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on Forecasting |
| By: | Furno, Francesco; Giannone, Domenico |
| Abstract: | We propose a simple yet robust framework to nowcast recession risk at a monthly frequency in both the United States and the Euro Area. Our nowcast leverages both macroeconomic and financial conditions, and is available the first business day after the reference month closes. In particular, we argue that financial conditions are not only useful to predict future downturns–as emphasized by the existing literature–but they are also useful to distinguish between expansions and downturns as they unfold. We then connect our recession risk nowcast with growth-at-risk by drawing on the literature on distributional regressions and quantile regressions. Finally, we benchmark our nowcast with the Survey of Professional Forecasters (SPF) and show that, while both have a similar ability to identify downturns, the former is more accurate in correctly identifying periods of expansion. |
| Keywords: | Business cycles; Financial conditions; Macroeconomic forecasting; Risk modeling and forecasting |
| JEL: | E32 C32 C53 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19483 |
| By: | Hauzenberger, Niko; Huber, Florian; Klieber, Karin; Marcellino, Massimiliano |
| Abstract: | Macroeconomic data is characterized by a limited number of observations (small T), many time series (big K) but also by featuring temporal dependence. Neural networks, by contrast, are designed for datasets with millions of observations and covariates. In this paper, we develop Bayesian neural networks (BNNs) that are well-suited for handling datasets commonly used for macroeconomic analysis in policy institutions. Our approach avoids extensive specification searches through a novel mixture specification for the activation function that appropriately selects the form of nonlinearities. Shrinkage priors are used to prune the network and force irrelevant neurons to zero. To cope with heteroskedasticity, the BNN is augmented with a stochastic volatility model for the error term. We illustrate how the model can be used in a policy institution through simulations and by showing that BNNs produce more accurate point and density forecasts compared to other machine learning methods. |
| Keywords: | Bayesian neural networks; Model selection; Shrinkage priors; Macro forecasting |
| JEL: | C11 C30 C45 C53 E3 E44 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19381 |
| By: | Fan, Tony Q.; Liang, Yucheng; Peng, Cameron |
| Abstract: | Evidence from the laboratory and the field has uncovered both underreaction and overreaction to new information. We provide new experimental evidence on the underlying mechanisms of under- and overreaction by comparing how people make inferences and revise forecasts in the same information environment. Participants underreact to signals when inferring about underlying states, but overreact to the same signals when revising forecasts about future outcomes— a phenomenon we term “the inference-forecast gap.” We show that this gap is largely driven by different simplifying heuristics used in the two tasks. Additional treatments suggest that the choice of heuristics is affected by the similarity between statistics in the information environment and the statistic elicited by the belief-updating problem. |
| Keywords: | belief updating;inference;forecast revision;attribute substitution |
| JEL: | F3 G3 J1 |
| Date: | 2026–07–01 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:131071 |
| By: | Bassetti, Federico; Casarin, Roberto; Del Negro, Marco |
| Abstract: | We propose a nonparametric Bayesian approach for conducting inference on probabilistic surveys. We use this approach to study whether U.S. Survey of Professional Forecasters density projections for output growth and inflation from 1982 to 2022 are consistent with the noisy rational expectations hypothesis. We find that, in contrast to theory, for horizons close to two years there is no relationship whatsoever between subjective uncertainty and forecast accuracy for output growth density projections, both across forecasters and over time, and only a mild relationship for inflation projections. As the horizon shortens, the relationship becomes one-to-one as theory predicts. |
| Keywords: | Noisy rational expectations |
| JEL: | C11 C14 C53 C82 E31 E32 E37 |
| Date: | 2024–09 |
| URL: | https://d.repec.org/n?u=RePEc:cpr:ceprdp:19426 |
| By: | Runyao Yu; Yuchen Tao; Yujie Chen; Wentao Wang; Derek W. Bunn |
