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on Forecasting |
| By: | Oguzhan Cepni (Center for Economics and Policy Studies, Istinye University, Istanbul, Turkiye; Ostim Technical University, Ankara, Turkiye; Department of Economics, Copenhagen Business School, Denmark); Rangan Gupta (Department of Economics, University of Pretoria, Private Bag X20, Hatfield 0028, South Africa) |
| Abstract: | We examine whether panel machine learning and mixed-frequency indicators improve fore- casts of quarterly real GDP growth across the 50 U.S. states. Using weekly, monthly and quarterly predictors, we compare six models under state-specific, global and grouped estimation at horizons of one to four quarters. The evaluation covers 2015--2026 and uses final- vintage data aligned with an assumed publication calendar. Pooled forecasts have roughly half the mean squared forecast error of a random walk benchmark over the full sample. Outside the pandemic window, the globally pooled forecast combination reduces MSFE by approximately 45 percent--56 percent relative to the random walk, with statistically significant improvements at each forecast horizon. Pooled implementations generally outperform separate state estimation at longer horizons, while differences among pooled structures are small. Model rankings vary across horizons and evaluation samples, and forecast accuracy differs across states. Extreme pandemic observations affect subsequent forecasts as they move through the mixed-frequency lag vector; excluding the collapse and rebound targets from estimation and limiting extreme predictors reduces this deterioration. The findings support combining timely regional indicators within pooled forecasting systems and evaluating their performance across horizons, states and economic conditions. |
| Keywords: | regional forecasting, panel data, mixed-frequency data, machine learning, forecast evaluation, GDP growth |
| JEL: | C53 C55 E37 R11 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:pre:wpaper:202632 |
| By: | Paulo Júlio |
| Abstract: | This paper assesses whether pretrained time-series foundation models and locally trained deep neural networks can make a useful contribution to central bank forecasting in a small, data-constrained economy. We compare TimesFM, Chronos-2, Moirai 2.0, and several locally estimated architectures with conventional statistical and econometric benchmarks and Banco de Portugal official projections. The applications cover monthly inflation, direct quarterly macroeconomic forecasting, and mixed-frequency nowcasting. All headline exercises use historical-vintage information and final data as evaluation truth. Foundation models are generally more competitive than locally trained neural networks and, for selected targets and horizons, match or outperform simple benchmarks and official projections. These gains are not uniform. Official projections remain particularly strong when judgment, granular or advance information, and exceptional economic events matter, while persistent or irregular series can leave little scope for deep-learning improvements. Locally trained and native mixed-frequency models nevertheless add useful signals for selected real and labor-market aggregates. The results therefore favor using deep-learning forecasts as systematic, reproducible complements to official projections rather than as universal replacements. |
| JEL: | C45 C53 E27 E31 E37 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ptu:wpaper:w202612 |
| By: | Christian Jobelius Schulz; Christoph Hanck; Simon Hirsch; Florian Ziel |
| Abstract: | We propose an online multivariate distributional forecasting model in which we condition the parameters of a vine copula on covariates. An online algorithm updates the parameters sequentially using only the most recent observations. Existing approaches require full batch re-estimation using all available data at each update step, which makes them computationally slow in large-scale forecasting applications and less suitable for settings in which dependence structures change over time. Simulation results show that the online procedure achieves forecasting performance comparable to that of the batch estimator, while reducing computation time substantially. We illustrate the method in a forecasting application to electricity net-demand across five regions of Great Britain and show that (i) the conditional vine copula model outperforms unconditional Gaussian and vine copula benchmarks and (ii) that online estimation improves forecast accuracy when batch re-estimation is not available. The algorithm is implemented in the Python package ondil. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.23119 |
| By: | Jiahao Weng; Jiyu Liu |
