Publications in Peer Reviewed Journals
Caterina Santi, 2023. Investor Climate Sentiment and Financial Markets. International Review of Financial Analysis, Forthcoming. [Link]
Data on Investors Climate Sentiment available here.
He, Xuezhong, Kai Li, Caterina Santi, and Shi, Lei, 2022. Social Interaction, Stochastic Volatility, and Momentum. Journal of Economic Behavior and Organization, 203, 125-149. [Link]
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]
Exploring style herding by mutual funds [Link]
with Remco C.J. Zwinkels. R&R.
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.
Carbon Risk Premium and Worries about Climate Change [Link]
Abstract. This paper sheds light on the impact of public attitudes towards climate change on the pricing of emission (carbon-intensive) and clean (low-emission) stocks. We develop a regional indicator of worries about climate change using data from the European Social Survey Round 8. We classify European regions as little worried, worried and very worried. We confirm previous evidence that emission stocks tend to have higher returns than clean stocks. However, when we focus on stocks quoted in exchange markets located in regions with low level of worries about climate change, we do not find evidence of a carbon risk premium. Conversely, the emission premium in worried regions is significant for medium-high quantiles of the return distribution.
The Need for Reliable and Timely Data to Contrast COVID-19: What Went Wrong? [Link]
with Angelo Moretti.
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.