| Abstract: | Probabilistic K-line forecasting describes uncertainty in four complementary prices, namely open--high--low--close (OHLC). However, it introduces two consistency problems: quantile crossing and K-line crossing. Quantile crossing occurs when a higher-quantile forecast falls below a lower-quantile forecast, while K-line crossing occurs when the forecast low exceeds the open or close, or the forecast high falls below the open or close. Existing solutions generally address only one problem through output reordering, specialized architectures, or penalized training objectives. We propose K-line--Quantile Sequential Projection (KQSP), a parameter-free and training-free reconciliation method applicable to forecasts produced by any model. Compared with other crossing solutions, KQSP preserves predictive accuracy while producing substantially smaller corrections to the original forecasts. To mitigate model bias, we evaluate KQSP using various models, including pretrained foundation models. KQSP reduces both quantile and K-line crossing rates to zero for all test data undertaken. These results show that probabilistic K-line consistency can be enforced independently of forecast generation and without retraining. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.26792 |
| By: | Junyi Ye; Gargi Vijay Borde |
| Abstract: | Financial volatility is regime dependent, yet incorporating regime information into neural networks can also destabilize training. This paper asks where such information should enter a neural cross-sectional volatility forecasting model. We study five-day realized-volatility forecasts for 1, 027 U.S. equities using a rolling walk-forward evaluation framework in which information, model capacity, hyperparameter tuning, and random seeds are matched across architectures. We propose RG-ResMoE, a regime-gated residual mixture-of-experts architecture in which regime information is used only for expert routing rather than for direct forecasting. The base predictor models volatility from stock features, while a gating network uses regime state variables to route residual corrections. RG-ResMoE consistently outperforms a capacity-matched MLP in both forecasting accuracy and training stability in the main U.S. study. Similar gains are observed on an independent Japanese panel. The integration pathway is decisive: appending the same regime variables directly to the forecasting input degrades both predictive performance and training stability, whereas restricting them to the routing gate improves accuracy and Value-at-Risk calibration. Hard routing consistently underperforms soft routing. The results suggest that, in compact neural volatility forecasting models, the primary value of mixture-of-experts models lies less in increasing model capacity than in controlling how nonstationary regime information influences prediction. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.12251 |
| By: | Travis L. Johnson; Jiannan Jiang; Soumyabrata Chaudhuri; Yihao Chen; Lauren Falvey; Donal O'Cofaigh |
| Abstract: | Specialist training beats generalist scale when forecasting financial statements. To our knowledge, no prior work jointly forecasts complete financial statements beyond one year, yet in a discounted-cash-flow valuation most firm value sits past that window. We release ProForma-20Q, a reproducible benchmark for forecasting 78 statement line items 1-20 quarters ahead, for anonymized firms, from past statements and an industry code, scored by change-space $R^2$. On it, Forma, a transformer that reads statements as sets of (account, quarter, value) tuples and maximizes a masked-tuple Gaussian likelihood, beats every competitor we field: classical machine learning, chained gradient boosting, a zero-shot time-series foundation model, and frontier large language models. Its lead widens with horizon, where valuation needs accuracy most, and its Gaussian predictive intervals never under-cover. Forma's forecasts nearly satisfy accounting identities; exact coherence is recoverable at no statistically significant accuracy cost. Its tuple interface supports scenario analysis without retraining, and we show that pinning future revenue paths sharpens the rest of the statement. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.11327 |
| By: | Esteban Sánchez-Gómez (Economic Division, Central Bank of Costa Rica) |
