Francesco Audrino
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Prof. Ph.D.
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Audrino
First name
Francesco
Email
francesco.audrino@unisg.ch
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+41 71 224 2431
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https://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=333418

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  • Publication
    Yield Curve Trading Strategies Exploiting Sentiment Data
    ( 2022-12)
    Audrino, Francesco
    ;
    Serwart, Jan
    This 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. Although 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, we find some evidence that models exploiting inflation sentiment are economically useful when trading the curvature of the yield curve.
    Type: conference paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/108013
  • Publication
    Sentiment spillover effects for US and European companies
    ( 2017-04-24)
    Audrino, Francesco
    ;
    Tetereva, Anastasija
    The fast-growing literature on the news and social media analysis provide empirical evidence that the financial markets are often driven by information rather than facts. However, the direct e˙ects of sentiments on the returns are of main interest. In this paper, we propose to study the cross-industry influence of the news for a set of US and European stocks. The graphical Granger causality of the news sentiments - excess return networks is estimated by applying the adaptive Lasso procedure. We introduce two characteristics to measure the influence of the news coming from each sector and analyze their dynamics for a period of 10 years ranging from 2005 to 2014. The results obtained provide insight into the news spillover e˙ects among the industries and the importance of sentiments related to certain sectors during periods of financial instability.
    Type: monograph
    Journal: Working paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/102449
  • Publication
    The (adaptive) Lasso in the Zoo - Firm Characteristic Selection in the Cross-Section of Expected Returns
    ( 2017-03-09)
    Messmer, Marcial
    ;
    Audrino, Francesco
    We find short-term reversal, the twelve-months momentum and research spending scaled by market-value to be the firm characteristics (FC) most robustly selected by the adaptive Lasso in the US cross-section of stock returns. Moreover, the majority of the 68 FC included in our analysis are not considered. Nonetheless, the return process we identify is multi-dimensional, comprising 14 FC. Additionally, our Monte Carlo Simulations indicate that the adaptive Lasso is superior to Lasso and OLS-based selection in panel specifications with a low signal-to-noise ratio. The results are robust to various assumptions. These findings gain support by an empirical out-of-sample factor analysis.
    Type: monograph
    Journal: Working paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/102552
  • Publication
    How does post-earnings announcement sentiment affect firms' dynamics? New evidence from causal machine learning
    ( 2020-12-22)
    Audrino, Francesco
    ;
    Chassot, Jonathan
    ;
    Huang, Chen
    ;
    Knaus, Michael
    ;
    Lechner, Michael
    ;
    Ortega Lahuerta, Juan-Pablo
    We revisit the role played by sentiment extracted from news articles related to earnings announcements as a driver of firms' return, volatility, and trade volume dynamics. To this end we apply causal machine learning on the earnings announcements of a wide cross-section of US companies. This approach allows us to investigate firms' price and volume reactions to different types of post-earnings announcement sentiment (positive, negative, and mixed sentiments) under various underlying macroeconomic and aggregated investors' moods in a properly defined causal framework. Our empirical results support the presence of (i) investors' overconfidence and mispricing due to biased expectations; (ii) a leverage effect in sentiment where reactions are (on average) larger for negative sentiment; and (iii) investors' underreaction to news. Finally, we show that the difference in the average causal effects of the sentiment's types is larger when the general macroeconomic conditions are worse or the uncertainty in the global financial market is higher.
    Type: working paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/111461
  • Publication
    Extending the logit model with Midas aggregation: The case of US bank failures
    ( 2018-01-19)
    Audrino, Francesco
    ;
    Kostrov, Alexander
    ;
    Ortega, Juan-Pablo
    We propose a new approach based on a generalization of the classic logit model to improve prediction accuracy in US bank failures. We introduce mixed-data sampling (Midas) aggregation to construct financial predictors in a logistic regression. This allows relaxing the limitation of conventional annual aggregation in financial studies. Moreover, we suggest an algorithm to reweight observations in the log-likelihood function to mitigate the class-imbalance problem, that is, when one class of observations is severely undersampled. We also address the issue of the classification accuracy evaluation when imbalance of the classes is present. When applying the suggested model to the period from 2004 to 2016, we show that it correctly classifies more bank failure cases than the reference logit model introduced in the literature, in particular for long-term forecasting horizons. This improvement has a strong significant impact both in statistical and economic terms. Some of the largest recent bank failures in the US that were previously misclassified are now correctly predicted.
    Type: working paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/100819
  • Publication
    Flexible HAR Model for Realized Volatility
    (University of St. Gallen, 2016-04-01)
    Audrino, Francesco
    ;
    Huang, Chen
    ;
    Okhrin, Ostap
    The Heterogeneous Autoregressive (HAR) model is commonly used in modeling the dynamics of realized volatility. In this paper, we propose a flexible HAR(1,...,p) specification, employing the adaptive LASSO and its statistical inference theory to see whether the lag structure (1, 5, 22) implied from an economic point of view can be recovered by statistical methods. Adaptive LASSO estimation and the subsequent hypothisis testing results show that there is no strong evidence that such a fixed lag structure can be exactly recovered by a flexible model. In terms of the out-of-sample forecasting, the proposed model slightly outperforms the classic specification and a superior predictive ability test shows that it cannot be significantly outperformed by any of the alternatives. We also apply the group LASSO and some related tests to check the validity of the classic HAR, which is rejected in most cases. The main reason for rejection might be the arrangement of groups, and a minor reason is the equality constraints on AR coefficients. This justifies our intention to use a flexible lag structure while still keeping the HAR frame. Finally, the time-varying behaviors show that when the market environment is not stable, the structure of (1, 5, 22) does not hold very well.
    Type: working paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/104478
  • Publication
    A general multivariate threshold GARCH model with dynamic conditional correlations (Revised Version of Paper no. 2005-04)
    (2007-25, VWA Discussion Papers Series, HSG St. Gallen, 2007)
    Audrino, Francesco
    ;
    Trojani, Fabio
    http://ideas.repec.org/p/usg/dp2005/2005-04.html
    Type: working paper
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/81271