Francesco Audrino

We 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.

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.

Building 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.

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.

We analyze the impact of sentiment and attention variables on stock market volatility by using a novel and extensive dataset that combines social media, news articles, information consumption, and search engine data. Applying a state-of-the-art sentiment classification technique, we investigate the question of whether sentiment and attention measures contain additional predictive power for realized volatility when controlling for a wide range of economic and financial predictors. Using a penalized regression framework, we identify investors' attention, as measured by the number of Google searches on financial keywords (e.g. "financial market" and "stock market"), and the daily volume of company-specific short messages posted on StockTwits to be the most relevant variables. In addition, our study shows that attention and sentiment variables are able to significantly improve volatility forecasts, although the improvements are of relatively small magnitude from an economic point of view.

We introduce a wild multiplicative bootstrap for M and GMM estimators in nonlinear models when autocorrelation structures of moment functions are unknown. The implementation of the bootstrap algorithm does not require any parametric assumptions on the data generating process. After proving its validity, we also investigate the accuracy of our procedure through Monte Carlo simulations. The wild bootstrap algorithm always outperforms inference based on standard first-order asymptotic theory. Moreover, in most cases the accuracy of our procedure is also better and more stable than that of block bootstrap methods. Finally, we apply the wild bootstrap approach to study the forecast ability of variance risk premia to predict future stock returns. We consider US equity from 1990 to 2010. For the period under investigation, our procedure provides significance in favor of predictability. By contrast, the block bootstrap implies ambiguous conclusions that heavily depend on the selection of the block size.

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. The model differs from Audrino and Knaus (2016) where the authors apply LASSO on the AR(p) model, which does not necessarily lead to a HAR model. Adaptive LASSO estimation and the subsequent hypothesis testing results fail to show strong evidence that such a fixed lag structure can be recovered by a flexible model. We also apply the group LASSO and related tests to check the validity of the classic HAR, which is rejected in most cases. The results justify our intention to use a flexible lag structure while still keeping the HAR frame. In terms of the out-of-sample forecasting, the proposed flexible specification works comparably to the benchmark HAR(1, 5, 22). Moreover, the time-varying model combinations show that when the market environment is not stable, the fixed lag structure (1, 5, 22) is not particularly accurate and effective.

The fast-growing literature on news analytics provides evidence that financial markets are partially driven by sentiments. In contrast with previous studies that have almost exclusively focused on the direct effects of the news related to single companies or sectors, we investigate the time-varying dynamics of news' cross-industry influences for a set of US and European stocks over a period of 10 years. The graphical Granger causality of the news sentiments-excess return networks is estimated by applying the adaptive lasso. We find significant spillover effects and show the importance of sentiments related to certain sectors for the whole cross-section of stocks.

We address the fiercely debated question of whether the strongest European football clubs get special, preferential treatment from match officials in their decisions on the teams' players over the course of the teams' trophy winning streaks. To give an empirical answer to this question, we apply a rigorous econometric analysis for causal effect estimation to a self-constructed data set. We consider the two clubs in the Italian Serie A that experienced a prolonged winning streak during the period 2006 to 2016, namely Internazionale Milan (Inter) and Juventus Turin, as well as one team from the German Bundesliga (Borussia Dortmund) and one from the English Premier League (Manchester United) that also experienced a winning streak during the same period. This allows us to perform an analysis with enough statistical power to be able to estimate properly the effect of interest. The general opinion among fans, sports journalists, and insiders that the strongest clubs are favored by match officials' decisions is supported only by the results of the analysis we run for Juventus, whereas for the other clubs under investigation, we did not find any significant bias. During its winning streak, more yellow cards and total booking points (an aggregated measure of yellow and red cards) were given to Juventus opponents. These effects are not only statistically significant, but also have a sizeable impact.