| Abstract: | This paper evaluates the performance of machine learning (ML) methods for forecasting year-over-year inflation in Costa Rica using monthly data from 2012-2025 and compares their performance against standard benchmarks within a rolling out-of-sample framework. ML techniques are particularly useful for capturing nonlinearities and complex interactions between inflation and a broad set of macroeconomic covariates. The results show that nonlinear ensemble methods such as XGBoost and BART provide the strongest gains at short horizons, while linear shrinkage methods are more competitive at longer horizons. ***Resumen: Este documento evalúa el desempeño de los métodos de aprendizaje automático (ML) para pronosticar la inflación interanual en Costa Rica, utilizando datos mensuales de 2012 a 2025 y comparándolos con estándares de referencia dentro de un esquema de muestra móvil fuera de muestra. Las técnicas de ML son especialmente útiles para captar no linealidades e interacciones complejas entre la inflación y un amplio conjunto de variables macroeconómicas. Los resultados muestran que los métodos de ensamblaje no lineal como XGBoost y BART presentan los mayores beneficios en horizontes cortos, mientras que los métodos de reducción lineal son más competitivos en horizontes largos. |
| Keywords: | Inflation, Macroeconomic Forecasting, Machine Learning, Monetary Policy, Hybrid Models, inflación, Pronósticos, Aprendizaje automático, Política Monetaria, Modelos Híbridos. |
| JEL: | C53 C33 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:apk:doctra:2606 |
| By: | Muzi Chen; Difang Huang; Shouyang Wang; Xinghan Xia |
| Abstract: | Firms covered by emissions trading systems need forecasts not only to value allowances, but also to decide when to buy them. This paper asks whether European Union Allowance (EUA) prices contain short-horizon predictability that survives a forecast-origin information design and improves simulated compliance procurement. Using daily data from 2019 to 2025, we produce direct forecasts for one to five trading days ahead. All predictors are observable at the forecast origin, and calibration and model-selection rules are fixed before the final holdout. The released forecast has the lowest point-estimate RMSE at every horizon among fourteen benchmarks, with the strongest loss-difference evidence at horizons three and four. Relative to a random walk, out-of-sample R^2 rises from 1.2% at one day to 15.5% at five days. We then use the forecast path in a constrained procurement problem with execution costs, market impact, capacity limits, and tail risk; sensitivity exercises add demand uncertainty. For a fixed 100, 000-EUA order, optimized schedules lower average realized costs by 8.5 to 38.5 basis points relative to uniform execution across horizons h=2 to h=5. The gains come from reallocating purchases within a fixed window, not from reliable next-day directional timing. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.23426 |
| By: | Junyi Ye; Ivy Gateri Wanjiku |
| Abstract: | Financial forecasting models are typically developed in full precision, yet production deployment often requires low-precision inference to reduce memory and computational cost. Post-training quantization (PTQ) enables such deployment without retraining. However, reliable activation quantization requires calibration: activation ranges are estimated from historical data before deployment and then remain fixed during future inference. The importance of this deployment choice for financial forecasting remains poorly understood. We present a systematic study of activation calibration for PTQ in cross-sectional volatility forecasting on the S&P 500. Our evaluation covers seven representative neural architectures, eight walk-forward test years (2018-2025), and 560 trained models. We find that activation calibration has little effect at 8 bits but becomes the primary determinant of predictive performance at 4 bits. Under default absolute-maximum (abs-max) calibration, static 4-bit quantization of both weights and activations removes 11-62% of the full-precision mean information coefficient in affected architectures. Replacing abs-max with percentile calibration recovers 53-94% of this degradation in the four most affected architectures. The preferred activation range also varies across market periods. Narrow ranges improve resolution under typical market conditions but lose part of their advantage when test-period market dispersion exceeds the calibration history. These findings show that activation calibration is a first-class deployment decision for reliable 4-bit PTQ in financial forecasting. When substantial degradation remains, 8-bit activations or weight-only 4-bit quantization provide more robust deployment choices. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.12259 |
| By: | Andrea Bastianin (University of Milan, Italy and Fondazione Eni Enrico Mattei (FEEM)); Elisabetta Mirto (Study Center Gerzensee); Yan Qin (ClearBlue Markets); Luca Rossini (University of Milan, Italy and Fondazione Eni Enrico Mattei (FEEM)) |
