Abstract: Investment strategies based on technical analysis have attracted much attention from researchers and investors in A-share market. With the evolution of financial markets, the characteristics of asset price become more and more complicated, and some researchers have pointed out that the performances of classical linear models suffer noticeable degradation in terms of predicting asset price. Considering that deep learning techniques are efficient at capturing intricate quantitative models in nonlinear system, deep-learning-based methods have been introduced into the fields of economic and financial analysis recently. In this paper, deep neutral network (DNN) is applied to analyze the technical indicators and predict the falling and rising tendency of sector indexes in A-share market. The results of empirical analysis indicate that DNN has certain ability in technical analysis. The DNN-based technical analysis method contains four major steps. Firstly, a 36-dimension vector of technical indicators is computed to represent the technical states of sector indexes. Secondly, in order to reduce the dimension of input data and improve the performance of the DNN system, the data of technical indicators are compressed by using two-directional two-dimensional principle component analysis ((2D)²PCA). Thirdly, the DNN system is trained to obtain optimized network coefficients by using historical data. Finally, the test technical features are inputted into DNN to predict the tendency of the sector indexes. The proposed method is validated by using trading data of 28 sector indexes in A-share market between 2000/01/04 and 2017/07/20. Results of empirical test show that the DNN-based method improves the accuracy for predicting the tendency of sector indexes in comparison with methods based on support vector machine (SVM). Furthermore, the prediction accuracy reaches 55%~65% when the DNN-based method is used to distinguish rise and falling tendency, which again implies the potential application value of DNN in technical analysis in A-share markets.

Key words: deep learning, neutral network, technical analysis, sector index