Francesco Audrino
Title
Prof. Ph.D.
Last Name
Audrino
First name
Francesco
Email
francesco.audrino@unisg.ch
ORCID
Phone
+41 71 224 2431
SSRN
https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=333418
81 results
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Item type:Publication, HARd to beat: The overlooked impact of rolling windows in the era of machine learning(2026-02); Type:journal articleJournal:International Journal of ForecastingVolume:42Issue:2Scopus© Citations 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, HARd to Beat: The Overlooked Impact of Rolling Windows in the Era of Machine LearningWe investigate the predictive abilities of the heterogeneous autoregressive (HAR) model compared to machine learning (ML) techniques across an unprecedented dataset of 1,445 stocks. Our analysis focuses on the role of fitting schemes, particularly the training window and re-estimation frequency, in determining the HAR model’s performance. Despite extensive hyperparameter tuning, ML models fail to surpass the linear benchmark set by HAR when utilizing a refined fitting approach for the latter. Moreover, the simplicity of HAR allows for an interpretable model with drastically lower computational costs. We assess performance using QLIKE, MSE, and realized utility metrics, finding that HAR consistently outperforms its ML counterparts when both rely solely on realized volatility and VIX as predictors. Our results underscore the importance of a correctly specified fitting scheme. They suggest that properly fitted HAR models provide superior forecasting accuracy, establishing robust guidelines for their practical application and use as a benchmark. This study not only reaffirms the efficacy of the HAR model but also provides a critical perspective on the practical limitations of ML approaches in realized volatility forecasting.Type:journal articleJournal:International Journal of ForecastingVolume:Forthcoming - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The impact of macroeconomic news sentiment on interest rates(2024); Eric OffnerWe provide evidence that sentiment extracted from articles related to interest rates, inflation, and the labor market has the ability to explain short-term interest rate movements that cannot be accounted for by professionals’ and consumers’ expectations. Additionally, sentiment can pin down two short rate regimes that are correlated with the business cycle. By combining these results with a yield curve model, we find that market sentiment has a statistically significant negative effect on the short end of the yield curve and a positive effect on the slope. We also show that sentiment improves the out-of-sample forecast accuracy of short-term yields.Type:journal articleJournal:International Review of Financial AnalysisVolume:94Issue:July 2024Scopus© Citations 12 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Yield Curve Trading Strategies Exploiting Sentiment DataThis paper builds upon previous research findings that show macro sentiment data-augmented models are better at predicting the yield curve. We extend the dynamic Nelson-Siegel model with macro sentiment data from either Twitter or RavenPack. Vector autogressive (VAR) models and Markov-switching VAR models are used to predict changes in the shape of the yield curve. We build bond butterfly trading strategies that exploit our yield curve shape change predictions. We find that the economic returns from our trading strategies based upon models exploiting macro sentiment data do not statistically significantly differ from those which do not rely on it.Type:journal articleJournal:North American Journal of Economics and FinanceVolume:74Issue:September 2024Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, When does attention matter? The effect of investor attention on stock market volatility around news releases(2022-04-20); ; Sigrist, FabioWe empirically investigate how retail and institutional investor attention is related to the way stock markets process information. With a focus on 360 US stocks in the S&P 500 universe, our results show that higher retail investors’ attention around news releases increases the post-announcement stock return volatility, whereas in-stitutional investor attention has a small but negative impact on volatility on days following news releases on average over the cross-section of companies. These find-ings are in line with the hypotheses that attention of retail investors slows price-adjustments to new information and attention of institutional investors results in the opposite reaction. We show that these e˙ects are heterogeneous in the type of news and the topic of the information being released. A portfolio allocation ap-plication highlights that these results are not only statistically significant but also sizeable in economic terms and can lead to an overperformance as large as dozens of basis points.Type:journal articleJournal:International Review of Financial AnalysisVolume:82Issue:July 2022 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Lasso and the Factor Zoo - Predicting Expected Returns in the Cross-Section(2022-11-17); We investigate whether Lasso-type linear methods are able to improve the predictive accuracy of OLS in selecting relevant firm characteristics for forecasting the future cross-section of stock returns. Through extensive Monte Carlo simulations we show that Lasso-type predictions are superior to OLS when type II errors are a concern. The results change if the aim is to minimize type I errors. Finally, we analyze the predictive performance of the competing methods on the US cross-section of stock returns between 1974 and 2020 and show that only small and micro-cap stocks are highly predictable through-out the entire sample.Type:journal articleJournal:ForecastingVolume:4Issue:4 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Empirical Implementation of the Ross Recovery Theorem as a Prediction DeviceBuilding on the method of Ludwig (2015) to construct robust state price density surfaces from snapshots of option prices, we develop a nonparametric estimation strategy based on the recovery theorem of Ross (2015). Using options on the S&P 500, we then investigate whether or not recovery yields predictive information beyond what can be gleaned from risk-neutral densities. Over the 13 year period from 2000 to 2012, we find that market timing strategies based on recovered moments outperform those based on risk-neutral moments.Type:journal articleJournal:Journal of Financial EconometricsVolume:19Issue:2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Strongest team favoritism in European national football: Myth or reality?(2020-07-09)Are the financially and institutionally strongest clubs capable of systematically reaching the top positions in the European national football leagues treated differently in terms of awarded sanctions because of the external off the pitch pressure they can put on match officials? This study helps shed some light on this controversial question fiercely debated among fans and sports journalists and extends our knowledge of how football match officials may be unconsciously influenced by external (social) forces. Except for France where the evidence is weak, data analysis of the top five European leagues for the seasons from 2011-2012 to 2017-2018 provides empirical evidence supporting the existence of a referees' off the pitch strongest team bias. In fact, in England referees award significantly more yellow cards and total booking points (an aggregate measure of yellow and red cards) to the opponents' players, and in Italy, Germany and Spain significantly fewer yellow cards and total booking points are given to the top teams' players. The referees' strongest team bias comes on top of the referees' home bias discussed in the previous literature and displays a non-negligible size that can reach approximately the same size of the referees' home bias in some cases.Type:journal articleJournal:International Journal of Sport FinanceIssue:15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The impact of sentiment and attention measures on stock market volatility(2020); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Wild multiplicative bootstrap for M and GMM estimators in time series(2019-04-08);