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Forecasting
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Advanced Forecasting in an Era of Uncertainty and Its Impact...
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Advanced Forecasting in an Era of Uncertainty and Its Impact on Strategic Investment Decisions

A special issue of Forecasting (ISSN 2571-9394).

Deadline for manuscript submissions: 30 November 2026 | Viewed by 10520

Editors

Dr. Marek Nagy

E-Mail Website
Guest Editor
Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
Interests: AI-driven finance; corporate finance; business; management; digitalization; behavioral finance
Dr. Katarina Valaskova

E-Mail Website
Guest Editor
Department of Economics, Faculty of Operation and Economics of Transport and Communications, University of Zilina, Univerzitna 1, 010 26 Zilina, Slovakia
Interests: international financial markets; behavioural decision making; risk modeling; risk analysis; financial statistics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the context of global volatility and rapid technological change, advanced forecasting has become indispensable for informed decision-making in finance and economics. The increasing uncertainty in financial markets demands innovative approaches that go beyond conventional econometric tools. The integration of artificial intelligence (AI), machine learning and predictive modeling has opened new frontiers in financial analysis, enabling researchers and practitioners to enhance financial forecasting, optimize strategic investments and anticipate potential crises such as bankruptcy prediction.

The transformative role of AI-powered finance lies in its ability to uncover complex interdependencies within large-scale data environments. Beyond numerical data, emerging methods in investor sentiment modeling capture behavioral and psychological dimensions of market dynamics, linking algorithmic prediction with behavioral finance insights. This multidimensional approach allows for a deeper understanding of how rational and emotional factors jointly shape investment behavior under uncertainty.

The Special Issue “Advanced Forecasting in an Era of Uncertainty and Its Impact on Strategic Investment Decisions” aims to bring together cutting-edge research that explores the synergy between intelligent forecasting techniques and strategic financial decision-making. It welcomes contributions (original research articles and review papers) that combine theoretical development, empirical evidence and practical applications to strengthen financial stability and resilience.

This Special Issue welcomes manuscripts that link the following themes:

  • AI and Machine Learning in Financial Forecasting and Bankruptcy Prediction;
  • Behavioral and Sentiment-Based Predictive Modeling in Investment Decisions;
  • Strategic Investment, Risk Management and Financial Planning under Market Uncertainty.

We look forward to receiving your original research articles and reviews.

Dr. Marek Nagy
Dr. Katarina Valaskova
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

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. Forecasting is an international peer-reviewed open access semimonthly 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

  • advanced forecasting
  • financial analysis
  • artificial intelligence
  • machine learning
  • predictive modeling
  • uncertainty in financial markets
  • bankruptcy prediction
  • financial forecasting
  • AI-powered finance
  • investor sentiment modeling

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Published Papers (6 papers)

