Forecasting http://lists.repec.org/mailman/listinfo/nep-for Forecasting 2026-03-09 The Accuracy Smoothness Dilemma in Prediction: a Novel Multivariate M-SSA Forecast Approach https://d.repec.org/n?u=RePEc:arx:papers:2602.13722&r=&r=for Forecasting presents a complex estimation challenge, as it involves balancing multiple, often conflicting, priorities and objectives. Conventional forecast optimization methods typically emphasize a single metric--such as minimizing the mean squared error (MSE)--which may neglect other crucial aspects of predictive performance. To address this limitation, the recently developed Smooth Sign Accuracy (SSA) framework extends the traditional MSE approach by simultaneously accounting for sign accuracy, MSE, and the frequency of sign changes in the predictor. This addresses a fundamental trade-off--the so-called accuracy-smoothness (AS) dilemma--in prediction. We extend this approach to the multivariate M-SSA, leveraging the original criterion to incorporate cross-sectional information across multiple time series. As a result, the M-SSA criterion enables the integration of various design objectives related to AS forecasting performance, effectively generalizing conventional MSE-based metrics. To demonstrate its practical applicability and versatility, we explore the application of the M-SSA in three primary domains: forecasting, real-time signal extraction (nowcasting), and smoothing. These case studies illustrate the framework's capacity to adapt to different contexts while effectively managing inherent trade-offs in predictive modelling. Marc Wildi 2026-02 A Quadratic Link between Out-of-Sample $R^2$ and Directional Accuracy https://d.repec.org/n?u=RePEc:arx:papers:2602.07841&r=&r=for This study provides a novel perspective on the metric disconnect phenomenon in financial time series forecasting through an analytical link that reconciles the out-of-sample $R^2$ ($R^2_{OOS}$) and directional accuracy (DA). In particular, using the random walk model as a baseline and assuming that sign correctness is independent of realized magnitude, we show that these two metrics exhibit a quadratic relationship for MSE-optimal point forecasts. For point forecasts with modest DA, the theoretical value of $R^2_{OOS}$ is intrinsically negligible. Thus, a negative empirical $R^2_{OOS}$ is expected if the model is suboptimal or affected by finite sample noise. Cheng Zhang 2026-02 The Predictive Content of U.S. Energy Information Administration Oil Market Forecasts https://d.repec.org/n?u=RePEc:tur:wpapnw:104&r=&r=for This paper investigates the information content of oil market forecasts produced by the U.S. Energy Information Administration (EIA). We evaluate the maximum informative forecast horizons for EIA projections of world and U.S. oil demand, supply, inventories, and prices. Our results show that U.S. forecasts are systematically more informative than their global counterparts, with content horizons extending up to six quarters for most U.S. variables. The information content embedded in EIA forecasts reflects both the agency's ability to track evolving market conditions and, particularly at short horizons, the incorporation of information that goes beyond simple trend extrapolation. Garratt Anthony Petrella Ivan Zhang Yunyi EIA Forecasts, Oil Market, Forecast Horizon, Forecast Path, Non-convergent Forecasts. 2026-03 Sub-City Real Estate Price Index Forecasting at Weekly Horizons Using Satellite Radar and News Sentiment https://d.repec.org/n?u=RePEc:arx:papers:2602.18572&r=&r=for Reliable real estate price indicators are typically published at city level and low frequency, limiting their use for neighborhood-scale monitoring and long-horizon planning. We study whether sub-city price indices can be forecasted at weekly frequency by combining physical development signals from satellite radar with market narratives from news text. Using over 350, 000 transactions from Dubai Land Department (2015-2025), we construct weekly price indices for 19 sub-city regions and evaluate forecasts from 2 to 34 weeks ahead. Our framework fuses regional transaction history with Sentinel-1 SAR backscatter, news sentiment combining lexical tone and semantic embeddings, and macroeconomic context. Results are strongly horizon dependent: at horizons up to 10 weeks, price history alone matches multimodal configurations, but beyond 14 weeks sentiment and SAR become critical. At long horizons (26-34 weeks), the full multimodal model reduces mean absolute error from 4.48 to 2.93 (35% reduction), with gains statistically significant across regions. Nonparametric learners consistently outperform deep architectures in this data regime. These findings establish benchmarks for weekly sub-city index forecasting and demonstrate that remote sensing and news sentiment materially improve predictability at strategically relevant horizons. Baris Arat Hasan Fehmi Ates Emre Sefer 2026-02 Nowcasting GDP Growth for Kenya https://d.repec.org/n?u=RePEc:imf:imfwpa:2026/032&r=&r=for This paper develops a nowcasting model to produce timely estimates of quarterly GDP growth for Kenya. Nowcasting combines official monthly indicators with digital transaction data. Exploiting strong comovement of macroeconomic time series, a few latent factors summarize aggregate dynamics and enhance forecasts. The model is updated with each data release, decomposing revisions into predictable and news components. Results demonstrate robust performance of the nowcasting model in data-constrained environments and show that nowcasting is applicable to low-income countries. Nikolay Danov Domenico Giannone Alain N. Kabundi Cedric I Okou Mr. Antonio Spilimbergo Nowcasting; Dynamic factor models; Forecasting 2026-02-20 Kalshi and the Rise of Macro Markets https://d.repec.org/n?u=RePEc:fip:fedgfe:102839&r=&r=for Prediction markets offer a new market-based approach to measuring macroeconomic expectations in real-time. We evaluate the accuracy of prediction market-implied forecasts from Kalshi, the largest federally regulated prediction market overseen by the CFTC. We compare Kalshi with more traditional survey and market-implied forecasts, examine how expectations respond to macroeconomic and financial news, and how policy signals are interpreted by market participants. Our results suggest that Kalshi markets provide a high-frequency, continuously updated, distributionally rich benchmark that is valuable to both researchers and policymakers. Anthony M. Diercks Jared Dean Katz Jonathan H. Wright Forecasts; Monetary policy; Information economics; Inflation expectations 2026-02-18 Can satellites predict oil demand? https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263198&r=&r=for We investigate whether satellite observations of nitrogen dioxide (NO₂) – a short-lived pollutant primarily emitted by fossil fuel combustion – can improve the forecasting of oil demand. After retrieving, cleaning, and aggregating daily satellite data, we integrate NO₂ into a range of forecasting models. Across a panel of advanced and emerging economies, we find that including NO₂ significantly enhances nowcasting accuracy relative to benchmark models based on autoregressive terms and traditional predictors such as industrial activity, prices, weather, and vehicle registrations. Accuracy gains are particularly strong during crisis episodes but remain present in more stable times. Non-linear models, especially neural networks, yield the largest improvements, highlighting the non-linear link between energy demand and pollution. By offering a timely, globally consistent, and freely available proxy, satellite-based NO₂ data provide a valuable new tool for real-time monitoring of oil dema JEL Classification: C51, C81, E23, E37 Bricongne, Jean-Charles Meunier, Baptiste Macalos, Joao Milis, Julia Pical, Thomas big data, energy consumption, machine learning, nowcasting, satellite data 2026-02 A probabilistic measure to state fragility https://d.repec.org/n?u=RePEc:unu:wpaper:wp-2026-20&r=&r=for This paper develops a novel measure of state fragility: the probability of state failure. We define state failure as the inability to perform core functions and operationalize it through observable breakdowns—conflict, territorial control, institutional deficiency, and public service deterioration. Using a machine-learning approach, we estimate failure probabilities for 160 countries and assess their predictive performance and key predictors. The proposed measure is forward-looking, continuous, comparable across countries and over time, and exhibits strong predictive power. Joan Margalef State fragility, State failure, Measurement, Forecasting 2026 A Novel Quarterly Macroeconomic Forecasting Framework: Illustration on the Case of Bosnia and Herzegovina https://d.repec.org/n?u=RePEc:imf:imfwpa:2026/036&r=&r=for This paper describes the Quarterly Macro Forecasting