In this paper, we present a novel methodology for stock investment using episode mining and technical indicators. The time-series data of stock price and the derived moving average, a class of well-known technical indicators, are used for the construction of complex episode events and rules. Our objective is to devise a profitable episodebased investment model to mine associated events in the stock market. Using Taiwan Capitalization Weighted Stock Index (TAIEX), the empirical results show that our proposed model significantly outperforms the benchmark in terms of cumulative total returns. We also show that the level of the precision by our model is close to 60%, which is better than random guessing. Based upon the results obtained, we expect this novel episodebased methodology will advance the research in data mining for computational finance and provide an alternative to stock investment in practice.