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on Forecasting |
| By: | Sharif Al Mamun; Rakib Hossain; Md. Jobayer Rahman; Malay Kumar Devnath; Farhana Afroz; Lisan Al Amin |
| Abstract: | A Bayesian analytics framework that precisely quantifies uncertainty offers a significant advance for financial risk management. We develop an integrated approach that consistently enhances the handling of risk in market volatility forecasting, fraud detection, and compliance monitoring. Our probabilistic, interpretable models deliver reliable results: We evaluate the performance of one-day-ahead 95% Value-at-Risk (VaR) forecasts on daily S&P 500 returns, with a training period from 2000 to 2019 and an out-of-sample test period spanning 2020 to 2024. Formal tests of unconditional (Kupiec) and conditional (Christoffersen) coverage reveal that an LSTM baseline achieves near-nominal calibration. In contrast, a GARCH(1, 1) model with Student-t innovations underestimates tail risk. Our proposed discount-factor DLM model produces a slightly liberal VaR estimate, with evidence of clustered violations. Bayesian logistic regression improves recall and AUC-ROC for fraud detection, and a hierarchical Beta state-space model provides transparent and adaptive compliance risk assessment. The pipeline is distinguished by precise uncertainty quantification, interpretability, and GPU-accelerated analysis, delivering up to 50x speedup. Remaining challenges include sparse fraud data and proxy compliance labels, but the framework enables actionable risk insights. Future expansion will extend feature sets, explore regime-switching priors, and enhance scalable inference. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.15739 |
| By: | Mohit Beniwal |
| Abstract: | Long-term price forecasting remains a formidable challenge due to the inherent uncertainty over the long term, despite some success in short-term predictions. Nonetheless, accurate long-term forecasts are essential for high-net-worth individuals, institutional investors, and traders. The proposed improved genetic algorithm-optimized support vector regression (IGA-SVR) model is specifically designed for long-term price prediction of global indices. The performance of the IGA-SVR model is rigorously evaluated and compared against the state-of-the-art baseline models, the Long Short-Term Memory (LSTM), and the forward-validating genetic algorithm optimized support vector regression (OGA-SVR). Extensive testing was conducted on the five global indices, namely Nifty, Dow Jones Industrial Average (DJI), DAX Performance Index (DAX), Nikkei 225 (N225), and Shanghai Stock Exchange Composite Index (SSE) from 2021 to 2024 of daily price prediction up to a year. Overall, the proposed IGA-SVR model achieved a reduction in MAPE by 19.87% compared to LSTM and 50.03% compared to OGA-SVR, demonstrating its superior performance in long-term daily price forecasting of global indices. Further, the execution time for LSTM was approximately 20 times higher than that of IGA-SVR, highlighting the high accuracy and computational efficiency of the proposed model. The genetic algorithm selects the optimal hyperparameters of SVR by minimizing the arithmetic mean of the Mean Absolute Percentage Error (MAPE) calculated over the full training dataset and the most recent five years of training data. This purposefully designed training methodology adjusts for recent trends while retaining long-term trend information, thereby offering enhanced generalization compared to the LSTM and rolling-forward validation approach employed by OGA-SVR, which forgets long-term trends and suffers from recency bias. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.15113 |
| By: | Yoosoon Chang (Indiana University, Department of Economics); Youngmin Choi (Baruch College (CUNY), Zicklin School of Business, Department of Economics and Finance); Soohun Kim (Korea Advanced Institute of Science and Technology (KAIST), College of Business); Joon Park (Indiana University, Department of Economics) |
| Abstract: | This paper develops a novel functional predictive regression framework linking option-implied distributions to stock market returns, motivated by the fundamental link between risk-neutral and physical densities. Using extensive S&P 500 option panels, our model exhibits significant forecasting power, achieving robust out-of-sample R2 exceeding 4% and outperforming traditional predictors. Superior performance arises from leveraging the full spectrum of the risk-neutral density via functional principal components. Our analysis reveals forecasting success stems from nuanced variations in risk-neutral densities beyond conventional finite moments, underscoring the predictive value of distributional shape and higher-order information, and demonstrates potential economic gains through a market-timing strategy. |
| Keywords: | functional predictive regression, market risk premium, option market, return predictability, risk-neutral measure, stochastic discount factor |
| Date: | 2025–07 |
| URL: | https://d.repec.org/n?u=RePEc:inu:caeprp:2025003 |
| By: | Martin McCarthy (Reserve Bank of Australia); Stephen Snudden (Wilfrid Laurier University) |
