Pedro Manuel Nogueira Reis

This paper proposes an Investor Sentiment Index for the European market and tests its predictability power over returns and volatility. The constructed Investor Sentiment Index for Europe draws upon three well-established and two recent individual sentiment proxies through a novel dynamic factor modeling addressed to behavioral finance. The index is obtained through an extended period of analysis and validated with other sentiment index measures. The work relies on individual sentiment proxies based on a dynamic factor model and tests it using a TGARCH model for volatility and returns. It carries out an in-sample and out-of-sample analysis to examine this sentiment index’s forecasting power over returns sustained on a recursive rolling window prediction against Fama and French’s three-factor model. The findings demonstrate that the proposed index closely predicts STOXX600 variance and returns and confirms a strong spillover effect between European and US stock markets. This study also concludes that the proposed European Sentiment Index is a valid alternative method for investors to monitor and predict market behaviors. The developed sentiment measure is a vital market prediction movement tool for financial information providers, investors, bankers, and financial analysts. The research combines the sentiment index with a TGARCH approach over the extended period of analysis and validates the method with other sentiment index measures. An in-sample and out-of-sample study confirms the predictive power of this work’s sentiment over returns compared to Fama and French’s three-factor model.

Acknowledgment
This work is funded by National Funds through the FCT – Foundation for Science and Technology, I.P., within the scope of the project Refª UIDB/05583/2020. Furthermore, we would like to thank the Research Centre in Digital Services (CISeD) and the Polytechnic of Viseu for their support.

Although some studies recently address the association between COVID-19 sentiment and returns, volatility, or stock trading volume, no one conducts an analysis to measure the impact of investor rationality or irrationality on the influence on countries and sectors’ returns.
This work creates a text media sentiment and combines its influence with the outbreak cases on the stock market sector returns of the US, Europe, and their main countries most affected by the pandemic.
This allows us to perceive the ranking impact of rationality or irrationality on country and sector stock returns. This work applies a random-effects robust panel estimation, with an M-estimator. This paper concludes that US returns are more sensitive to sentiment, and thus more prone to irrational factors than confirmed cases compared to Europe and that country factors influence the returns differently. In Italy and Spain as the most punished countries in Europe apart from the UK, present sector indexes return more reactive to verified cases, or rationality, namely, tourism, real estate, and the automobile (this last one in Italy).
The importance of this work resides in providing a new in-depth analysis of irrational behavioral metrics among countries, which allows for comparison. Moreover, it allows observing which sectors’ and which countries’ asset returns are most sensitive to rational or irrational expressions of events, allowing for arbitraging, financial planning for investors, decision-makers, and academia on an in and out of pandemic context.

Acknowledgment
This work is funded by National Funds through the FCT – Foundation for Science and Technology, I.P., within the scope of the project Refª UIDB/05583/2020. Furthermore, we would like to thank the Research Centre in Digital Services (CISeD) and the Polytechnic Institute of Viseu for their support.

This study examines how consumer, investor, and manager sentiment explain US stock excess returns over 23 years. Its novelty resides in integrating the sentiments of three different types of economic and financial agents. It also performs a segmented temporal analysis using rolling window techniques, to assess sentiment’s impact across different time horizons. The empirical analysis utilizes the Paris-Winsten and Newey-West estimators, along with the ARMAX model to address autocorrelation and heteroscedasticity in linear regression, providing robust standard errors and reliable statistical inferences. The autoregressive moving average models estimate excess return based on the past values, shocks, and external variables. Combining the Fama-French five-factor model with the sentiment factor enriches the analysis. The study’s findings indicate that higher consumer optimism negatively impacts excess returns, as investors may anticipate a future decline in the stock market due to an existing overheated economy. Investor sentiment exhibits mixed behavior, where higher uncertainty may increase stock returns due to previous oversold markets creating opportunities for investors or due to the closing of short positions, which will also increase stock demand. It is also related to decreased stock returns depending on the proxy used. As for managers’ sentiment, this work did not demonstrate a relevant relationship between this sentiment and stock returns. The study also reveals that the importance of sentiment determinants of those three agents changes over time. The findings support behavioral models of asset pricing, which incorporate both market fundamentals and the psychological characteristics (sentiment) of different market participants.

Acknowledgments
This work is funded by National Funds through the FCT – Foundation for Science and Technology, I.P., within the scope of the project Ref. UIDB/05583/2020. Furthermore, we would like to thank the Research Centre in Digital Services (CISeD) and the Instituto Politécnico de Viseu for their support.