| Abstract: | Time-series observations are particularly susceptible to noise, and many real-world series have low signal-to-noise ratios (SNRs) together with heavy-tailed and time-varying disturbances. When Gaussian diffusion models are used for forecasting, reversing all the way to the zero-noise endpoint may yield an overly narrow and overconfident predictive distribution. We propose SHADE, an alpha-stable L\'evy diffusion forecaster that reads out at a nonzero depth so that the residual stable kernel acts as distribution-level shrinkage. By decomposing halting risk into smoothing bias and estimation error, we develop a scale-calibrated halt for estimating the optimal shrinkage $\tau^*$ and a context-amortized halt that requires no explicit noise labels. Gains concentrate in the extreme heavy-tailed regime $\alpha\leq1.3$: Across synthetic noise settings, median CRPS improves by 21.7\%, while the NLL falls significantly; Cryptocurrency and foreign-exchange data provide further evidence of improved tail calibration in real-data implement. On near-light-tailed or high-SNR data, the model reverts to Gaussian diffusion or full denoising. SHADE maintains overall forecast error comparable to strong baselines while providing more honest tail uncertainty for heavy-tailed series. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.26358 |
| By: | Larissa Nawo (Associated student HEC Montréal) |
| Abstract: | How do financial conditions shape U.S. macroeconomic tail risk, and what is their incremental forecasting value beyond standard macroeconomic predictors? Using quarterly U.S. data from 1971Q1 to 2026Q1, this paper compares macro-only and macro-financial Bayesian VARs. First-lag financial-conditions coefficients are negative for GDP growth and positive for unemployment, with 95% posterior credible intervals excluding zero; second-lag coefficients have opposite signs. Adding financial conditions to the Gaussian VAR raises predicted probabilities of negative GDP growth by 1.5 and 2.5 percentage points at four- and eight-quarter horizons and lowers GDP tail quantiles. However, out-of-sample tests do not establish a general forecast-accuracy improvement. Student-t forecast innovations, simulated with the same Gaussian posterior parameter draws, produce heavier predictive tails. Their positive GDP log-score differences reverse when 2020Q1–2021Q4 forecast targets are excluded, while CRPS favors Gaussian innovations in several comparisons. Financial conditions change measured tail risk without consistently improving forecast accuracy. |
| Keywords: | Financial conditions; macroeconomic tail risk; recession risk; macroeconomic vulnerability |
| Date: | 2026–10–07 |
| URL: | https://d.repec.org/n?u=RePEc:ris:crcrmw:023696 |
| By: | Zhiyu Zheng |
| Abstract: | Since the 2021 GameStop short squeeze, r/WallStreetBets (WSB) has been associated with episodes of extreme volatility, and its text is increasingly used to forecast volatility. A model using only large language model (LLM) embeddings of WSB posts attains an out-of-sample $R^2$ of 0.13 for log realized variance. That number shows predictive power, but it does not reveal where the information comes from or how much the text adds beyond a firm's own volatility history. We answer these questions with a diagnostic protocol that nests embeddings on a heterogeneous autoregressive (HAR) benchmark, compares them with text-free firm-identity controls, shuffles the day-to-text pairing within firm-years to test day-specific alignment, and checks whether gains persist across periods and coverage groups. We apply the protocol to frozen LLM embeddings in a panel of 202, 145 mentioned stock-days, using retrospective rolling out-of-sample tests over 2015-2022. The results indicate that most of the standalone fit reflects firm or firm-year information: on firms seen in training, a text-free firm baseline reaches $R^2 = 0.32$, shuffling preserves 89-92% of the standalone fit, and masking ticker strings reduces standalone $R^2$ to -0.005. Adding embeddings to HAR reduces equal-date MSE by 1.22% (95% CI [0.94, 1.49]). This small but statistically significant gain survives firm controls and ticker masking, but is largely eliminated by alignment shuffling. The gain is concentrated in 2015-2019 and is not distinguishable from zero in 2020-2022. Expanding coverage by 48% through off-hours and weekend posts adds stock-days with no reliable positive increment. More mentions therefore do not imply more incremental forecasting information. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.25468 |
| By: | Junyu Chen; Franklin Liu; Moshu Xu; Lijian Yang |
| Abstract: | Modeling limit order book (LOB) dynamics is central to market forecasting and execution-strategy evaluation. While diffusion models achieve strong empirical performance, existing LOB snapshot models typically lack a learned recurrent encoder and formal statistical guarantees. We introduce LOBGrad, a conditional DDPM framework for probabilistic LOB forecasting that combines an autoregressive RNN encoder with a diffusion-based generator. In particular, we establish an excess-risk oracle inequality for the one-step DDPM noise-prediction objective under exponential mixing and regularity assumptions. We further show that, under contractive dynamics, the dependence on the conditioning-window length can be reduced to a dependence on the latent-state dimension. Experiments on BTC/USDT, GOOGL, and META show that LOBGrad improves on its encoder-free diffusion ablation across all reported queue metrics and obtains competitive results against other baselines. It also reproduces the key stylized facts of LOB microstructure, and yields reasonably calibrated queue-based predictive intervals. A stylized downstream task illustrates the potential value of the generated forecasts. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.25495 |
