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on Forecasting |
| By: | Harrison Katz |
| Abstract: | Understanding how the composition of guest origin markets evolves over time is critical for destination marketing organizations, hospitality businesses, and tourism planners. We develop and apply Bayesian Dirichlet autoregressive moving average (BDARMA) models to forecast the compositional dynamics of guest origin market shares using proprietary Airbnb booking data spanning 2017--2024 across four major destination regions. Our analysis reveals substantial pandemic-induced structural breaks in origin composition, with heterogeneous recovery patterns across markets. The BDARMA framework achieves the lowest average forecast error across all destination regions, outperforming standard benchmarks including na\"ive forecasts, exponential smoothing, and SARIMA on log-ratio transformed data. For EMEA destinations, BDARMA achieves 23% lower forecast error than naive methods, with statistically significant improvements. By modeling compositions directly on the simplex with a Dirichlet likelihood and incorporating seasonal variation in both mean and precision parameters, our approach produces coherent forecasts that respect the unit-sum constraint while capturing complex temporal dependencies. The methodology provides destination stakeholders with probabilistic forecasts of source market shares, enabling more informed strategic planning for marketing resource allocation, infrastructure investment, and crisis response. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.18358 |
| By: | Runyao Yu; Derek W. Bunn; Julia Lin; Jochen Stiasny; Fabian Leimgruber; Tara Esterl; Yuchen Tao; Lianlian Qi; Yujie Chen; Wentao Wang; Jochen L. Cremer |
| Abstract: | Electricity price forecasting (EPF) plays a critical role in power system operation and market decision making. While existing review studies have provided valuable insights into forecasting horizons, market mechanisms, and evaluation practices, the rapid adoption of deep learning has introduced increasingly diverse model architectures, output structures, and training objectives that remain insufficiently analyzed in depth. This paper presents a structured review of deep learning methods for EPF in day-ahead, intraday, and balancing markets. Specifically, We introduce a unified taxonomy that decomposes deep learning models into backbone, head, and loss components, providing a consistent evaluation perspective across studies. Using this framework, we analyze recent trends in deep learning components across markets. Our study highlights the shift toward probabilistic, microstructure-centric, and market-aware designs. We further identify key gaps in the literature, including limited attention to intraday and balancing markets and the need for market-specific modeling strategies, thereby helping to consolidate and advance existing review studies. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.10071 |
| By: | Elliot Beck; Franziska Eckert; Linus Kühne; Helge Liebert; Rina Rosenblatt-Wisch |
| Abstract: | We develop a resource-efficient methodology for measuring economic outlook in news text that combines document embeddings with synthetic training data generated by large language models. Applied to 27 million news articles, the resulting indicator significantly improves GDP growth forecast accuracy and captures sentiment shifts weeks before official releases, proving particularly valuable during crises. The indicator outperforms both survey-based benchmarks and traditional dictionary methods and is interpretable, allowing identification of specific drivers of economic sentiment. Our approach addresses key institutional constraints: it performs sentiment classification locally, enabling analyses of proprietary news content without transmission to external services while requiring minimal computational resources compared to direct large language model classification. |
| Keywords: | Sentiment analysis, Economic outlook, Forecasting, Big data, Large language models, Natural language processing, Neural networks |
| JEL: | E66 C45 C55 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:snb:snbwpa:2026-04 |
| By: | Marc Wildi |
| Abstract: | We re-examine the traditional Mean-Squared Error (MSE) forecasting paradigm by formally integrating an accuracy-timeliness trade-off: accuracy is defined by MSE (or target correlation) and timeliness by advancement (or phase excess). While MSE-optimized predictors are accurate in tracking levels, they sacrifice dynamic lead, causing them to lag behind changing targets. To address this, we introduce two `look-ahead' frameworks--Decoupling-from-Present (DFP) and Peak-Correlation-Shifting (PCS)--and provide closed-form solutions for their optimization. Notably, the classical MSE predictor is shown to be a special case within these frameworks. Dually, our methods achieve maximum advancement for any given accuracy level, so our approach reveals the complete efficient frontier of the accuracy-timeliness trade-off, whereas MSE represents only a single point. We also derive a universal upper bound on lead over MSE for any linear predictor under a consistency constraint and prove that our methods hit this ceiling. We validate this approach through applications in forecasting and real-time signal extraction, introducing a leading-indicator criterion and tailored linear benchmarks. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.23087 |