| Abstract: | We tackle the issue of producing point, sign, and density forecasts for the monthly real price of carbon within the European carbon market, EU ETS. We show that a Bayesian Vector Autoregressive (BVAR) model, augmented with factors based on macroeconomic and financial variables, yields accuracy gains over a set of benchmark forecasts in both point and density forecasts. We also provide a qualitative comparison of model-based forecasts with survey expectations and forecasts released by data providers. Moreover, we consider verified emissions and demonstrate that adding stochastic volatility can further improve the forecasting performance of a single-factor BVAR model. Lastly, we rely on forecasts to build market monitoring tools that track demand and price pressure in the EU ETS. |
| Date: | 2026–06 |
| URL: | https://d.repec.org/n?u=RePEc:szg:worpap:2603 |
| By: | Muhammad Abdullah Haroon |
| Abstract: | Bitcoin price prediction on sub-daily timescales is a hard open problem in computational finance. Bitcoin exhibits fat-tailed returns, non-stationary dynamics, and a price discovery process influenced by social discourse on Reddit and Twitter. Conventional approaches fuse OHLCV technical features with sentiment via static concatenation, applying identical fusion weights regardless of market state. This is inconsistent with the behavioural finance literature, which shows that retail sentiment is most predictive during volatile periods and noisy during calm ones. This paper proposes Regime-Aware Multi-Modal Learning (RAML), which conditions fusion of sentiment and price features on a dynamically detected binary market regime. Rolling 24-hour volatility partitions observations into stable and volatile regimes; a learnable sigmoid gate adjusts the weight of the sentiment embedding relative to the price embedding, trusting sentiment more during volatility and price dynamics more during stable phases. The system is evaluated on 3, 491 hourly observations (July 2024-September 2025), combining Bitcoin OHLCV data with Reddit /r/Bitcoin FinBERT sentiment. Four models are compared - price-only BiLSTM, sentiment-only classifier, static-concatenation BiLSTM, and RAML - across 3-hour and 6-hour horizons, with an ablation study isolating the sentiment branch, regime detection, and adaptive fusion. RAML achieves macro-F1 of 0.5474 (3h) and 0.5513 (6h), with the highest AUC at 3 hours (0.5084), indicating better calibration. Ablation confirms every component is necessary, and replacing adaptive weighting with concatenation causes recall collapse at 6 hours (F1: 0.14). These results establish regime-conditioned adaptive fusion as a necessary design principle for multi-modal financial forecasting. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.23370 |
| By: | Lukasz Adamski; Robert Slepaczuk |
| Abstract: | Our primary goal is to forecast and empirically examine the evolution of the implied volatility (IV) surface, with particular focus on the dates of scheduled meetings of the Federal Open Market Committee (FOMC). Firstly, we check if IV increases before the announcement and if thes effect is stronger for short-dated, out-the-money (OTM) options in high volatility regimes. In the second part, we turn the focus to verifying if the ML framework can beat the benchmark random walk in forecasting this effect. A feature related to dates of scheduled FOMC meetings augments the model, which allows us to discover if it can learn the effect of elevated pre-announcement uncertainty. Our contribution relies mainly on the quantitative prediction of the pre-announcement effect and the inclusion of exogenous information inside the ML framework used for the IV surface forecasting. It is also on of the first attempts to apply ML models directly on the IV surface without relying on dimensionality reduction. To achieve this, we employ a convolutional two-dimensional LSTM model, which is capable of learning spatio-temporal signals in the surface. Our analysis reveals that the edge of the ML framework can be limited due to the noisy characteristics of the IV surface. Nevertheless, our study reinforces the perspective that ML models can effectively forecast the IV surface also during abnormal days. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.10693 |
| By: | Hongyu Lin; Yulin Chen; Yuanrong Wang; Antonio Briola; Tomaso Aste |
