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. Under review.
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.
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.
Social Interaction, Stochastic Volatility, and Momentum [Link]
with Xue-Zhong He, Kai Li, and Lei Shi. Under review.
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.
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.
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.