| By: | Robben, Jens (University of Amsterdam); Barigou, Karim (Université catholique de Louvain, LIDAM/ISBA, Belgium) |
| Abstract: | Accurate forecasts of weekly mortality are essential for public health and the insurance industry. We develop a forecasting framework that extends the Lee–Carter model with age- and region-specific seasonal effects and penalized distributed lag non-linear components that capture the delayed and non-linear effects of heat, cold, and influenza on mortality. The model accommodates overdispersed mortality rates via a negative binomial distribution. We model the temporal dynamics of the latent factors in the model using SARIMAX processes and capture cross-regional dependencies through a copula-based approach. Using regional French mortality data (1990–2019), we demonstrate that the proposed framework yields well-calibrated forecast distributions and improves predictive accuracy relative to benchmark models. The results further show substantial heterogeneity in temperature- and influenza-related relative risks between ages and regions. These findings underscore the importance of incorporating exogenous drivers and dependence structures into a weekly mortality forecasting framework. |
| Keywords: | Stochastic mortality modeling ; seasonal mortality ; distributed lag non-linear models ; excess mortality |
| Date: | 2025–09–29 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2025016 |
| By: | Lovisa Reiche; Gabriele Galati; Richhild Moessner; Maarten van Rooij |
| Abstract: | We study how consumers form and revise inflation expectations using a unique, highly balanced monthly panel of Dutch households. We develop a Bayesian framework that nests Full-Information Rational Expectations (FIRE) alongside common forecasting heuristics and test it by recovering person-specific belief-updating rules from individual time-series regressions. Our novel individual-level design reveals substantial heterogeneity in how households process information over time. On average, consumers systematically overreact to current inflation, echoing patterns found for professional forecasters. Only 2.5 percent, predominantly wealthier, more educated men, behave consistently with FIRE. Most consumers rely on simple heuristics, especially adaptive expectations. Our results show that heuristic learning, not FIRE, characterizes expectation formation for the vast majority of households. Crucially, heterogeneity in belief updating is both large and systematic. |
| Keywords: | household inflation expectations, FIRE, heuristic learning |
| JEL: | E31 E37 E70 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12450 |
| By: | Barigou, Karim (Université catholique de Louvain, LIDAM/ISBA, Belgium); Patten, Melanie; Zhou, Kenneth Q. |
| Abstract: | Climate change poses increasing challenges for mortality modeling and underscores the need to integrate climate-related variables into mortality forecasting. This study introduces a two-step approach that incorporates climate information from the Actuaries Climate Index (ACI) into mortality models. In the first step, we model region-specific seasonal mortality dynamics using the Lee-Carter model with SARIMA processes, a cosine-sine decomposition, and a cyclic spline-based function. In the second step, residual deviations from the baseline model are explained by ACI components using Generalized Linear Models, Generalized Additive Models, and Extreme Gradient Boosting. To further capture the dependence between mortality and climate, we develop a SARIMA-Copula forecasting approach linking mortality period effects with temperature extremes. Our results show that incorporating ACI components systematically enhances out-of-sample accuracy, underscoring the value of integrating climate-related variables into stochastic mortality modeling. The proposed framework offers actuaries and policymakers a practical tool for anticipating and managing climate-related mortality risks. |
| Keywords: | Mortality modeling ; Climate risk ; Actuaries Climate Index ; Copula ; Machine learning |
| Date: | 2025–10–21 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2025017 |
| By: | Piersilvio De Bortoli; Davide Ferrari; Francesco Ravazzolo; Luca Rossini |
| Abstract: | This paper studies the Model Selection Confidence Set (MSCS) methodology for univariate time series models involving autoregressive and moving average components, and applies it to study model selection uncertainty in the Italian electricity load data. Rather than relying on a single model selected by an arbitrary criterion, the MSCS identifies a set of models that are statistically indistinguishable from the true data-generating process at a given confidence level. The size and composition of this set reveal crucial information about model selection uncertainty: noisy data scenarios produce larger sets with many candidate models, while more informative cases narrow the set considerably. To study the importance of each model term, we consider numerical statistics measuring the frequency with which each term is included in both the entire MSCS and in Lower Boundary Models (LBM), its most parsimonious specifications. Applied to Italian hourly electricity load data, the MSCS methodology reveals marked intraday variation in model selection uncertainty and isolates a collection of model specifications that deliver competitive short-term forecasts while highlighting key drivers of electricity load like intraday hourly lags, temperature, calendar effects and solar energy generation. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.16527 |
