My main research interests are in the field of Financial Economics. I conduct empirical work in that field, with an emphasis on market efficiency and investors' behaviours. My research agenda covers the applied sub-fields of: Climate Finance, Behavioural Finance, Asset Pricing and Portfolio Optimization. I also have an interest in Financial Econometrics. 

In the past I have also conducted research in Economics of Innovation and recently on the measurement of COVID-19 from a survey methods point of view.

Investors' Climate Sentiment and Financial Markets

Abstract. We propose a measure of investors' climate sentiment by performing sentiment analysis on StockTwits posts on climate change and global warming. We find that investors' climate sentiment generates a mispricing in a portfolio that goes long on emission stocks and go shorts on clean stocks (EMC). Specifically, when investors share a positive attitudes towards climate change, they tend to overvalue the negative externalities produced by emission stocks. Moreover, we show that carbon prices are a successful incentive to reduce CO2 emissions. Indeed, a rise in carbon prices can cause a reduction in the value of the EMC portfolio. Finally, the price of the EMC portfolio can be predicted with a VECM model which includes EMC, carbon prices, oil prices, market portfolio price, and investors' climate sentiment.


Available at SSRN:

Speculative Bubbles in Present-Value Models: A Bayesian Markov-Switching State Space Approach
with Joshua C.C. Chan

Abstract. We incorporate a speculative bubble subject to a surviving and a collapsing regime into the present-value model by Binsbergen et al. (2010), who pioneer the latent variables approach to estimate expected returns and expected dividend growth rates. To estimate this new high-dimensional model, we develop an efficient Markov chain Monte Carlo sampler to simulate from the joint posterior distribution. We apply our present-value model to artificial as well as real-world datasets. Our setup is able to correctly identify 92.27% of all the bubble collapsing dates in the artificial datasets. And it never signals a bubble when there is none in the data generating process. We then show the existence of significant Markov-switching structures in real-world stock price bubbles. The results indicate that dividend growth rates are highly predictable. Further, we argue that present-value models should not ignore the bubble component of stock prices. Indeed, we find that bubble variation accounts for most of the variation in the price-dividend ratio in the US, UK, Malaysia and Japan, and more than 35% of the price-dividend variation in Brazil. Moreover, bubble variation explains also a large share of unexpected return variation.

Exploring style herding by mutual funds
with Remco C.J. Zwinkels

Abstract. This paper analyses style herding in the value-growth and size dimensions of U.S. domestic equity mutual funds. We document that mutual fund herding in styles is significant and persistent. Furthermore, the results show that mutual fund herding tends to increase after periods of high cumulative returns and market volatility. A higher sentiment is followed by an increase in mutual fund herding towards small stocks. Instead, mutual fund herding in value stocks significantly decreases after an improvement in economic conditions. Mutual fund herding in styles causes overpricing in the market portfolio, and SMB and HML factors; this effect is stronger when the average fund flows are higher. We also observe that mutual fund herding in styles in some cases affects the autocorrelation structure of factor returns. Finally,

we find that mutual fund herding in styles impacts the average fund flows while it has no effect on the performance of the industry.


Available at SSRN:

Research Quotient, Optimal R&D and Stock Returns

Abstract. We document that the interaction of the firm’s ability to innovate and R&D expenditure can predict future operating performance; moreover the magnitude of these effects are significantly and substantially higher for firms with an R&D spending above the optimum. We also show that a long-short portfolio strategy which exploits information on the firm’s innovative ability and R&D level is profitable only for overspender firms (RD > RD*), in particular it earns excess returns of 14% annually. However, a risk-averse investor may still prefer to apply the strategy on the entire sample of firms in order to reduce risk. The results are robust to the confounding effects of other documented return predictors. Finally, overspending firms experience a higher level of volatility of stock returns which may explain why the market tends to misvalue innovation of this group of firms.


Available at SSRN:

Relative performance of Mean-Variance, Kelly and Universal Portfolios in the Equity Market 
with Giulio Bottazzi

Abstract. We employ monthly and daily returns of US stocks to evaluate the out-of-sample performance of investment rules stemming from the mean-variance, Kelly and universal portfolio literature. We find that none of the strategies considered is significantly better or worse than all the others. Moreover, we show that the theoretical goal of the different strategies, be it either the maximization of the risk-adjusted portfolio return or the final wealth, is not related to their out-of-sample performance relative to the different measures adopted. Conversely, agents should take into account the properties (return, risk and correlation) of the set of stocks selected for investment when they are choosing the portfolio model to follow. Specifically, on the one hand if stocks are highly heterogeneous in terms of return and they have a low risk profile, the Kelly investor will get richer. On the other hand, if stocks have similar returns the minimum-variance and the universal portfolio will increase performance. However, relative performance of the latter remains poor. Finally, although the performance of the mean-variance rule is not significantly influenced by portfolio characteristics, it performs no worse than the other strategies when stocks have heterogeneous returns.

Exploring the link between innovation and growth in Chilean firms
with Pietro Santoleri
Small Business Economics (2017), 49(2)

Abstract. We investigate the relation between the introduction of innovation and subsequent firm growth employing a dataset representative of the Chilean productive structure. By means of quantile treatment effects (QTE), we estimate the effect of the introduction of innovation by comparing firms with a similar propensity to innovate for different quantiles of the firm growth distribution. Our results indicate that process innovation positively affects sales growth for those firms located at the 75th and 90th percentiles. Contrarily, product innovation appears not to be a driver of firm performance. We also find that process innovation benefits mature firms at higher quantiles while it positively affects young firms located at low-medium quantiles.

Commentary to Klingwort, J., and Schnell, R. (2020). Critical Limitations of Digital Epidemiology: Why COVID-19 Apps Are Useless.
with Angelo Moretti
Survey Research Methods (2020), 14(2)
The Need for Complex Survey Designs to Contrast COVID-19
with Angelo Moretti

Abstract. In this article, we aim to identify the problem of measuring COVID-19 from a survey methods point of view. In particular, the lack of a clear data collection plan for testing is discussed and we compare non-probabilistic to probabilistic sampling strategies. We highlight how complex survey designs based on probabilistic sampling experiments can provide statistical sound information i.e. making statistical inference possible. Then, we discuss which types of analysis can be helpful in order to provide information to policy-makers. The goal of this article is to stress to the survey research methodologists community the need for a clear data collection plan where statistical inference is possible. 

Available at SSRN:


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