| Abstract: | Using neural networks for stock return prediction typically requires choices about depth and hidden-layer width that are difficult to connect to financial interpretation. We study an alternative: estimate dependence among firm characteristics with a Maximally Filtered Clique Forest (MFCF), then map its clique structure to a Homological Neural Network (HNN). The MFCF maximum clique size K is the only parameter controlling architectural complexity, and it has a clear graphical meaning: it bounds the number of characteristics in each maximal clique and hence the highest interaction order the network can represent. The filtered graph then fixes the neural network's depth, layer widths, and sparse connections before training, in place of a separately chosen depth and width sequence. We apply two HNN variants to annual out-of-sample forecasts of U.S. stock excess returns from 1987 to 2016 using 94 firm characteristics. The HNN models match a three-hidden-layer benchmark on pooled predictive accuracy, rank the cross-section more accurately, and use roughly 80 times fewer parameters than a fully connected network with the same induced layer widths. Two structural ablations indicate that both the sparse connectivity and the estimated grouping of characteristics contribute to the ranking advantage, and both effects remain significant after correcting for multiple testing. These findings show that HNNs offer a practical and interpretable way to incorporate estimated dependence among firm characteristics into neural architecture design. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.14323 |
| By: | Paudel, Susan; Ramsey, A. Ford |
| Abstract: | We revisit the generalized inverse and ordinary demand systems for fish using four decades of monthly Japanese seafood data (1985–2025), a period over which the market shifted from domestic catch to heavy import dependence. The earlier consensus no longer holds. The ordinary specification is rejected under every instrument set, but the inverse specification is also rejected when the full set of instruments is used, so the preferred model depends on which instruments are chosen. Additionally, lag and macroeconomic instruments that once identified the supply side have also lost most of their explanatory power. Together these results indicate that the structure of the market has changed and that a single demand model may no longer describe the market well. Faced with the model selection uncertainty, we examine whether combining the competing forecasts is preferable to selecting one. Using unconditional rolling-window forecasts and a range of combination methods, we find that no single model forecasts best at every horizon, while the combinations are at least as accurate as the individual models and never as poor as the weakest among them. The gains from combination are small, because the demand systems are very similar yielding similarl forecasts. Combination therefore offers a reliable hedge against choosing the wrong model, even where it does not clearly improve on the best one |
| Keywords: | Marketing |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ags:aaea26:404527 |
| By: | Alireza Kargarzadeh; Nariman Khaledian; Navid Parvini; Arman Khaledian |
| Abstract: | Large language models can extract richer signals from financial news than fixed sentiment lexicons, and recent work has explored feeding such signals into portfolio construction. We study an uncertainty-aware construction that feeds model-predicted risk -- decomposed into aleatoric and epistemic components -- directly into the covariance matrix of portfolio allocators, rather than treating portfolio risk as fixed or adjusting only expected returns. We evaluate the pipeline on Russell 2000 equities under three stock-selection regimes: a pure-alpha trigger that isolates abnormal stock moves not explained by macro indicators, a pure-beta trigger that captures macro-indicator moves before the stock itself fires, and a beta trigger in which both channels agree. Across the full holding-period grid, the separated pure-alpha and pure-beta legs usually dominate the beta intersection on Sharpe and return. Two horizons are especially informative. At one day, pure beta can work under low and moderate transaction costs because it captures immediate lead-lag spillovers from liquid macro and sector indicators into exposed small-cap stocks, but this advantage disappears at 100 bps when turnover and microstructure noise dominate. At 40 days, pure beta works for a different reason: slower macro repricing overtakes the firm-specific pure-alpha channel. The strongest conservative row is pure beta with GPT-4o mini sentiment, a Student-t target, a 40-day holding period, and risk parity allocation, reaching Sharpe 2.33 at 100 bps. The results suggest that stock-selection regime and allocator choice matter at least as much as the sentiment model, and that separating firm-specific and macro-exposure triggers is more informative than requiring both to fire simultaneously. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.12283 |
