Publication Date:
2018
abstract:
Google search data has proven to be useful in portfolio management. The basic idea is that high search volumes are related to bad news and risk increase.
This paper shows additional evidence about the use of Google search volumes in risk management. The empirical application is performed on the Standard & Poor
Industrial index components from 2004 to 2017. To overcome the (time-series and cross-section) limitations Google imposes on the data download, a re-normalization
procedure is presented, to obtain a multivariate sample of volumes which preserve their relative magnitude. Different ways to incorporate Google data are compared,
showing that the volumes’ normalization and the starting portfolio are decisive for the portfolio performances. Correctly normalized Google search volumes yield poor
results. This may lead to revise the interpretation of the search volume: it can be considered a risk indicator, but when used in an equally risk contribution portfolio,
no evidence of the improvement of the risk-return performances is found.
This paper shows additional evidence about the use of Google search volumes in risk management. The empirical application is performed on the Standard & Poor
Industrial index components from 2004 to 2017. To overcome the (time-series and cross-section) limitations Google imposes on the data download, a re-normalization
procedure is presented, to obtain a multivariate sample of volumes which preserve their relative magnitude. Different ways to incorporate Google data are compared,
showing that the volumes’ normalization and the starting portfolio are decisive for the portfolio performances. Correctly normalized Google search volumes yield poor
results. This may lead to revise the interpretation of the search volume: it can be considered a risk indicator, but when used in an equally risk contribution portfolio,
no evidence of the improvement of the risk-return performances is found.
Iris type:
2.1 Contributo in volume (Capitolo o Saggio)
Keywords:
Portfolio management, Online searches, Google Trends
List of contributors:
Maggi, MARIO ALESSANDRO; Uberti, Pierpaolo
Book title:
Mathematical and Statistical Methods for Actuarial Sciences and Finance