| By: | Jung Hun Phee; Tucker Balch |
| Abstract: | We study the conditional distribution of limit-order volume submitted over the next five minutes, given the current order book and recent order flow. The task requires capturing price placement, total volume, and predictive uncertainty jointly. Coordinate-wise diffusion losses do not explicitly account for price displacement, while normalizing fields to unit mass removes information about total volume. Agreement on selected summary statistics also does not establish fidelity to the joint field distribution. We introduce OrderFlowDiT, a conditional Diffusion Transformer for unnormalized bid and ask volume-at-price fields, trained with an unbalanced optimal-transport (UOT) auxiliary loss. We also develop a full-field evaluation framework that combines a transport cost sensitive to price placement and total volume with proper scoring rules and calibration diagnostics. In the primary comparison on 1950 validation conditions across 30 U.S. equities, adding UOT reduces full-field transport cost by 45.8% and the transport energy score by 26.1% relative to training with mean squared error alone. Both measures improve for every stock. These findings support incorporating price geometry and volume into generative modeling, with field accuracy and predictive uncertainty evaluated together. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.22727 |
| By: | Aadit Jerfy |
| Abstract: | Topological Data Analysis (TDA) and Persistent Homology (PH) have been utilized in numerous fields$-$from protein modeling to financial analysis$-$to understand the underlying structure of data. In this paper, we incorporate TDA and PH into forecasting models over a prediction market universe to determine whether the belief state of such markets forms an underlying structure whose shape can provide predictive insight. We conclude that single-parameter PH is able to improve predictive ability and calibrated accuracy of linear forecasting models, while two-parameter multipersistence produces similar benefits for nonlinear models. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.25756 |
| By: | Elahe Boskabadi; Hyeongwoo Kim; Peng Shao; Shuwei Zhang |
| Abstract: | This paper studies heterogeneity in how forecasters translate fiscal stimulus news into longer-term output expectations. Using individual-level data from the Survey of Professional Forecasters, we classify forecasters according to their relative positions in the distributions of federal government spending and real GDP forecasts. A state-space model with drifting coefficients estimates the time-varying pass-through from near-term spending forecast revisions to longer-term GDP forecast revisions. We find pronounced cross-sectional asymmetry early in the sample: lower-tail forecasters exhibit substantially weaker pass-through than upper-tail forecasters, reflecting more pessimistic responses to fiscal news. Over time, lower-tail pass-through rises markedly and converges toward the relatively stable response of optimists, narrowing the initial disagreement over the transmission of fiscal news. |
| Keywords: | Forecast Revisions; Survey of Professional Forecasters; Heterogeneous Beliefs; Kalman Filter; Kalman Smoother |
| JEL: | C32 D84 E62 E37 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:abn:wpaper:auwp2026-08 |
| By: | Matteo Smerlak |
| Abstract: | The value of a forecast depends on its accuracy, but also on its actionability. In trading, an investor must translate return forecasts into positions while accounting for trading costs and the lingering impact of earlier trades. We solve the stationary quadratic trading problem with transient price impact using tools from prediction theory: Wiener-Hopf factorization gives the optimal policy as the composition of a conditional forecast and an explicit causal linear filter. We recover the G\^arleanu-Pedersen aim-portfolio under exponential resilience and derive fractional trading filters under power-law impact decay, requiring memory beyond finite-dimensional LQR control. At fixed positive risk penalty, signals of equal instantaneous predictive strength have equal frictionless Markowitz value. With trading costs, net attainable value is determined by the mean square of the friction-weighted conditional forecast. Under power-law impact with exposure bleeding, we find an intermediate optimal mean-reversion rate for Ornstein-Uhlenbeck signals. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.24558 |
| By: | Levent Bulut |
