Sentiment indices and their forecasting ability

The success of any timing strategy depends on the accuracy of market forecasts. In this paper, we test five indices to forecast the 1-month-ahead performance of the S&P 500 Index. These indices reflect investor sentiment, current business conditions, economic policy uncertainty, and market dislocation information. Each model is used in a logistic regression analysis to predict the 1-month-ahead market direction, and the forecasts are used to adjust the portfolio's beta. Beta optimization refers to a strategy designed to create a portfolio beta of 1.0 when the market is expected to go up, and a beta of -1.0 when a bear market is expected. Successful application of this strategy generates returns that are consistent with a call option or an option straddle position; that is, positive returns are generated in both up and down markets. Analysis reveals that the models' forecasts have discriminatory power in identifying substantial market movements, particularly during the bursting of the tech bubble and the financial crisis. Four of the five forecast models tested outperform the benchmark index.