| By: | Gerhard Hellstern; Danyal Maheshwari; Martin Zaefferer; Martin Braun; Tanja D\"ohler |
| Abstract: | In this study, we developed and evaluated four conditional energy-based forecasting architectures: a classical Gaussian-Bernoulli CRBM, a hybrid quantum-classical QCRBM, a full-register QQRBM, and a lag-feature QFeatureQRBM with complete derivations of their conditional distributions, Contrastive-Divergence gradients, and hybrid training, bridging the energy-based formulation and the implementation-level quantum computation. Unlike prior comparisons, our evaluation enforces symmetric hyperparameter optimisation: classical and quantum-specific hyperparameters receive an equally thorough grid search across thirteen structured experiments. We test on two data classes, a Gaussian-process dataset (GP) generated with real financial data and the input-driven NARMA-10 nonlinear benchmark. Across both regimes we find no systematic evidence of a quantum advantage at the available sample size: no quantum architecture improves on the best classical baseline. The fully quantum QQRBM and QFeatureQRBM are significantly worse, whereas the hybrid QCRBM is statistically indistinguishable from the strongest classical CRBM on both datasets. A power analysis bounds this null result: at n = 12 only medium-to-large effects are detectable, so small advantages cannot be excluded. An iso-parameter (matched-budget) comparison reaches the same conclusion: the classical CRBM is lowest at three of the four budgets and no CRBM-vs-QCRBM difference is significant at any budget. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.24065 |
| By: | Kasun Dewage; Suranadi De Silva; Shankhadeep Mondal |
| Abstract: | Foundation models for time series forecasting demonstrate impressive zero-shot generalization but often underperform on specialized domains such as high-frequency finance. We present a comprehensive study of hybrid neural-classical correction for adapting frozen TimesFM (200M parameters) to stock return prediction during the volatile opening trading hour. We compare two neural correction architectures - AttnCorrect (multi-head self-attention, approximately 471K parameters) and GatedLinear (low-rank bilinear projection with gating, approximately 49K parameters) - each augmented with Random Forest residual learning. Through systematic ablation across 10 major technology stocks (NVDA, MSFT, AAPL, GOOG, GOOGL, AMZN, META, AVGO, TSLA, NFLX) spanning 2 million data points, we reveal critical insights: (1) The hybrid neural-classical approach achieves 0.597 pooled correlation and 6.4x mean per-day correlation improvement over frozen TimesFM; (2) Classical residual learning (Random Forest) provides the largest single-component contribution, matching or exceeding the neural correction component; (3) Simpler neural architectures surprisingly outperform complex ones when classical residual learning is removed; (4) Self-attention provides the largest neural-only contribution. GatedLinear+RF achieves best overall performance with 9x fewer neural parameters than AttnCorrect+RF. We report three complementary correlation metrics - mean per-day, cross-day cumulative, and pooled - to provide a complete picture of predictive quality. Our results provide practical guidance: effective foundation model adaptation requires careful integration of neural and classical components, with classical methods playing a crucial complementary role. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.08825 |
| By: | Charisios Grivas; George Kapetanios; Zacharias Psaradakis; Vasilis Sarafidis; Marian Vavra; Alexia Ventouri |
| Abstract: | This paper proposes a nonlinear boosting with multiple testing (BMT) approach to variable selection in high-dimensional generalised linear models with binary responses. At each stage of the BMT procedure, the model is updated by adding only the most significant covariate, conditional on those already selected in previous stages, while taking into account the multiple testing nature of the problem. It is shown that, under the stated conditions, the BMT procedure selects all covariates whose true coefficients are nonzero, and no other covariates, with probability tending to one. Furthermore, the procedure enjoys an oracle property, in the sense that the post-BMT maximum likelihood estimator of the parameters of the model is asymptotically equivalent to an oracle estimator that knows the correct sparse model in advance. Monte Carlo experiments demonstrate that BMT outperforms competing methods, delivering high covariate-selection accuracy and low parameter estimation error. An empirical example illustrates that BMT delivers a predictive model for the probability that U.S. inflation exceeds a given threshold over a 12-month horizon which has very good out-of-sample performance. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.22440 |
