arXiv subjects

Marco Zanotti

Publications and source records attributed to Marco Zanotti.

4 recordsLinked to original sources

arXiv2026

Do Short Exposure and Systematic Risk Exposure Drive Asymmetries in the Disposition Effect?

This study examines the disposition effect in both long and short exposure positions in FTSE MIB tracking ETFs using a unique dataset of almost 9 million individual transactions. Building on the integrated framing approach, we extend the analysis to explicitly incorporate leverage and long short exposures, allowing us to assess how portfolio context and systematic risk exposure jointly are associated to investors realization behavior. Methodologically, we generalize Odean canonical Count and Total measures to wide and integrated framing, introduce a novel Value metric that captures the return thresholds required to realize gains versus losses, and implement these measures in dispositionEffect, an open-source R package for large-scale intraday data. We show that short positions exhibit a weaker disposition effect than long positions under narrow framing, but that this asymmetry reverses in positively performing portfolios under integrated framing. Systematic risk further amplifies these behavioral asymmetries across positions. Overall, our findings demonstrate that the disposition effect is not solely asset-specific, but is critically shaped by the interaction between portfolio context, position type, and systematic risk exposure. More broadly, the results are consistent with the joint predictions of Prospect Theory and Regret Theory, highlighting the central role of framing in investor decision-making.

q-fin.RM↗

arXiv2025

Analyzing the retraining frequency of global forecasting models: towards more stable forecasting systems

Forecast stability, that is, the consistency of predictions over time, is essential in business settings where sudden shifts in forecasts can disrupt planning and erode trust in predictive systems. Despite its importance, stability is often overlooked in favor of accuracy. In this study, we evaluate the stability of point and probabilistic forecasts across several retraining scenarios using three large forecastingdatasets and ten different global forecasting models. To analyze stability in the probabilistic setting, we propose a new model-agnostic, distribution-free, and scale-free metric that measuresprobabilistic stability: the Scaled Multi-Quantile Change (SMQC). The results show that less frequent retraining not only preserves but often improves forecast stability, challenging the need for frequent retraining. Moreover, the study shows that accuracy and stability are not necessarily conflicting objectives when adopting a global modeling approach. The study promotes a shift toward stability-aware forecasting practices, proposing a new metric to evaluate forecast stability effectively in probabilistic settings, and offering practical guidelines for building more stable and sustainable forecasting systems.

stat.AP↗

arXiv2025

The cost of ensembling: is it always worth combining?

Given the continuous increase in dataset sizes and the complexity of forecasting models, the trade-off between forecast accuracy and computational cost is emerging as an extremely relevant topic, especially in the context of ensemble learning for time series forecasting. To asses it, we evaluated ten base models and eight ensemble configurations across two large-scale retail datasets (M5 and VN1), considering both point and probabilistic accuracy under varying retraining frequencies. We showed that ensembles consistently improve forecasting performance, particularly in probabilistic settings. However, these gains come at a substantial computational cost, especially for larger, accuracy-driven ensembles. We found that reducing retraining frequency significantly lowers costs, with minimal impact on accuracy, particularly for point forecasts. Moreover, efficiency-driven ensembles offer a strong balance, achieving competitive accuracy with considerably lower costs compared to accuracy-optimized combinations. Most importantly, small ensembles of two or three models are often sufficient to achieve near-optimal results. These findings provide practical guidelines for deploying scalable and cost-efficient forecasting systems, supporting the broader goals of sustainable AI in forecasting. Overall, this work shows that careful ensemble design and retraining strategy selection can yield accurate, robust, and cost-effective forecasts suitable for real-world applications.

cs.LG↗

arXiv2025

On the retraining frequency of global models in retail demand forecasting

In an era of increasing computational capabilities and growing environmental consciousness, organizations face a critical challenge in balancing the accuracy of forecasting models with computational efficiency and sustainability. Global forecasting models, lowering the computational time, have gained significant attention over the years. However, the common practice of retraining these models with new observations raises important questions about the costs of forecasting. Using ten different machine learning and deep learning models, we analyzed various retraining scenarios, ranging from continuous updates to no retraining at all, across two large retail demand datasets. We showed that less frequent retraining strategies maintain the forecast accuracy while reducing the computational costs, providing a more sustainable approach to large-scale forecasting. We also found that machine learning models are a marginally better choice to reduce the costs of forecasting when coupled with less frequent model retraining strategies as the frequency of the data increases. Our findings challenge the conventional belief that frequent retraining is essential for maintaining forecasting accuracy. Instead, periodic retraining offers a good balance between predictive performance and efficiency, both in the case of point and probabilistic forecasting. These insights provide actionable guidelines for organizations seeking to optimize forecasting pipelines while reducing costs and energy consumption.

stat.AP↗