RMW) or minimum central pressure (MCP). Two integration strategies are examined: a stratified approach, in which events are partitioned by the secondary parameter and separate MWS-based loss functions are fitted within each stratum, and a multivariable approach, in which both parameters enter a bivariate polynomial loss function directly without event partitioning. Four dual-parameter configurations are evaluated against a single-metric MWS baseline using 5-fold cross-validation (CV) on a large stochastic catalog for Jamaica, across four training data strategies. Results show that RMW improves predictive accuracy over the MWS baseline under most training strategies, although the multivariable extension underperforms the baseline under binned training. MCP, by contrast, offers only a marginal benefit that disappears under binned training, consistent with its strong correlation with MWS. In addition, results also show that multivariable models require IQR-filtered raw training to realize their potential, while stratified models perform best with binned training. Full article

(This article belongs to the Special Issue AI-Driven Financial Econometrics and Risk Management)

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24 pages, 1601 KB

Open AccessArticle

Deep Reinforcement Learning for Cryptocurrency Portfolio Management: A Free-Energy Framework with Geometry-Based Transaction Costs and Efficiency Bounds

by Ntebogang Dinah Moroke

Risks 2026, 14(5), 103; https://doi.org/10.3390/risks14050103 - 2 May 2026

Cited by 6 | Viewed by 2205

Abstract

This paper develops a deep reinforcement learning framework for cryptocurrency portfolio management in which transaction costs are derived from the Riemannian geometry of the underlying volatility model rather than assumed constant. A Proximal Policy Optimisation agent is trained on a reward function grounded in non-equilibrium thermodynamics: we use the free-energy Bellman equation, in which transaction costs are the geodesic slippage on the Fisher information manifold of a maximum-entropy Markov-switching GARCH model, and regime-transition costs are the Wasserstein-2 distance between the calm and turbulent return distributions. A thermodynamic Carnot bound on portfolio efficiency is established and empirically validated. Five hypotheses are tested across Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash over January 2017 to March 2026. The geometric-cost agent achieves statistically superior Sharpe ratios relative to flat-fee baselines on four of five assets; portfolio turnover is reduced by 56 to 83 percent relative to signal-following; the thermodynamic friction point at which the agent prefers no-trade is asset-specific and ordered by turbulent half-life; a joint topological and geometric circuit breaker reduces Maximum Drawdown by 28 to 38 percent; and ablation confirms that every component of the observation vector contributes a statistically significant performance gain. The framework requires liquid cryptocurrency markets with validated parametric volatility models; transferability to other asset classes requires upstream recalibration. Full article

(This article belongs to the Special Issue AI-Driven Financial Econometrics and Risk Management)

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27 pages, 1070 KB

Open AccessArticle

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

by Binxu Lei

Risks 2026, 14(3), 63; https://doi.org/10.3390/risks14030063 - 12 Mar 2026

Viewed by 3841

Abstract

This study provides a structured review of statistical arbitrage research in the context of artificial intelligence, with a particular focus on machine learning based methods. The reviewed literature highlights the evolution from linear, rule-based strategies to increasingly complex data-driven models, while also documenting persistent challenges related to tail-risk exposure, regime instability, limited interpretability, and regulatory and governance constraints in practical applications. Building on this literature synthesis, the paper develops a conceptual AI-led, human-in-the-loop statistical arbitrage framework that integrates ML-generated signal modeling with structured human oversight—encompassing risk calibration, discretionary intervention, and interpretability review. This framework resonates with human-AI collaboration systems across other financial domains, collectively supporting the proposition that collaborative systems show potential to enhance resilience compared to purely AI-driven alternatives under specific market stress scenarios. It is positioned as a governance-oriented synthesis that qualitatively extends existing human-in-the-loop concepts by structurally embedding adaptive oversight within the statistical arbitrage decision architecture. Full article

(This article belongs to the Special Issue AI-Driven Financial Econometrics and Risk Management)

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