Predicting Bear Stock Markets in Taiwan with Dynamic Probit Models

Different from the most commonly used static model for bear market forecasting in Taiwan, this paper employed dynamic probit model with multivariate predictive variables in order to enhance the predictability of bear markets of the TAIEX. Empirical research in Nyberg (2010) and Kauppi and Saikkonen (2008) have already proved that the predictive power of dynamic probit models is higher than static probit model, regardless of in-sample or out-of-sample results. After our examinations targeting Taiwan stock market, we reached the same conclusions as they did, though the extent of superiority of dynamic models depends on whether a univariate or multivariate model is adopted. Moreover, besides those domestic macroeconomic variables, which have been fully discussed in market forecasting thesis, this paper considered more diversified variables such as stock market activity related indicators and international macroeconomic & other commodity price indicators. We found that US term spread and the number of M&A are good predictors in 1 month to 12 months forecasting scope, while TAIEX historical return, the change of TAIEX PER, the change of TAIEX dividend yield, the change of Dow Jones index, yield spread between investment grade-high yield bonds, and the change of MSCI EM index proved to be significant in short term bear market forecasting. As for half-to-one year forecasting period, ratings from equity research analysts would be a better indicator. Our design of dynamic probit models embedded with multivariate predictive indicators successfully improve the explanatory power for bear market forecasting if comparing with traditional static univariate predicting models. Finally, we developed a market timing trading strategy base on our optimal predicting models and tested it with historical data, revealing an average monthly return that beat the passive buy-and-hold strategy. The results could be more pronounced once a bear market probability weighted asset allocation rule is followed. However, we should be noted that dynamic models failed to outperform static models in providing higher return under this trading test.