| Abstract: | Older observations can help a forecaster learn a predictive relationship while becoming misleading when that relationship changes. We study this tension using paired return histories that differ in older observations but share recent returns and the conditional distribution of the next return. Across five foundation-model forecasting procedures and three simulated processes, each evaluated on 1, 024 independent replications, conflicting history reduces the probability of improving on zero in all 30 higher-signal comparisons. This loss of usefulness occurs despite successful signal recovery under stable conditions. A separate exploratory comparison on 56 assets finds that shortening inputs from 256 to 64 returns increases mean absolute error for every model by 1.77-4.58 basis points; neither length improves observed overall error over zero. The evidence distinguishes the ability to extract a signal from the ability to withstand conflicting history, while showing why context sensitivity alone does not justify discarding older data. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.25822 |
| By: | Kevin Foley; Jonathan Hartadi; Shivesh Prakash; Charishma Takkallapalli; Swapnil Vatsal |
| Abstract: | Language models that read financial news are usually trained on text from the same years used to test them, so a backtest can reward hindsight. We build news topics from NoLBERT, an encoder pretrained only on text published between 1976 and 1995, adapted into a sentence encoder by contrastive fine-tuning on passages of the same article, with no labels or returns. We compare it with LDA, MiniLM and FinBERT readers on 1.26 million articles from 2012 to 2024, convert each reader's topics into attention shocks and stock exposures, and forecast the returns of the 1, 000 largest US stocks with eight models at three rebalancing frequencies. Models are fitted on 2013-2019, choices are made on 2020-2021, and 2022-2024 is held out. The experiment with the best validation score loses money at every frequency. Measuring attention shocks against the past 63 trading days instead of 5 raises the test rank IC of every reader. NoLBERT gives the most stable topics and forecasts about as well as LDA. An equal-weight combination of all readers and models earns a monthly net Sharpe ratio of 1.12, significant against zero before adjusting for the search but not against a price-only benchmark. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.22745 |
| By: | Martin Geiger (Liechtenstein Institute and University of Innsbruck); Iacovos Sterghides (Central Bank of Cyprus); Marios Zachariadis (University of Cyprus) |
| Abstract: | Utilizing a large household-level dataset of inflation expectations for twelve euro-area economies, we attempt to assess the formation and accuracy of inflation expectations following major disruptions of the macroeconomy which we identify during the period from 2004:1 to 2025:2. We find that the arrival of such adverse events tends to increase the degree of inaccuracy in inflation expectations. We also find that this happens because inflation expectations tend to go up in response to these adverse shocks relative to the 12-month ahead inflation realizations, which offers direct evidence of households overestimating inflation. This is consistent with overreaction of inflation expectations in response to inflationary news and with inflation-as-a-bad behavioral patterns in response to adverse non-inflationary shocks. We infer that such behavioral forces appear to have played an important role in the formation of inflation expectations in the euro-area following adverse shocks during the past two decades. |
| Keywords: | forecast errors; shocks; behavioral; belief heterogeneity; Central Bank communication |
| JEL: | D84 E31 E70 |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:cyb:wpaper:2026-3 |
| By: | Carlos Montes-Gald\'on |
| Abstract: | Most empirical models of trend inflation treat it as a latent random walk driven by unobserved shocks, so they cannot say which economic conditions move it. Deep-$\pi^*$ makes the innovations to the trend a possibly nonlinear function of a large number of macroeconomic indicators, estimated with neural networks, so that Shapley values can be used to attribute every movement of the trend to the indicators behind it. Two complementary loss functions define a current trend, which fits inflation today, and a forward-looking trend, which best predicts inflation over the next year. In US data, employment-related variables were the largest contributors to the post-pandemic rise of the current trend. However, the forward-looking trend rose earlier, with real activity, employment, and money and credit as the main drivers, and peaked eight to twelve months before the current trend. Both trends looked through most of the movements in energy and commodity prices. The contributions of the indicators are nonlinear, with sizeable interactions between them. Re-estimated every month during the surge, the model forecast inflation more accurately than the standard UC-SV model and a random walk, and in early 2021, when professional forecasters expected inflation of 3% or less, the forward-looking trend was already signalling an increase in persistent inflation. |
| Date: | 2026–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2610.26298 |