| By: | Yannik Pitcan |
| Abstract: | Studies of association-football forecasting routinely report three-way accuracy in the low fifties and present it as competitive with the betting market. Accuracy against a uniform benchmark answers the wrong question; the question worth asking is whether a model carries information a margin-free closing price has not already absorbed. We formalise that test as the fitted weight in a logarithmic opinion pool and apply it to nineteen complete Serie A seasons (7, 220 matches). The answer is negative and stable. A Dixon-Coles model with tuned exponential decay attains 53.4% accuracy and a Ranked Probability Score of 0.1972 against the market's 0.1905; the paired difference is +0.0067 (95% CI [0.0046, 0.0088]) and the market wins in all seven test seasons. The fitted pooling weight on the structural model is 0.000, and the log-loss profile is monotone increasing in that weight on validation and test alike, so this is a boundary solution, not an optimisation artefact. Refitting the same machinery to shots on target yields a variant earning weight 0.35 against the goals model -- it carries information the goals model lacks -- and 0.000 against the market. Two structural signals, each informative about the other, both priced. The structural model is better calibrated than the market on the home-win margin (slope 0.995 versus 1.103) while clearly less sharp: the market's advantage is discrimination rather than honesty, which accuracy alone cannot distinguish. Value lies not in a better forecast but in what is built on a calibrated one. We define match leverage, the change in a club's probability of achieving a season objective between winning and losing a fixture, and compute it for ACF Fiorentina: an away fixture against a relegation rival carried 2.25x the leverage of hosting the eventual champions. The paper also documents and corrects errors in an earlier study of our own. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.11505 |
| By: | Rahma Mzouri (Faculté des Sciences Juridiques, Economiques et Sociales - UM5 - Université Mohammed V de Rabat [Agdal]); Abdelkrim Kandrouch (Faculté des Sciences Juridiques, Economiques et Sociales - UM5 - Université Mohammed V de Rabat [Agdal]) |
| Abstract: | Corporate failure prediction represents a major challenge for lenders, investors, and managers in a context characterized by increasing bankruptcy rates and growing economic uncertainty. Although discriminant analysis and logistic regression models have been extensively employed in the bankruptcy prediction literature, comparative studies incorporating the Partial Least Squares (PLS) method remain relatively limited, particularly in contexts characterized by high multicollinearity among financial variables.This study aims to compare the predictive performance of Linear Discriminant Analysis (LDA), Logistic Regression (Logit), and the PLS method in forecasting corporate failure.The study is based on a balanced sample of 200 Moroccan firms, including 100 failed companies and 100 non-failed companies. Thirty-three financial ratios covering financial structure, liquidity, solvency, profitability, activity, and growth were analyzed over three forecasting horizons prior to failure (T-1, T-2, and T-3). Discriminating variables were selected using Wilks' Lambda and Fisher's statistic before being incorporated into the different prediction models.The results suggest that the Logit model provides the best short-term predictive performance, achieving a classification accuracy of 93.4% at T-1, compared with 91.2% for discriminant analysis and 90.8% for the PLS method. At longer forecasting horizons, the PLS approach appears to be the most robust, with a classification accuracy of 83.2% at T-3, outperforming both discriminant analysis (78.4%) and Logistic Regression (81.3%). Ratios related to working capital, working capital requirements, solvency, and profitability emerge as the most relevant indicators for the early detection of financial distress.These findings highlight the relevance of Logit and PLS approaches for the development of early warning systems and credit risk scoring models used by financial institutions and decision-makers. |
