||Due to the improvement of computing power of hardware and the software of Deep Learning model, a lot of Institutional investors have been using Deep Learning in developing investment strategies. Besides, scholars in Finance field also strive to predict complex stock trend by using artificial intelligence and the method of algorithm to generalize the trend of stock, further finding an adaptive stock buying logic to help the investors to gain and stop loss in this highly dynamic and uncertain stock market. Basically, we conduct stock pattern recognition which is broadly used in the practice fields. I also input trading data with technical indicator and stock pattern characteristic into the Deep Learning model in order to get the result of predicting trend. I further empirically test the Deep Learning and help investors gain excess return in the stock market.|
First, we classify Chinese stock and Taiwanese stock data into 16 types by stock pattern recognition and verify the returns and gain probability. Second, I combine technical indicators and stock characteristics. Third, after choosing good characteristic and standardizing the feature data, we randomly sort the training data. Forth, after using Deep Learning model to train and predict, we obtain the return and gain probability. Fifth, I investigate practice results and use student’s t test to check significance of the results.
From the empirical results, Deep Learning model significantly improve the original trading strategy performance, and training data using one market is better than using two markets.