| By: | Sumin Kim; Jihoon Kwon; Yoon Kim; Nicole Kagan; Raffi Khatchadourian; Wonbin Ahn; Alejandro Lopez-Lira; Jaewon Lee; Yoontae Hwang; Oscar Levy; Yongjae Lee; Chanyeol Choi |
| Abstract: | Mention markets, a type of prediction market in which contracts resolve based on whether a specified keyword is mentioned during a future public event, require accurate probabilistic forecasts of keyword-mention outcomes. While recent work shows that large language models (LLMs) can generate forecasts competitive with human forecasters, it remains unclear how input context should be designed to support accurate prediction. In this paper, we study this question through experiments on earnings-call mention markets, which require forecasting whether a company will mention a specified keyword during its upcoming call. We run controlled comparisons varying (i) which contextual information is provided (news and/or prior earnings-call transcripts) and (ii) how \textit{market probability}, (i.e., prediction market contract price) is used. We introduce Market-Conditioned Prompting (MCP), which explicitly treats the market-implied probability as a prior and instructs the LLM to update this prior using textual evidence, rather than re-predicting the base rate from scratch. In our experiments, we find three insights: (1) richer context consistently improves forecasting performance; (2) market-conditioned prompting (MCP), which treats the market probability as a prior and updates it using textual evidence, yields better-calibrated forecasts; and (3) a mixture of the market probability and MCP (MixMCP) outperforms the market baseline. By dampening the LLM's posterior update with the market prior, MixMCP yields more robust predictions than either the market or the LLM alone. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.21229 |
| By: | Yijie Wang; Hao Gao; Campbell R. Harvey; Yan Liu; Xinyuan Tao |
| Abstract: | The standard approach to portfolio selection involves two stages: forecast the asset returns and then plug them into an optimizer. We argue that this separation is deeply problematic. The first stage treats cross-sectional prediction errors as equally important across all securities. However, given that final portfolios might differ given distinct risk preferences and investment restrictions, the standard approach fails to recognize that the investor is not just concerned with the average forecast error - but the precision of the forecasts for the specific assets that are most important for their portfolio. Hence, it is crucial to integrate the two stages. We propose a novel implementation utilizing machine learning tools that unifies the expected return generation process and the final optimized portfolio. Our empirical example provides convincing evidence that our end-to-end method outperforms the traditional two-stage approach. In our framework, each investor has their own, endogenously determined, efficient frontier that depends on risk preferences, investor-specific constraints, as well as exposure to market frictions. |
| JEL: | C45 C55 G11 G12 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34861 |
| By: | Doron Avramov; Xin He |
| Abstract: | This paper develops a unified framework that links firm-level predictive signals, cross-asset spillovers, and the stochastic discount factor (SDF). Signals and spillovers are jointly estimated by maximizing the Sharpe ratio, yielding an interpretable SDF that both ranks characteristic relevance and uncovers the direction of predictive influence across assets. Out-of-sample, the SDF consistently outperforms self-predictive and expected-return benchmarks across investment universes and market states. The inferred information network highlights large, low-turnover firms as net transmitters. The framework offers a clear, economically grounded view of the informational architecture underlying cross-sectional return dynamics. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.20856 |
| By: | Sumin Kim; Minjae Kim; Jihoon Kwon; Yoon Kim; Nicole Kagan; Joo Won Lee; Oscar Levy; Alejandro Lopez-Lira; Yongjae Lee; Chanyeol Choi |
| Abstract: | Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead-lag relationships remains challenging due to spurious statistical correlations. We propose a hybrid two-stage causal screener to address this challenge: (i) a statistical stage that uses Granger causality to identify candidate leader-follower pairs from market-implied probability time series, and (ii) an LLM-based semantic stage that re-ranks these candidates by assessing whether the proposed direction admits a plausible economic transmission mechanism based on event descriptions. Because causal ground truth is unobserved, we evaluate the ranked pairs using a fixed, signal-triggered trading protocol that maps relationship quality into realized profit and loss (PnL). On Kalshi Economics markets, our hybrid approach consistently outperforms the statistical baseline. Across rolling evaluations, the win rate increases from 51.4% to 54.5%. Crucially, the average magnitude of losing trades decreases substantially from 649 USD to 347 USD. This reduction is driven by the LLM's ability to filter out statistically fragile links that are prone to large losses, rather than relying on rare gains. These improvements remain stable across different trading configurations, indicating that the gains are not driven by specific parameter choices. Overall, the results suggest that LLMs function as semantic risk managers on top of statistical discovery, prioritizing lead-lag relationships that generalize under changing market conditions. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.07048 |
