Anger in predicting the index futures returns

Abstract

This paper aims to investigate how different emotions affect the subsequent index futures returns. We test the forecasting regressions which predict the S&P 500 index futures returns with lagged text‐based emotion (anger, joy, fear, optimism, and gloom) indices and find asymmetric forecasting power exists between pessimism and optimism emotion indices. We show that only the text‐based anger index could reliably perform at predicting index futures return in‐sample and outperform the prevailing unconditional mean out‐of‐sample. Notably, the predictive power of the text‐based anger index persists after controlling for other emotion indices, investor sentiment indices, and fundamental variables known to predict the futures market. And the asset allocation conditioning on text‐based anger index can generate substantial economic benefits. Furthermore, the anger index influences the index futures return through both the discount rate and cash flow channels.

Revisiting the puzzle of jumps in volatility forecasting: The new insights of high‐frequency jump intensity

“…Although recent studies have focused on predicting index futures returns (c.f. Jondeau et al, 2020;Cao et al, 2020Cao et al, , 2023, this study emphasizes on examining the information extracted from the S&P 500 index futures. By focusing on the high-frequency (5-and 10-min) prices of the S&P 500 index and S&P 500 index futures, we demonstrate significant improvements in volatility forecasting accuracy of S&P 500 index futures by combining jump intensity with a mark structure under the HAR framework and utilizing the rich information of event characteristics related to jump intensity.…”