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In this paper, we present a new method to forecast the daily Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy time series and fuzzy variation groups, where the main input factor is the previous day's TAIEX, and the secondary factor is either the Dow Jones, the NASDAQ, the M 1b, or their combination. First, the proposed method fuzzifies the historical training data of the TAIEX into fuzzy sets to form fuzzy logical relationships. Second, it groups the fuzzy logical relationships into fuzzy logical relationship groups (FLRGs) based on the fuzzy variations of the secondary factor. Third, it evaluates the leverage of the fuzzy variations between the main factor and the secondary factor to construct fuzzy variation groups. Fourth, it gets the statistics of the fuzzy variations appearing in each fuzzy variation group. Fifth, it calculates the weights of the statistics of the fuzzy variations appearing in each fuzzy variation group, respectively. Finally, based on the weights of the statistics of the fuzzy variations appearing in the fuzzy variation groups and the FLRGs, it performs the forecasting of the daily TAIEX. Because the proposed method uses both fuzzy variation groups and FLRGs to analyze in detail the historical training data, it gets higher forecasting accuracy rates to forecast the TAIEX than the existing methods.