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Research

32 pages, 702 KB  
Article
S-NODE-ANF-RRC: Stochastic Neural ODE for Financial Regime Forecasting and False Alarm Control on JSE Equities
by Ntebogang Dinah Moroke
Forecasting 2026, 8(4), 54; https://doi.org/10.3390/forecast8040054 - 24 Jun 2026
Viewed by 349
Abstract
Emerging-market equity exchanges require regime forecasting systems that are continuous in time, robust to heavy-tailed distributions, and optimised against false alarms. No existing method addresses all three simultaneously, and no prior study has reported a crisis false-alarm rate on JSE equities. We propose [...] Read more.
Emerging-market equity exchanges require regime forecasting systems that are continuous in time, robust to heavy-tailed distributions, and optimised against false alarms. No existing method addresses all three simultaneously, and no prior study has reported a crisis false-alarm rate on JSE equities. We propose S-NODE-ANF-RRC: a stochastic neural ODE within an Adaptive Neuro-Fuzzy Risk-Regime Clustering architecture, integrated by a Milstein scheme with Lyapunov-regularised dual-loss training. The system is evaluated as a one-step-ahead probabilistic forecaster (h=1 trading day) on 2696 daily observations across 17 JSE securities (March 2015–March 2026). Gaussian mixture clustering on raw features (kurtosis 54.8) inflates ARI by 1.3×; log-transformation corrects this artefact. Two operational profiles emerge: the N-ODE-ANF-RRC achieves the lowest cost (10,350 bp, 65.1% below GMM) and longest lead time (0.71 days); the S-NODE-ANF-RRC achieves the lowest false alarm rate among probabilistic architectures (FAR = 0.051), with a 42.0% cost reduction versus GMM (McNemar p=0.027, power 1−β=0.73; bootstrap CI [5250, 19,600] bp excludes zero). Ablation confirms drift, diffusion, and dual-loss as the minimum viable daily-frequency configuration. Full article
(This article belongs to the Special Issue Advanced Forecasting in an Era of Uncertainty and Its Impact on Strategic Investment Decisions)
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38 pages, 689 KB  
Article
Bitcoin Volatility Forecasting Through Market Sentiment, Blockchain Fundamentals, and Endogenous Market Uncertainty
by Marcel Figura, Martin Bugaj, Elvira Nica and Gheorghe H. Popescu
Forecasting 2026, 8(3), 41; https://doi.org/10.3390/forecast8030041 - 19 May 2026
Viewed by 2272
Abstract
The study develops and empirically evaluates a forecasting-orientated structural model in which future Bitcoin historical volatility is modelled as being associated with market sentiment and blockchain fundamentals through market uncertainty. Market Sentiment (MS) is specified as a behavioural construct, Blockchain Fundamentals (BF) as [...] Read more.
The study develops and empirically evaluates a forecasting-orientated structural model in which future Bitcoin historical volatility is modelled as being associated with market sentiment and blockchain fundamentals through market uncertainty. Market Sentiment (MS) is specified as a behavioural construct, Blockchain Fundamentals (BF) as network conditions, and Market Uncertainty (MU) as an endogenous regime construct that consolidates signals shaping historical volatility at t+1. Using 262 weekly observations from January 2021 to January 2026, the analysis applies partial least squares structural equation modelling (PLS-SEM) with formative constructs and a forward-dated volatility target to preserve temporal ordering. Paths are evaluated with bootstrapping, effect sizes, and mediation analysis, while predictive performance is assessed using PLSpredict, the cross-validated predictive ability test (CVPAT), benchmark-based comparison, and Diebold-Mariano (DM) tests. MU emerges as the dominant predictor of Future Historical Volatility, denoted as HV(t+1) in the structural model (β = 0.864, p-value < 0.001; f2 = 2.036). The effect of BF is largely indirect, with 91.02% of the total effect transmitted via uncertainty, indicating indirect-only mediation. The model explains substantial variation in HV(t+1) (R2 = 0.791) and shows predictive relevance (Q2 predict = 0.287), while the benchmark-based results indicate mixed but competitive forecasting performance relative to persistence-based and econometric alternatives. These findings are consistent with a regime-based interpretation of Bitcoin volatility and highlight the explanatory and predictive relevance of an integrated behavioural-network-uncertainty architecture. Full article
(This article belongs to the Special Issue Advanced Forecasting in an Era of Uncertainty and Its Impact on Strategic Investment Decisions)
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27 pages, 732 KB  
Article
Multi-Timeframe Feature Engineering for Bitcoin Market Prediction: A Price-Level-Agnostic Machine Learning Approach
by Pedro Sobreiro, Domingos Martinho, Rui Martins and Ricardo Vardasca
Forecasting 2026, 8(3), 40; https://doi.org/10.3390/forecast8030040 - 18 May 2026
Viewed by 2478
Abstract
Predicting profitable entry signals in Bitcoin markets remains challenging due to price volatility, the absence of fundamental valuation frameworks, and methodological pitfalls that are common in the literature. In this study, we evaluate five machine learning classifiers using a 37-feature hierarchical multi-timeframe pipeline [...] Read more.