| Abstract: | Forecasting period-average exchange rates requires using high-frequency data to efficiently construct forecasts and to test the accuracy of these forecasts against the traditional random walk hypothesis. To achieve this, we construct the first real-time dataset of daily effective exchange rates for all available countries, both nominal and real. The real-time vintages account for the typical delay in the publication of trade weights and inflation. Our findings indicate that forecasts constructed with daily data can significantly improve accuracy, up to 40 per cent compared to using monthly averages. We also find that unlike bilateral exchange rates, daily effective exchange rates exhibit properties distinct from random walk processes. When applying efficient estimation and testing methods made possible for the first time by the daily data, we find new evidence of real-time predictability for effective exchange rates in up to fifty per cent of countries. |
| Keywords: | temporal aggregation; exchange rates; forecasting; forecast evaluation; high-frequency data |
| JEL: | C43 C5 F31 F37 |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:rba:rbardp:rdp2025-09 |
| By: | Julia Ko\'nczal; Micha{\l} Balcerek; Krzysztof Burnecki |
| Abstract: | In recent years, the growing frequency and severity of natural disasters have increased the need for effective tools to manage catastrophe risk. Catastrophe (CAT) bonds allow the transfer of part of this risk to investors, offering an alternative to traditional reinsurance. This paper examines the role of climate variability in CAT bond pricing and evaluates the predictive performance of various machine learning models in forecasting CAT bond coupons. We combine features typically used in the literature with a new set of climate indicators, including Oceanic Ni{\~n}o Index, Arctic Oscillation, North Atlantic Oscillation, Outgoing Longwave Radiation, Pacific-North American pattern, Pacific Decadal Oscillation, Southern Oscillation Index, and sea surface temperatures. We compare the performance of linear regression with several machine learning algorithms, such as random forest, gradient boosting, extremely randomized trees, and extreme gradient boosting. Our results show that including climate-related variables improves predictive accuracy across all models, with extremely randomized trees achieving the lowest root mean squared error (RMSE). These findings suggest that large-scale climate variability has a measurable influence on CAT bond pricing and that machine learning methods can effectively capture these complex relationships. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.22660 |
| By: | Liyuan Cui; Guanhao Feng; Yuefeng Han; Jiayan Li |
| Abstract: | We tackle the challenge of estimating grouping structures and factor loadings in asset pricing models, where traditional regressions struggle due to sparse data and high noise. Existing approaches, such as those using fused penalties and multi-task learning, often enforce coefficient homogeneity across cross-sectional units, reducing flexibility. Clustering methods (e.g., spectral clustering, Lloyd's algorithm) achieve consistent recovery under specific conditions but typically rely on a single data source. To address these limitations, we introduce the Panel Coupled Matrix-Tensor Clustering (PMTC) model, which simultaneously leverages a characteristics tensor and a return matrix to identify latent asset groups. By integrating these data sources, we develop computationally efficient tensor clustering algorithms that enhance both clustering accuracy and factor loading estimation. Simulations demonstrate that our methods outperform single-source alternatives in clustering accuracy and coefficient estimation, particularly under moderate signal-to-noise conditions. Empirical application to U.S. equities demonstrates the practical value of PMTC, yielding higher out-of-sample total $R^2$ and economically interpretable variation in factor exposures. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.23567 |
| By: | Agostino Capponi; Chengpiao Huang; J. Antonio Sidaoui; Kaizheng Wang; Jiacheng Zou |
| Abstract: | We investigate machine learning models for stock return prediction in non-stationary environments, revealing a fundamental nonstationarity-complexity tradeoff: complex models reduce misspecification error but require longer training windows that introduce stronger non- stationarity. We resolve this tension with a novel model selection method that jointly optimizes model class and training window size using a tournament procedure that adaptively evaluates candidates on non-stationary validation data. Our theoretical analysis demonstrates that this approach balances misspecification error, estimation variance, and non-stationarity, performing close to the best model in hindsight. Applying our method to 17 industry portfolio returns, we consistently outperform standard rolling-window benchmarks, improving out-of-sample $R^2$ by 14-23% on average. During NBER- designated recessions, improvements are substantial: our method achieves positive $R^2$ during the Gulf War recession while benchmarks are negative, and improves $R^2$ in absolute terms by at least 80bps during the 2001 recession as well as superior performance during the 2008 Financial Crisis. Economically, a trading strategy based on our selected model generates 31% higher cumulative returns averaged across the industries. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.23596 |
| By: | Benjamin Born; Nora Lamersdorf; Jana-Lynn Schuster; Sascha Steffen |
