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The authors compare sentiment level with sentiment shock from different angles to determine which measure better captures the relationship between sentiment and stock returns.

This paper examines the relationship between investor sentiment and contemporaneous stock returns. It also proposes a model of systems science to explain the empirical findings.

The authors find that sentiment shock has a higher explanatory power on stock returns than sentiment itself, and sentiment shock beta exhibits a much higher statistical significance than sentiment beta. Compared with sentiment level, sentiment shock has a more robust linkage to the market factors and the sentiment shock is more responsive to stock returns.

This is the first study to compare sentiment level and sentiment shock. It concludes that sentiment shock is a better indicator of the relationship between investor sentiment and contemporary stock returns.

The study examines the relationship between Economic Policy Uncertainty (EPU) and stock liquidity, and the mediating role of investor sentiment.

This study draws on a sample of 4,620 firm-year observations covering nonfinancial firms in the United States from 2007 to 2020. We employ multiple regression analysis with panel data and path analysis with Structural Equation Modeling (SEM) to examine the impact of EPU on stock liquidity in detail.

EPU significantly enhances stock liquidity. However, at elevated levels of EPU, this relationship reverses. The path analysis results indicate that EPU positively affects stock liquidity via the investor sentiment channel. This sentiment partially mediates the relationship between EPU and both trading volume and turnover rate, and fully mediates the relationship between EPU and both turnover price impact and illiquidity.

Our findings underscore the importance of liquidity for investors, who may require higher returns for holding more illiquid stocks. Second, they can help the government understand the implications of changes in EPU, highlighting the need for clear communication and the implementation of appropriate capital market policies.

While considerable research focuses on the relationship between EPU and stock market liquidity, the analysis of the channels through which EPU influences stock market liquidity remains largely unexplored. Our study highlights the importance of investor sentiment in explaining this relationship.

This work aims at designing an indicator for detecting and forecasting price volatility and speculative bubbles in three markets dealing with agricultural and soft commodities, i.e. Intercontinental Exchange Futures market Europe, (IFEU), Intercontinental Exchange Futures market United States (IFUS) and Chicago Board of Trade (CBOT). This indicator, designed as a demand/supply odds ratio, intends to overcome the subjectivity limits embedded in sentiment indexes as the Bull and Bears ratio by the Bank of America Merrill Lynch.

Data evidence allows for the parameter estimation of a Jacobi diffusion process that models the demand share and leads the forecast of speculative bubbles and realised volatility. Validation of outcomes is obtained through the dynamic regression with autoregressive integrated moving average (ARIMA) error. Results are discussed in comparison with those from the traditional generalized autoregressive conditional heteroskedasticity (GARCH) models. The database is retrieved from Thomson Reuters DataStream (nearby futures daily frequency).

The empirical analysis shows that the indicator succeeds in capturing the trend of the observed volatility in the future at medium and long-time horizons. A comparison of simulations results with those obtained with the traditional GARCH models, usually adopted in forecasting the volatility trend, confirms that the indicator is able to replicate the trend also providing turning points, i.e. additional information completely neglected by the GARCH analysis.

The authors' commodity demand as discrete-time process is capable of replicating the observed trend in a continuous-time framework, as well as turning points. This process is suited for estimating behavioural parameters of the agents, i.e. long-term mean, speed of mean reversion and herding behaviour. These parameters are used in the forecast of speculative bubbles and realised volatility.