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

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  • 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