| Abstract: | Using modern natural language processing, we construct a high-frequency inflation expectations index from German-language tweets. This index closely tracks realized inflation and aligns even more closely with household survey expectations. It also improves short-run forecasts relative to standard benchmarks. In response to monetary policy tightening, the index declines within about a week, with the effects concentrated in tweets by private individuals and during the recent period of elevated inflation. Using 117 million online transactions from German retailers, we show that higher inflation expectations are followed by lower household spending on discretionary goods. By linking these shifts in demand to stock returns, we find that, during periods of elevated inflation, firms operating in discretionary sectors experience significantly lower stock returns when inflation expectations rise. Thus, our Twitter-based index provides market participants and policymakers with a timely tool to monitor inflation sentiment and its economic consequences. |
| Keywords: | inflation expectations, social media (Twitter/X), large language models (LLMs), NLP, household consumption, stock returns, monetary policy |
| JEL: | E31 D84 E58 C45 C81 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12361 |
| By: | Rocío Clara A. Mora-Quiñones; Antonio José Orozco-Gallo; Dora Alicia Mora-Pérez |
| Abstract: | This study introduces an approach for measuring sentiment and uncertainty indices in Colombia through text mining. Economic news from digital media, spanning March 2020 to September 2024, is analyzed using dictionary-based methods and predefined word lists. The constructed indices reflect major macroeconomic events, such as the phased reopening during the pandemic, the national strike in May 2021, and the decline in demand associated with elevated inflation. These indices function as leading indicators and exhibit statistically significant associations with high-frequency economic data. Incorporating news-based sentiment and uncertainty indices improves the precision of nowcasting Colombia’s economic activity using a dynamic factor model. The results indicate that incorporating qualitative, forward-looking news with traditional data enhances the monitoring of short-term economic fluctuations and the identification of turning points. *****RESUMEN: Este estudio presenta un método para medir el sentimiento y la incertidumbre económica en Colombia mediante técnicas de minería de texto. A partir de noticias publicadas entre marzo de 2020 y septiembre de 2024 y empleando metodologías de diccionario basadas en listas predefinidas de palabras positivas y negativas, se construyeron los índices de sentimiento e incertidumbre. Estos índices identificaron episodios macroeconómicos relevantes, como la reapertura gradual tras la pandemia, el Paro Nacional de 2021 y la desaceleración de la demanda en un entorno de elevada inflación. Los índices exhiben propiedades de series adelantadas y mantienen relaciones estadísticamente significativas con variables económicas de alta frecuencia. El análisis empírico muestra que su incorporación en modelos factoriales dinámicos mejora de manera sistemática la precisión en los pronósticos de la actividad económica. Los resultados muestran que la información cualitativa y prospectiva contenida en las noticias complementa los datos tradicionales y fortalece la capacidad para determinar dinámicas de corto plazo y anticipar puntos de inflexión de la actividad económica colombiana. |
| Keywords: | sentiment, uncertainty, artificial intelligence, text analysis techniques, natural language processing, dynamic factor model, sentimiento, incertidumbre, inteligencia artificial, técnicas de análisis de texto, procesamiento de lenguaje natural, modelo factorial dinámico. |
| JEL: | C53 C82 E27 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:bdr:borrec:1340 |
| By: | Greta Polo; Yuan Gao Rollinson; Ms. Yevgeniya Korniyenko; Tongfang Yuan |
| Abstract: | This paper presents a machine learning–based nowcasting framework for estimating quarterly non-oil GDP growth in the Gulf Cooperation Council (GCC) countries. Leveraging machine learning models tailored to each country, the framework integrates a broad range of high-frequency indicators—including real activity, financial conditions, trade, and oil-related variables—to produce timely, sector-specific estimates. Advancing the nowcasting literature for the MENA region, this approach moves beyond single-model methodologies by incorporating a richer set of high-frequency, cross-border indicators. It presents two key innovations: (i) a tailored data integration strategy that broadens and automates the use of high-frequency indicators; and (ii) a novel application of Shapley value decompositions to enhance model interpretability and guide the iterative selection of predictive indicators. The framework’s flexibility allows it to account for the region’s unique economic structures, ongoing reform agendas, and the spillover effects of oil market volatility on non-oil sectors. By enhancing the granularity, responsiveness, and transparency of short-term forecasts, the model enables faster, data-driven policy decisions strengthening economic surveillance and enhancing policy agility across the GCC amid a rapidly evolving global environment. |
| Keywords: | GCC; Nowcasting; Machine Learning; Non-oil Growth |
| Date: | 2025–12–19 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2025/268 |