Effective options trading strategies based on volatility forecasting recruiting investor sentiment

This study investigates the algorithm of effective option trading strategy based on the superior volatility forecasts using actual option price data in Taiwan stock market. Forecast evaluation supports the significant incremental explanatory power of investor sentiments on the fitting and forecasting of future volatility to its adversarial multiple-factor model, especially the market turnover and volatility index which are referred to investors’ mood gauge and proxy of overreaction. The simulated trading presents that the long (short) straddle traded 15-day before the option final settlement day based on the 60 days in-sample-period volatility forecasting recruiting market turnover achieve the best average monthly return of 28% (3.6%). This study bridges the gap between option trading, market volatility, and the signal of investors’ overreaction through the simulation of option trading strategy. The trading algorithm based on the volatility forecasting recruiting investor sentiments could further be applied in the electronic trading and the other artificial intelligence decision support