Predicting profitable entry signals in Bitcoin markets remains challenging due to price volatility, the absence of fundamental valuation frameworks, and methodological pitfalls that are common in the literature. In this study, we evaluate five machine learning classifiers using a 37-feature hierarchical multi-timeframe pipeline with price-level-agnostic normalization across four temporal resolutions (15-min, 4-h, daily, and 3-day), spanning January 2020 to November 2025. Binary training labels were generated via majority-vote aggregation across 54 stop-loss/take-profit combinations, producing 6951 balanced samples (48.5% positive class). Five algorithms—Logistic Regression, Decision Tree, Random Forest, XGBoost, and LightGBM—are compared using expanding-window TimeSeriesSplit validation (5 folds). Random Forest achieved the highest cross-validated ROC-AUC (0.6086), with all models showing modest but consistent discriminative ability (range 0.57–0.61). Feature importance analysis identifies 4-hour Bollinger Band position and RSI as dominant predictors, with all timeframes contributing meaningfully. A true out-of-sample holdout on 1136 independently generated 2025 samples confirms generalization, with Logistic Regression achieving 0.6087 ROC-AUC. A subtle multi-timeframe look-ahead bias in higher-timeframe data alignment is identified and corrected, which inflated performance by approximately 0.20 ROC-AUC points before correction. Event-driven backtesting on 2025 out-of-sample data yields a gross upper-bound return of +35.97% (185 trades, SL = 1%, TP = 2%, threshold = 0.7, Sharpe = 0.14) before transaction costs, after realistic round-trip fees, net returns are likely negligible. The central finding is that models with ROC-AUC ≈ 0.60 cannot reliably generate economically significant returns once transaction costs are accounted for. The methodology provides a reproducible framework for ML-based binary classification studies requiring transparent, bias-corrected validation across diverse market regimes. Full article
(This article belongs to the Special Issue Advanced Forecasting in an Era of Uncertainty and Its Impact on Strategic Investment Decisions)
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33 pages, 2407 KB  
Article
Determinants of Successful IoT and AI Initiatives in the SMART Economy: An Enterprise Perspective
by Jan Dvorsky, Matus Senci, Abdul Bashiru Jibril and Zora Petrakova
Forecasting 2026, 8(3), 39; https://doi.org/10.3390/forecast8030039 - 12 May 2026
Viewed by 577
Abstract
AI/IoT initiatives are increasingly adopted in business, yet reported success varies substantially across firms. This study develops and evaluates a firm-level predictive framework for the reported AI/IoT success rate, measured on a bounded 0–100 scale. Using enterprise survey data from Slovakia and the [...] Read more.
AI/IoT initiatives are increasingly adopted in business, yet reported success varies substantially across firms. This study develops and evaluates a firm-level predictive framework for the reported AI/IoT success rate, measured on a bounded 0–100 scale. Using enterprise survey data from Slovakia and the Czech Republic (n = 1250), we compare a regularized linear baseline (Elastic Net) with nonlinear approaches (Decision Tree and Random Forest) under a consistent out-of-sample evaluation framework, and we examine the best-performing model using permutation importance and PDP/ICE tools. Random Forest achieves the strongest out-of-sample predictive performance and reduces absolute errors relative to Elastic Net for most test observations, although diagnostics also reveal a small tail of extreme errors. Across model families, ai_iot_advantage_share emerges as the most stable predictor of reported AI/IoT success. Nonlinear diagnostics indicate a threshold-like transition in predicted success around the mid-range of advantage attribution and a saturation pattern at higher values. Readiness and performance-related variables are associated with higher predicted success, whereas higher barrier levels are associated with lower predicted success. The results position value realization as the most informative predictive signal in the dataset and provide an interpretable basis for enterprise-level screening and managerial reflection rather than causal inference. Full article
(This article belongs to the Special Issue Advanced Forecasting in an Era of Uncertainty and Its Impact on Strategic Investment Decisions)
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42 pages, 966 KB  
Article
Garbage In, Garbage Out? The Impact of Data Quality on the Performance of Financial Distress Prediction Models
by Veronika Labosova, Lucia Duricova, Katarina Kramarova and Marek Durica
Forecasting 2026, 8(3), 35; https://doi.org/10.3390/forecast8030035 - 22 Apr 2026
Viewed by 1641
Abstract
Financial distress prediction remains a central topic in corporate finance and risk management, with extensive research devoted to improving classification accuracy through increasingly sophisticated statistical and machine learning techniques. Nevertheless, the influence of data preparation on predictive performance has received comparatively less systematic [...] Read more.