| Abstract: | La prévision de la défaillance des entreprises constitue un enjeu majeur pour les établissements de crédit, les investisseurs et les dirigeants, dans un contexte marqué par l'augmentation des faillites et l'incertitude économique. Bien que les modèles d'analyse discriminante et de régression logistique aient été largement mobilisés dans la littérature, les comparaisons intégrant la méthode Partial Least Squares (PLS) demeurent relativement limitées, notamment dans les contextes caractérisés par une forte multicolinéarité entre les variables financières. Cette recherche vise à comparer les performances prédictives de l'analyse discriminante linéaire (AD), de la régression Logit et de la méthode PLS dans la prévision de la défaillance des entreprises.L'étude repose sur un échantillon équilibré de 200 entreprises marocaines, composé de 100 entreprises défaillantes et 100 entreprises saines. Trente-trois ratios financiers couvrant la structure financière, la liquidité, la solvabilité, la rentabilité, l'activité et la croissance ont été analysés sur trois horizons temporels précédant la défaillance (T-1, T-2 et T-3). Les variables discriminantes ont été sélectionnées à l'aide du test de Wilks et de la statistique de Fisher, puis intégrées dans les différents modèles de prévision.Les résultats suggèrentque la régression Logit présente la meilleure performance prédictive à court terme avec un taux de bonne classification de 93, 4 % à T-1, contre 91, 2 % pour l'analyse discriminante et 90, 8 % pour la méthode PLS. À horizon plus éloigné, la méthode PLS se révèle la plus robuste avec un taux de classification de 83, 2 % à T-3, supérieur à ceux obtenus par l'analyse discriminante (78, 4 %) et la régression Logit (81, 3 %). Les ratios liés au fonds de roulement, au besoin en fonds de roulement, à la solvabilité et à la rentabilité apparaissent comme les principaux indicateurs de détection précoce des difficultés financières.Ces résultats soulignent l'intérêt des approches Logit et PLS pour la mise en place de systèmes d'alerte précoce et de dispositifs de scoring du risque de crédit destinés aux institutions financières et aux décideurs. |
| Keywords: | Logistic Regression, Défaillance des entreprises Prévision de faillite Analyse discriminante Régression Logit Partial Least Squares Ratios financiers Risque de crédit. JEL classification : G32 C38 C51 M41 Recherche empirique Corporate Failure Bankruptcy Prediction Discriminant Analysis Logistic Regression Partial Least Squares (PLS) Financial Ratios Credit Risk. JEL Classification : G32, C38, C51, Credit Risk. JEL Classification : G32, Financial Ratios, Partial Least Squares (PLS), M41 Paper type : Empirical research, Discriminant Analysis, Bankruptcy Prediction, M41 Recherche empirique Corporate Failure, Risque de crédit. JEL classification : G32, Ratios financiers, Partial Least Squares, Régression Logit, Analyse discriminante, Prévision de faillite, Défaillance des entreprises |
| Date: | 2026–06–09 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05652823 |
| By: | Igor Halperin |
| Abstract: | We present a simple framework for dynamic portfolio management that uses nothing but daily prices, trading volumes, and market capitalizations. Its state is three fixed-size matrices built from the price history: the distance matrix of the return correlations and the transition matrices of two Markov chains that rank the S\&P 500 names monthly by trailing return and by trailing volatility. These three matrices rest on the price history alone, the same information Markowitz mean-variance optimization draws on, but they replace its expected-return vector and covariance matrix. Our method requires no matrix inversion, works on outlier-robust cross-sectional ranks, and is dynamic rather than single-period. Empirically the volatility rank is forecastable one step ahead while the return rank stays close to unforecastable. A portfolio built on the forecasts, a market-neutral momentum long-short blended with an opportunistic long-only sleeve, beats the market on two non-overlapping out-of-sample test sets, January 2022 to December 2024 and January 2025 to July 2026, at Sharpes of $1.06$ and $1.32$ against the market's $0.78$ and $1.14$, respectively, net of a five-basis-point trading cost and marked to market daily. It also outperforms the classical minimum-variance and maximum-diversification portfolios. Diversifying the long sleeve by residual distance adds a further edge on both periods, lifting the Sharpe to $1.08$ and $1.44$ and the annualized return from $18\%$ to $20\%$ and from $44\%$ to $56\%$, respectively. A convex information-leader overlay separately insures the market-neutral sleeve, buying convexity and a shallower drawdown at a small cost in return, the Sharpe unchanged. |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2607.27461 |