Framework (QMFF), which is a novel approach to macroeconomic policy analysis and forecasting. At the core of this framework is a Quarterly Projection Model, with main behavioral equations quantified as deviations of real variables from their trends. The model comprises a simultaneous system of calibrated equations that cover the main sectors of the Financial Programming and Policies framework, as well as key accounting restrictions within and across sectors. By explicitly accounting for trends observed in real variables and relative prices, the framework ensures a balanced growth path and constant expenditure shares relative to nominal GDP. The QMFF is sufficiently flexible to be adapted to different monetary frameworks and exchange rate regimes. After describing the framework, the paper illustrates its implementation for policy analysis and forecasting on the case of Bosnia and Herzegovina. The empirical work with the model includes not only calibration but also testing the model’s dynamic and in-sample simulation properties. Tibor Hlédik Rilind Kabashi Maria Arakelyan Belma Colakovic Elma Hasanović Semi-structural Modeling; Financial Programming; Macroeconomic Policies; Forecasting and Policy Analysis; Gap Models; Scenario Analysis; Bosnia and Herzegovina 2026-02-27 Model Selection in High-Dimensional Linear Regression using Boosting with Multiple Testing https://d.repec.org/n?u=RePEc:arx:papers:2602.19705&r=&r=for High-dimensional regression specification and analysis is a complex and active area of research in statistics, machine learning, and econometrics. This paper proposes a new approach, Boosting with Multiple Testing (BMT), which combines forward stepwise variable selection with the multiple testing framework of Chudik et al (2018). At each stage, the model is updated by adding only the most significant regressor conditional on those already included, while a family-wise multiple testing filter is applied to the remaining candidates. In this way, the method retains the strong screening properties of Chudik et al (2018) while operating in a less greedy manner with respect to proxy and noise variables. Using sharp probability inequalities for heterogeneous strongly mixing processes from Dendramis et al (2022), we show that BMT enjoys oracle type properties relative to an approximating model that includes all true signals and excludes pure noise variables: this model is selected with probability tending to one, and the resulting estimator achieves standard parametric rates for prediction error and coefficient estimation. Additional results establish conditions under which BMT recovers the exact true model and avoids selection of proxy signals. Monte Carlo experiments indicate that BMT performs very well relative to OCMT and Lasso type procedures, delivering higher model selection accuracy and smaller RMSE for the estimated coefficients, especially under strong multicollinearity of the regressors. Two empirical illustrations based on a large set of macro-financial indicators as covariates, show that BMT yields sparse, interpretable specifications with favourable out-of-sample performance. George Kapetanios Vasilis Sarafidis Alexia Ventouri 2026-02 Overreaction as an indicator for momentum in algorithmic trading: A Case of AAPL stocks https://d.repec.org/n?u=RePEc:arx:papers:2602.18912&r=&r=for This paper investigates whether short-term market overreactions can be systematically predicted and monetized as momentum signals using high-frequency emotional information and modern machine learning methods. Focusing on Apple Inc. (AAPL), we construct a comprehensive intraday dataset that combines volatility normalized returns with transformer-based emotion features extracted from Twitter messages. Overreactions are defined as extreme return realizations relative to contemporaneous volatility and transaction costs and are modeled as a three-class prediction problem. We evaluate the performance of several nonlinear classifiers, including XGBoost, Random Forests, Deep Neural Networks, and Bidirectional LSTMs, across multiple intraday frequencies (1, 5, 10, and 15 minute data). Model outputs are translated into trading strategies and assessed using risk-adjusted performance measures and formal statistical tests. The results show that machine learning models significantly outperform benchmark overreaction rules at ultra short horizons, while classical behavioral momentum effects dominate