| By: | Mattia Marzi; Tiziano Squartini |
| Abstract: | Networks underpin systems that range from finance to biology, yet their structure is often only partially observed. Current reconstruction methods typically fit the parameters of a model anew to each snapshot, thus offering no guidance to predict future configurations. Here, we develop a Bayesian approach that uses the information about past network snapshots to inform a prior and predict the subsequent ones, while quantifying uncertainty. Instantiated with a single-parameter fitness model, our method infers link probabilities from node strengths and carries information forward in time. When applied to the Electronic Market for Interbank Deposit across the years 1999-2012, our method accurately recovers the number of connections per bank at subsequent times, outperforming probabilistic benchmarks designed for analogous, link prediction tasks. Notably, each predicted snapshot serves as a reliable prior for the next one, thus enabling self-sustained, out-of-sample reconstruction of evolving networks with a minimal amount of additional data. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.21869 |
| By: | Marc Schmitt |
| Abstract: | I construct a Market Stress Probability Index (MSPI) that estimates the probability of high stress in the U.S. equity market one month ahead using information from the cross-section of individual stocks. Using CRSP daily data, each month is summarized by a set of interpretable cross-sectional fragility signals and mapped into a forward-looking stress probability via an L1-regularized logistic regression in a real-time expanding-window design. Out of sample, MSPI tracks major stress episodes and improves discrimination and accuracy relative to a parsimonious benchmark based on lagged market return and realized volatility, delivering calibrated stress probabilities on an economically meaningful scale. Further, I illustrate how MSPI can be used as a probability-based measurement object in financial econometrics. The resulting index provides a transparent and easily updated measure of near-term equity-market stress risk. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.07066 |
| By: | Mustapha Abderrahim; Riad Mansouri; Fatma Zohra Ouail; Sara Abadi; Kenza Elkrim; Mohamed-Fariz Zidane; Mr. Philippe D Karam; Mr. Gyorgy Molnar; Karel Musil; Valeriu Nalban |
| Abstract: | The paper describes QMPM, the Quarterly Projection Model for the Bank of Algeria that underpins the Bank’s Forecasting and Policy Analysis System. The model is designed to capture the key features of the economy, including the importance of the hydrocarbon sector, sizable fiscal policy impacts, monetary-fiscal interactions, a monetary aggregate targeting policy framework, and a managed exchange rate regime. Model-based analytical exercises demonstrate that AMPM displays both theoretical consistency and a robust data fit, confirming its practicality for conducting real-time policy analysis, forecasts, and risk scenarios in support of the Bank of Algeria’s policy processes. |
| Keywords: | Algeria; Forecasting and Policy Analysis; Quarterly Projection Model; Monetary Policy; Fiscal Policy; Transmission Mechanism |
| Date: | 2026–02–13 |
| URL: | https://d.repec.org/n?u=RePEc:imf:imfwpa:2026/025 |
| By: | Niakh, Fallou; Bassière, Alicia; Denuit, Michel (Université catholique de Louvain, LIDAM/ISBA, Belgium); Robert, Christian |
| Abstract: | This work presents a framework for peer-to-peer (P2P) basis risk management applied to solar electricity generation. The approach leverages physically based simulation models to estimate the day-ahead production forecasts and the actual realized production at the solar farm level. We quantify the financial loss from mismatches between forecasted and actual production using the outputs of these simulations. The framework then implements a parametric insurance mechanism to mitigate these financial losses and combines it with a P2P market structure to enhance participant risk sharing. By integrating day-ahead forecasts and actual production data with physical modeling, this method provides a comprehensive solution to manage production variability, offering practical insights for improving financial resilience in renewable energy systems. The results highlight the potential of combining parametric insurance with P2P mechanisms to foster reliability and collaboration in renewable energy markets. |
| Keywords: | Parametric insurance ; Basis risk ; P2P insurance ; Renewable production insurance |
| Date: | 2025–04–15 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2025007 |
| By: | Denuit, Michel (Université catholique de Louvain, LIDAM/ISBA, Belgium); Trufin, Julien (ULB) |
| Abstract: | Ehm et al. (2016) defined forecast dominance, or Bregman dominance as dominance for every Bregman loss function. This letter explores Bregman dominance to compare competing candidate pure premiums. An effective testing procedure for Bregman dominance is proposed based on Murphy diagrams and its performance is evaluated through a simulation study. An application to a Swiss motor insurance data set demonstrates the potential of the proposed procedure. |
| Keywords: | Insurance pricing ; Model comparison ; Bregman dominance ; Murphy diagram ; Statistical test |
| Date: | 2024–12–13 |
| URL: | https://d.repec.org/n?u=RePEc:aiz:louvad:2024025 |