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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.

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.