at intermediate frequencies, particularly around 10 minutes. Explainability analysis based on SHAP reveals that volatility and negative emotions, especially fear and sadness, play a central role in driving predicted overreactions. Overall, the findings demonstrate that emotion-driven overreactions contain a predictable structure that can be exploited by machine learning models, offering new insights into the behavioral origins of intraday momentum and the interaction between sentiment, volatility, and algorithmic trading. Szymon Lis Robert \'Slepaczuk Pawe{\l} Sakowski 2026-02 A New Method for Measuring Underlying Inflation in Türkiye https://d.repec.org/n?u=RePEc:tcb:wpaper:2605&r=&r=for In this study, we propose a trend inflation indicator by using the Multivariate Unobserved-Components Stochastic Volatility Outlier-Adjusted (MUCSVO) model to better capture the underlying inflation dynamics in Türkiye. Our measure effectively filters out temporary shocks and exhibits superior forecasting performance at horizons beyond three months. Moreover, results imply that the permanent component of inflation declined from 3.9 in October 2023 to 2.2 in June 2025. Services emerge as the dominant driver of trend inflation, contributing about 55% despite having only 31% of the consumption basket weight. These results highlight the importance of sectoral decomposition in understanding inflation persistence and improving monetary policy design. As an addition to the underlying trend inflation indicators currently monitored by the Central Bank of the Republic of Türkiye (CBRT), the MUCSVO model enhances the CBRT’s capacity to monitor underlying price dynamics. Merve Capan Ahmet Gulveren Tuba Ozsevinc Unobserved component models, Trend inflation, Inflation forecasting, Monetary policy design 2026 Machine learning mutual fund flows https://d.repec.org/n?u=RePEc:zbw:cfrwps:337467&r=&r=for We present improved out-of-sample predictability of future fund flows using state-of-the-art machine learning methods. Nonlinear machine learning models significantly outperform linear models in terms of out-of-sample R-squared. Using interpretable ML methods, we identify past flows and the Morningstar rating as the most important predictors for net- flows, while other past performance variables are of minor importance. We find that the importance of Morningstar ratings and expenses has increased over time. In addition, the interaction effect of past flows with the Morningstar rating has a substantial impact on future flows. Furthermore, our results demonstrate that machine learning-based fund flow predictions can be used to ex-ante differentiate between high and low-performing mutual funds. Finally, funds whose flow predictions can be improved the most using ML reveal the worst performance, consistent with the idea that liquidity management is particularly challenging for these funds. Fausch, Jürg Frigg, Moreno Ruenzi, Stefan Weigert, Florian Machine learning, fund flow prediction, big data, interpretable machine learning 2026 Looser, tighter, clearer: a new Financial Conditions Index for the euro area https://d.repec.org/n?u=RePEc:ecb:ecbwps:20263193&r=&r=for Financial Conditions Indices (FCIs) are a widely used tool for assessing the broader monetary policy stance beyond the central bank’s direct control. This paper presents a novel vector autoregressive (VAR) model that includes key macroeconomic variables and maps financial variables into a single index, named Macro-Finance FCI. The VAR coefficients and the FCI weights are estimated jointly in one step, ensuring a model-consistent microfinance feedback. The model-implied long-run mean of the index provides a neutral benchmark to which financial conditions converge when inflation is at target and output is at potential. For the euro area, the proposed FCI incorporates nine asset prices – including risk-free rates, sovereign spreads, risk assets, and the exchange rate – and assigns a dominant role to nominal interest rates. It outperforms existing indices in out-of-sample forecasts of inflation and output. A structural identification of supply, demand, and financial shocks indicates that financial conditions require up to one year to transmit to the real economy and almost up to two years to inflation. JEL Classification: C32, E44, E52 Bletzinger, Tilman Martorana, Giulia Mistak, Jakub financial conditions index, monetary policy, structural macro-finance VAR 2026-02