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, Empirical Asset Pricing and Behavioural Finance. I also have an interest in Financial Econometrics.
I have also conducted research in Economics of Innovation and recently on the data issues on COVID-19 from a survey methods point of view.
Publications in Peer Reviewed Journals
Chan, Joshua C.C., and Santi, Caterina, 2021. Speculative Bubbles in Present-Value Models: A Bayesian Markov-Switching State Space Approach. Journal of Economic Dynamics and Control. Volume 127, 104101. [Link]
Moretti, Angelo, and Santi, Caterina, 2020 Commentary to "Klingwort, J., and Schnell, R. (2020). Critical Limitations of Digital Epidemiology: Why COVID-19 Apps Are Useless." Survey Research Methods, 14(2), 95-101. [Link]
Investors' Climate Sentiment and Financial Markets [Link]
Data on Investors Climate Sentiment available here.
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 the Emission-minus-Clean (EMC) portfolio (Choi et al., 2020), the portfolio that invests in emission stocks and goes short on clean stocks. Specifically, when investors share a positive attitude 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. Finally, a portfolio strategy that uses information on investors’ climate sentiment and carbon prices generates a return of 9.77% annually.
The Need for Reliable and Timely Data to Contrast COVID-19: What Went Wrong? [Link]
with Angelo Moretti. Under review.
Abstract. In this article, we aim to stress that the fight of COVID-19 needs clear and timely data collection plans. Without data to support decisions, we can only hope for a fortunate guess. We need synergies between different research communities, policy-makers, Official Statistics, health institutions, and private companies which may provide non-conventional source of data. After having discussed policies and data collection initiatives in several European countries, we argue that on the one hand policy decisions are often not driven by the data. On the other hand, data collection either does not occur in a timely manner or it does not enable researchers to generalise the sample results to the entire population.
Exploring style herding by mutual funds [Link]
with Remco C.J. Zwinkels. Under review.
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.
Research Quotient, Optimal R&D and Stock Returns [Link]
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.
Work in Progress
Social Interaction, Stochastic Volatility, and Momentum
with Xue-Zhong He, Kai Li, and Lei Shi
Abstract. Volatility clustering and momentum are two prominent features of equity returns. When the informativeness of observed noisy signals on asset fundamentals is either high or low, the peer-group effect of social interaction among investors can lead to two Nash equilibria that characterize two volatility regimes in asset returns and bi-modal distribution in population dynamics. Consequently, the stochastic switching between the two regimes and the persistent dominance of the investors who believe high (or low) informativeness generates volatility clustering, short-run momentum, and long-run reversal in equity returns. The effects become more significant in strong social interaction and high dispersion in the informativeness.
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.