Financial distress prediction remains a central topic in corporate finance and risk management, with extensive research devoted to improving classification accuracy through increasingly sophisticated statistical and machine learning techniques. Nevertheless, the influence of data preparation on predictive performance has received comparatively less systematic attention. This study examines how an economically grounded data-preparation process affects the predictive performance of selected statistical and machine-learning models dedicated to predicting corporate financial distress. Using the chosen financial ratios, generally accepted indicators of corporate financial stability and economic performance, financial distress models are estimated on both raw, unprocessed input data and pre-processed data involving the exclusion of economically implausible accounting values, treatment of missing observations, and class balancing. In light of the above, the study adopts a structured methodological approach to assess the predictive performance of selected classification models, namely decision tree algorithms (CART, CHAID, and C5.0), artificial neural networks (ANNs), logistic regression (LR), and linear discriminant analysis (DA), using confusion-matrix–based evaluation and a comprehensive set of evaluation measures. The results suggest that the process of input data preparation is a critical factor, significantly improving the predictive performance of financial distress prediction models across most modelling techniques employed. The most pronounced gains are observed in decision tree models. ANNs also demonstrate marked improvement after input data preparation, whereas LR benefits more moderately, and linear DA remains limited despite preprocessing. The average gain in accuracy across all six modelling techniques, calculated as the difference between pre-processed and raw performance for each method and averaged across methods, was approximately 15.6 percentage points, with specificity improving by approximately 26.9 percentage points on average, amounting to roughly half the performance variation attributable to algorithm choice, which underscores that data preparation is a primary determinant of model reliability alongside algorithm selection. A step-level detailed analysis further shows that missing value imputation is the dominant driver of improvement for tree-based models, while class balancing contributes most for ANNs and logistic regression. The findings highlight that reliable financial distress prediction depends not only on technique selection but also on the consistency and economic plausibility of the input data, underscoring the central role of structured data preparation in developing robust early-warning models. Full article
(This article belongs to the Special Issue Advanced Forecasting in an Era of Uncertainty and Its Impact on Strategic Investment Decisions)
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32 pages, 3102 KB  
Article
Crude Oil Shocks and Saudi Stock Returns: An Integrated Granger–LSTM–XGBoost Analysis
by Priyanka Aggarwal, Nevi Danila, Eddy Suprihadi and Manoj Kumar Manish
Forecasting 2026, 8(2), 19; https://doi.org/10.3390/forecast8020019 - 24 Feb 2026
Viewed by 2017
Abstract
This study investigates regime-dependent forecasting of the Saudi stock market by combining macro-controlled dependence analysis with nonlinear predictive modeling. Using daily data from September 2010 to August 2025, we analyze the interaction between the Tadawul All Share Index (TASI) returns and crude oil [...] Read more.
This study investigates regime-dependent forecasting of the Saudi stock market by combining macro-controlled dependence analysis with nonlinear predictive modeling. Using daily data from September 2010 to August 2025, we analyze the interaction between the Tadawul All Share Index (TASI) returns and crude oil returns while controlling for inflation and interest-rate dynamics. A four-variable VAR with macro controls is estimated separately in pre- and post-COVID regimes to characterize directional predictability and changes in transmission lags. We then evaluate out-of-sample return forecasting performance across econometric benchmarks (ARIMA, ARIMAX, and VAR) and machine learning models (LSTM and XGBoost) under a strictly time-ordered expanding-window design with sequential train/validation/test partitioning. The results indicate that traditional linear benchmarks exhibit limited predictive ability in both regimes, with negative out-of-sample explanatory power. By contrast, XGBoost delivers the strongest overall performance, achieving positive out-of-sample R2 in both regimes (0.046 in pre-COVID and 0.010 in post-COVID), together with the lowest forecast errors (RMSE = 0.0081 pre-COVID; 0.0078 post-COVID). Interpretability analysis further reveals a regime-sensitive shift in drivers: short-horizon equity lag dynamics dominate during stable periods, whereas oil-related and macro-financial variables gain importance under turbulent conditions. Economic-value evaluation supports the practical relevance of these gains, showing that XGBoost-based signals yield superior risk-adjusted trading outcomes and remain favorable under downside-risk and drawdown-based assessment. Overall, these findings highlight that forecasting in oil-linked emerging markets is inherently regime-dependent and that nonlinear ensemble learners, particularly XGBoost, provide a more robust and economically meaningful approach under structural change. Full article
(This article belongs to the Special Issue Advanced Forecasting in an Era of Uncertainty and Its Impact on Strategic Investment Decisions)
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